From f2cd16f340aa11afadb8fa90c29f85ca1b75a600 Mon Sep 17 00:00:00 2001
From: aktersnurra <gustaf.rydholm@gmail.com>
Date: Mon, 16 Nov 2020 20:26:32 +0100
Subject: Added a whitening transform.

---
 src/text_recognizer/datasets/transforms.py | 8 ++++++++
 1 file changed, 8 insertions(+)

(limited to 'src/text_recognizer/datasets')

diff --git a/src/text_recognizer/datasets/transforms.py b/src/text_recognizer/datasets/transforms.py
index 1105f23..d1ca127 100644
--- a/src/text_recognizer/datasets/transforms.py
+++ b/src/text_recognizer/datasets/transforms.py
@@ -64,3 +64,11 @@ class AddTokens:
             target = torch.cat([sos, target], dim=0)
 
         return target
+
+
+class Whitening:
+    """Whitening of Tensor, i.e. set mean to zero and std to one."""
+
+    def __call__(self, x: Tensor) -> Tensor:
+        """Apply the whitening."""
+        return (x - x.mean()) / x.std()
-- 
cgit v1.2.3-70-g09d2


From 527bb98b191d82b308de1585047e06056258d08d Mon Sep 17 00:00:00 2001
From: aktersnurra <gustaf.rydholm@gmail.com>
Date: Wed, 18 Nov 2020 20:56:19 +0100
Subject: Some minor changes.

---
 poetry.lock                                | 1220 ++++++++++++++++------------
 pyproject.toml                             |    5 +-
 src/notebooks/04a-look-at-iam-lines.ipynb  | 1159 +++-----------------------
 src/notebooks/Untitled.ipynb               |  509 ++----------
 src/text_recognizer/datasets/transforms.py |   15 +-
 src/text_recognizer/line_predictor.py      |    4 +-
 src/text_recognizer/models/__init__.py     |    5 -
 src/text_recognizer/models/metrics.py      |  107 ---
 src/text_recognizer/networks/__init__.py   |    5 +
 src/text_recognizer/networks/metrics.py    |  107 +++
 src/training/run_experiment.py             |    8 +-
 src/training/trainer/train.py              |    4 +
 12 files changed, 1019 insertions(+), 2129 deletions(-)
 delete mode 100644 src/text_recognizer/models/metrics.py
 create mode 100644 src/text_recognizer/networks/metrics.py

(limited to 'src/text_recognizer/datasets')

diff --git a/poetry.lock b/poetry.lock
index 6d07542..c0c061c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -40,6 +40,14 @@ dev = ["coverage (>=5.0.2)", "hypothesis", "pytest", "sphinx", "wheel", "pre-com
 docs = ["sphinx"]
 tests = ["coverage (>=5.0.2)", "hypothesis", "pytest"]
 
+[[package]]
+category = "dev"
+description = "Async generators and context managers for Python 3.5+"
+name = "async-generator"
+optional = false
+python-versions = ">=3.5"
+version = "1.10"
+
 [[package]]
 category = "main"
 description = "Atomic file writes."
@@ -55,13 +63,13 @@ description = "Classes Without Boilerplate"
 name = "attrs"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "19.3.0"
+version = "20.3.0"
 
 [package.extras]
-azure-pipelines = ["coverage", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "pytest-azurepipelines"]
-dev = ["coverage", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "sphinx", "pre-commit"]
-docs = ["sphinx", "zope.interface"]
-tests = ["coverage", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface"]
+dev = ["coverage (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "furo", "sphinx", "pre-commit"]
+docs = ["furo", "sphinx", "zope.interface"]
+tests = ["coverage (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface"]
+tests_no_zope = ["coverage (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six"]
 
 [[package]]
 category = "main"
@@ -69,7 +77,7 @@ description = "Internationalization utilities"
 name = "babel"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "2.8.0"
+version = "2.9.0"
 
 [package.dependencies]
 pytz = ">=2015.7"
@@ -123,7 +131,7 @@ description = "An easy safelist-based HTML-sanitizing tool."
 name = "bleach"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "3.1.5"
+version = "3.2.1"
 
 [package.dependencies]
 packaging = "*"
@@ -155,7 +163,7 @@ description = "Python package for providing Mozilla's CA Bundle."
 name = "certifi"
 optional = false
 python-versions = "*"
-version = "2020.6.20"
+version = "2020.11.8"
 
 [[package]]
 category = "dev"
@@ -163,7 +171,7 @@ description = "Foreign Function Interface for Python calling C code."
 name = "cffi"
 optional = false
 python-versions = "*"
-version = "1.14.2"
+version = "1.14.3"
 
 [package.dependencies]
 pycparser = "*"
@@ -191,7 +199,7 @@ marker = "sys_platform == \"win32\" or platform_system == \"Windows\" or python_
 name = "colorama"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "0.4.3"
+version = "0.4.4"
 
 [[package]]
 category = "main"
@@ -199,11 +207,11 @@ description = "Updated configparser from Python 3.8 for Python 2.6+."
 name = "configparser"
 optional = false
 python-versions = ">=3.6"
-version = "5.0.0"
+version = "5.0.1"
 
 [package.extras]
 docs = ["sphinx", "jaraco.packaging (>=3.2)", "rst.linker (>=1.9)"]
-testing = ["pytest (>=3.5,<3.7.3 || >3.7.3)", "pytest-checkdocs (>=1.2.3)", "pytest-flake8", "pytest-black-multipy", "pytest-cov"]
+testing = ["pytest (>=3.5,<3.7.3 || >3.7.3)", "pytest-checkdocs (>=1.2.3)", "pytest-flake8", "pytest-cov", "jaraco.test (>=3.2.0)", "pytest-black (>=0.3.7)", "pytest-mypy"]
 
 [[package]]
 category = "dev"
@@ -211,7 +219,7 @@ description = "Code coverage measurement for Python"
 name = "coverage"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4"
-version = "5.2.1"
+version = "5.3"
 
 [package.dependencies]
 [package.dependencies.toml]
@@ -238,7 +246,7 @@ description = "A utility for ensuring Google-style docstrings stay up to date wi
 name = "darglint"
 optional = false
 python-versions = ">=3.5,<4.0"
-version = "1.5.2"
+version = "1.5.5"
 
 [[package]]
 category = "main"
@@ -332,24 +340,13 @@ optional = false
 python-versions = ">=2.7"
 version = "0.3"
 
-[[package]]
-category = "main"
-description = "A library for efficient similarity search and clustering of dense vectors."
-name = "faiss-gpu"
-optional = false
-python-versions = "*"
-version = "1.6.3"
-
-[package.dependencies]
-numpy = "*"
-
 [[package]]
 category = "main"
 description = "the modular source code checker: pep8 pyflakes and co"
 name = "flake8"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
-version = "3.8.3"
+version = "3.8.4"
 
 [package.dependencies]
 mccabe = ">=0.6.0,<0.7.0"
@@ -366,7 +363,7 @@ description = "Flake8 Type Annotation Checks"
 name = "flake8-annotations"
 optional = false
 python-versions = ">=3.6.1,<4.0.0"
-version = "2.3.0"
+version = "2.4.1"
 
 [package.dependencies]
 flake8 = ">=3.7,<3.9"
@@ -473,7 +470,7 @@ description = "Python Git Library"
 name = "gitpython"
 optional = false
 python-versions = ">=3.4"
-version = "3.1.7"
+version = "3.1.11"
 
 [package.dependencies]
 gitdb = ">=4.0.1,<5"
@@ -573,7 +570,7 @@ marker = "python_version < \"3.8\""
 name = "importlib-metadata"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
-version = "1.7.0"
+version = "2.0.0"
 
 [package.dependencies]
 zipp = ">=0.5"
@@ -606,7 +603,7 @@ description = "IPython: Productive Interactive Computing"
 name = "ipython"
 optional = false
 python-versions = ">=3.7"
-version = "7.17.0"
+version = "7.19.0"
 
 [package.dependencies]
 appnope = "*"
@@ -614,7 +611,7 @@ backcall = "*"
 colorama = "*"
 decorator = "*"
 jedi = ">=0.10"
-pexpect = "*"
+pexpect = ">4.3"
 pickleshare = "*"
 prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
 pygments = "*"
@@ -696,7 +693,7 @@ description = "Lightweight pipelining: using Python functions as pipeline jobs."
 name = "joblib"
 optional = false
 python-versions = ">=3.6"
-version = "0.16.0"
+version = "0.17.0"
 
 [[package]]
 category = "dev"
@@ -742,7 +739,7 @@ description = "Jupyter protocol implementation and client libraries"
 name = "jupyter-client"
 optional = false
 python-versions = ">=3.5"
-version = "6.1.6"
+version = "6.1.7"
 
 [package.dependencies]
 jupyter-core = ">=4.6.0"
@@ -752,15 +749,15 @@ tornado = ">=4.1"
 traitlets = "*"
 
 [package.extras]
-test = ["async-generator", "ipykernel", "ipython", "mock", "pytest", "pytest-asyncio", "pytest-timeout"]
+test = ["ipykernel", "ipython", "mock", "pytest", "pytest-asyncio", "async-generator", "pytest-timeout"]
 
 [[package]]
 category = "dev"
 description = "Jupyter terminal console"
 name = "jupyter-console"
 optional = false
-python-versions = ">=3.5"
-version = "6.1.0"
+python-versions = ">=3.6"
+version = "6.2.0"
 
 [package.dependencies]
 ipykernel = "*"
@@ -784,13 +781,24 @@ version = "4.6.3"
 pywin32 = ">=1.0"
 traitlets = "*"
 
+[[package]]
+category = "dev"
+description = "Pygments theme using JupyterLab CSS variables"
+name = "jupyterlab-pygments"
+optional = false
+python-versions = "*"
+version = "0.1.2"
+
+[package.dependencies]
+pygments = ">=2.4.1,<3"
+
 [[package]]
 category = "main"
 description = "A fast implementation of the Cassowary constraint solver"
 name = "kiwisolver"
 optional = false
 python-versions = ">=3.6"
-version = "1.2.0"
+version = "1.3.1"
 
 [[package]]
 category = "main"
@@ -798,14 +806,14 @@ description = "Python logging made (stupidly) simple"
 name = "loguru"
 optional = false
 python-versions = ">=3.5"
-version = "0.5.1"
+version = "0.5.3"
 
 [package.dependencies]
 colorama = ">=0.3.4"
 win32-setctime = ">=1.0.0"
 
 [package.extras]
-dev = ["codecov (>=2.0.15)", "colorama (>=0.3.4)", "flake8 (>=3.7.7)", "isort (>=4.3.20)", "tox (>=3.9.0)", "tox-travis (>=0.12)", "pytest (>=4.6.2)", "pytest-cov (>=2.7.1)", "Sphinx (>=2.2.1)", "sphinx-autobuild (>=0.7.1)", "sphinx-rtd-theme (>=0.4.3)", "black (>=19.3b0)"]
+dev = ["codecov (>=2.0.15)", "colorama (>=0.3.4)", "flake8 (>=3.7.7)", "tox (>=3.9.0)", "tox-travis (>=0.12)", "pytest (>=4.6.2)", "pytest-cov (>=2.7.1)", "Sphinx (>=2.2.1)", "sphinx-autobuild (>=0.7.1)", "sphinx-rtd-theme (>=0.4.3)", "black (>=19.10b0)", "isort (>=5.1.1)"]
 
 [[package]]
 category = "main"
@@ -821,12 +829,12 @@ description = "A lightweight library for converting complex datatypes to and fro
 name = "marshmallow"
 optional = false
 python-versions = ">=3.5"
-version = "3.7.1"
+version = "3.9.1"
 
 [package.extras]
-dev = ["pytest", "pytz", "simplejson", "mypy (0.782)", "flake8 (3.8.3)", "flake8-bugbear (20.1.4)", "pre-commit (>=2.4,<3.0)", "tox"]
-docs = ["sphinx (3.1.2)", "sphinx-issues (1.2.0)", "alabaster (0.7.12)", "sphinx-version-warning (1.1.2)", "autodocsumm (0.1.13)"]
-lint = ["mypy (0.782)", "flake8 (3.8.3)", "flake8-bugbear (20.1.4)", "pre-commit (>=2.4,<3.0)"]
+dev = ["pytest", "pytz", "simplejson", "mypy (0.790)", "flake8 (3.8.4)", "flake8-bugbear (20.1.4)", "pre-commit (>=2.4,<3.0)", "tox"]
+docs = ["sphinx (3.3.0)", "sphinx-issues (1.2.0)", "alabaster (0.7.12)", "sphinx-version-warning (1.1.2)", "autodocsumm (0.2.1)"]
+lint = ["mypy (0.790)", "flake8 (3.8.4)", "flake8-bugbear (20.1.4)", "pre-commit (>=2.4,<3.0)"]
 tests = ["pytest", "pytz", "simplejson"]
 
 [[package]]
@@ -835,10 +843,9 @@ description = "Python plotting package"
 name = "matplotlib"
 optional = false
 python-versions = ">=3.6"
-version = "3.3.1"
+version = "3.3.3"
 
 [package.dependencies]
-certifi = ">=2020.06.20"
 cycler = ">=0.10"
 kiwisolver = ">=1.0.1"
 numpy = ">=1.15"
@@ -868,7 +875,7 @@ description = "More routines for operating on iterables, beyond itertools"
 name = "more-itertools"
 optional = false
 python-versions = ">=3.5"
-version = "8.4.0"
+version = "8.6.0"
 
 [[package]]
 category = "dev"
@@ -894,13 +901,33 @@ optional = false
 python-versions = "*"
 version = "0.4.3"
 
+[[package]]
+category = "dev"
+description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
+name = "nbclient"
+optional = false
+python-versions = ">=3.6"
+version = "0.5.1"
+
+[package.dependencies]
+async-generator = "*"
+jupyter-client = ">=6.1.5"
+nbformat = ">=5.0"
+nest-asyncio = "*"
+traitlets = ">=4.2"
+
+[package.extras]
+dev = ["codecov", "coverage", "ipython", "ipykernel", "ipywidgets", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "check-manifest", "flake8", "mypy", "tox", "bumpversion", "xmltodict", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "black"]
+sphinx = ["Sphinx (>=1.7)", "sphinx-book-theme", "mock", "moto", "myst-parser"]
+test = ["codecov", "coverage", "ipython", "ipykernel", "ipywidgets", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "check-manifest", "flake8", "mypy", "tox", "bumpversion", "xmltodict", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "black"]
+
 [[package]]
 category = "dev"
 description = "Converting Jupyter Notebooks"
 name = "nbconvert"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "5.6.1"
+python-versions = ">=3.6"
+version = "6.0.7"
 
 [package.dependencies]
 bleach = "*"
@@ -908,19 +935,21 @@ defusedxml = "*"
 entrypoints = ">=0.2.2"
 jinja2 = ">=2.4"
 jupyter-core = "*"
+jupyterlab-pygments = "*"
 mistune = ">=0.8.1,<2"
+nbclient = ">=0.5.0,<0.6.0"
 nbformat = ">=4.4"
 pandocfilters = ">=1.4.1"
-pygments = "*"
+pygments = ">=2.4.1"
 testpath = "*"
 traitlets = ">=4.2"
 
 [package.extras]
-all = ["pytest", "pytest-cov", "ipykernel", "jupyter-client (>=5.3.1)", "ipywidgets (>=7)", "pebble", "tornado (>=4.0)", "sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "sphinxcontrib-github-alt", "ipython", "mock"]
-docs = ["sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "sphinxcontrib-github-alt", "ipython", "jupyter-client (>=5.3.1)"]
-execute = ["jupyter-client (>=5.3.1)"]
+all = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (0.2.2)", "tornado (>=4.0)", "sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"]
+docs = ["sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"]
 serve = ["tornado (>=4.0)"]
-test = ["pytest", "pytest-cov", "ipykernel", "jupyter-client (>=5.3.1)", "ipywidgets (>=7)", "pebble", "mock"]
+test = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (0.2.2)"]
+webpdf = ["pyppeteer (0.2.2)"]
 
 [[package]]
 category = "dev"
@@ -928,7 +957,7 @@ description = "The Jupyter Notebook format"
 name = "nbformat"
 optional = false
 python-versions = ">=3.5"
-version = "5.0.7"
+version = "5.0.8"
 
 [package.dependencies]
 ipython-genutils = "*"
@@ -937,7 +966,16 @@ jupyter-core = "*"
 traitlets = ">=4.1"
 
 [package.extras]
-test = ["pytest", "pytest-cov", "testpath"]
+fast = ["fastjsonschema"]
+test = ["fastjsonschema", "testpath", "pytest", "pytest-cov"]
+
+[[package]]
+category = "dev"
+description = "Patch asyncio to allow nested event loops"
+name = "nest-asyncio"
+optional = false
+python-versions = ">=3.5"
+version = "1.4.3"
 
 [[package]]
 category = "dev"
@@ -945,14 +983,14 @@ description = "Python package for creating and manipulating graphs and networks"
 marker = "python_version == \"3.7\""
 name = "networkx"
 optional = false
-python-versions = ">=3.5"
-version = "2.4"
+python-versions = ">=3.6"
+version = "2.5"
 
 [package.dependencies]
 decorator = ">=4.3.0"
 
 [package.extras]
-all = ["numpy", "scipy", "pandas", "matplotlib", "pygraphviz", "pydot", "pyyaml", "gdal", "lxml", "pytest"]
+all = ["numpy", "scipy", "pandas", "matplotlib", "pygraphviz", "pydot", "pyyaml", "lxml", "pytest"]
 gdal = ["gdal"]
 lxml = ["lxml"]
 matplotlib = ["matplotlib"]
@@ -971,7 +1009,7 @@ marker = "python_version == \"3.7\""
 name = "ninja"
 optional = false
 python-versions = "*"
-version = "1.10.0.post1"
+version = "1.10.0.post2"
 
 [[package]]
 category = "main"
@@ -1001,7 +1039,7 @@ description = "A web-based notebook environment for interactive computing"
 name = "notebook"
 optional = false
 python-versions = ">=3.5"
-version = "6.1.3"
+version = "6.1.5"
 
 [package.dependencies]
 Send2Trash = "*"
@@ -1029,7 +1067,7 @@ description = "NumPy is the fundamental package for array computing with Python.
 name = "numpy"
 optional = false
 python-versions = ">=3.6"
-version = "1.19.1"
+version = "1.19.4"
 
 [[package]]
 category = "main"
@@ -1045,7 +1083,7 @@ description = "A flexible configuration library"
 name = "omegaconf"
 optional = false
 python-versions = ">=3.6"
-version = "2.0.2"
+version = "2.0.5"
 
 [package.dependencies]
 PyYAML = ">=5.1"
@@ -1056,11 +1094,8 @@ category = "main"
 description = "Wrapper package for OpenCV python bindings."
 name = "opencv-python"
 optional = false
-python-versions = ">=3.5"
-version = "4.4.0.42"
-
-[package.dependencies]
-numpy = ">=1.13.1"
+python-versions = ">=3.6"
+version = "4.4.0.46"
 
 [[package]]
 category = "main"
@@ -1079,12 +1114,13 @@ category = "dev"
 description = "Utilities for writing pandoc filters in python"
 name = "pandocfilters"
 optional = false
-python-versions = "*"
-version = "1.4.2"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "1.4.3"
 
 [[package]]
 category = "dev"
 description = "A Python Parser"
+marker = "python_version >= \"3.3\""
 name = "parso"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
@@ -1099,7 +1135,7 @@ description = "Utility library for gitignore style pattern matching of file path
 name = "pathspec"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "0.8.0"
+version = "0.8.1"
 
 [[package]]
 category = "main"
@@ -1114,8 +1150,8 @@ category = "dev"
 description = "Python Build Reasonableness"
 name = "pbr"
 optional = false
-python-versions = "*"
-version = "5.4.5"
+python-versions = ">=2.6"
+version = "5.5.1"
 
 [[package]]
 category = "dev"
@@ -1142,8 +1178,8 @@ category = "main"
 description = "Python Imaging Library (Fork)"
 name = "pillow"
 optional = false
-python-versions = ">=3.5"
-version = "7.2.0"
+python-versions = ">=3.6"
+version = "8.0.1"
 
 [[package]]
 category = "main"
@@ -1167,7 +1203,7 @@ description = "Python client for the Prometheus monitoring system."
 name = "prometheus-client"
 optional = false
 python-versions = "*"
-version = "0.8.0"
+version = "0.9.0"
 
 [package.extras]
 twisted = ["twisted"]
@@ -1192,7 +1228,7 @@ description = "Library for building powerful interactive command lines in Python
 name = "prompt-toolkit"
 optional = false
 python-versions = ">=3.6.1"
-version = "3.0.6"
+version = "3.0.8"
 
 [package.dependencies]
 wcwidth = "*"
@@ -1203,7 +1239,7 @@ description = "Cross-platform lib for process and system monitoring in Python."
 name = "psutil"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "5.7.2"
+version = "5.7.3"
 
 [package.extras]
 test = ["ipaddress", "mock", "unittest2", "enum34", "pywin32", "wmi"]
@@ -1211,7 +1247,7 @@ test = ["ipaddress", "mock", "unittest2", "enum34", "pywin32", "wmi"]
 [[package]]
 category = "dev"
 description = "Run a subprocess in a pseudo terminal"
-marker = "python_version >= \"3.3\" and sys_platform != \"win32\" or sys_platform != \"win32\" or os_name != \"nt\" or python_version >= \"3.3\" and sys_platform != \"win32\" and (python_version >= \"3.3\" and sys_platform != \"win32\" or sys_platform != \"win32\")"
+marker = "python_version >= \"3.3\" and sys_platform != \"win32\" or os_name != \"nt\""
 name = "ptyprocess"
 optional = false
 python-versions = "*"
@@ -1247,7 +1283,7 @@ description = "Python docstring style checker"
 name = "pydocstyle"
 optional = false
 python-versions = ">=3.5"
-version = "5.0.2"
+version = "5.1.1"
 
 [package.dependencies]
 snowballstemmer = "*"
@@ -1266,7 +1302,7 @@ description = "Pygments is a syntax highlighting package written in Python."
 name = "pygments"
 optional = false
 python-versions = ">=3.5"
-version = "2.6.1"
+version = "2.7.2"
 
 [[package]]
 category = "main"
@@ -1281,11 +1317,8 @@ category = "dev"
 description = "Persistent/Functional/Immutable data structures"
 name = "pyrsistent"
 optional = false
-python-versions = "*"
-version = "0.16.0"
-
-[package.dependencies]
-six = "*"
+python-versions = ">=3.5"
+version = "0.17.3"
 
 [[package]]
 category = "main"
@@ -1334,10 +1367,10 @@ description = "Thin-wrapper around the mock package for easier use with pytest"
 name = "pytest-mock"
 optional = false
 python-versions = ">=3.5"
-version = "3.2.0"
+version = "3.3.1"
 
 [package.dependencies]
-pytest = ">=2.7"
+pytest = ">=5.0"
 
 [package.extras]
 dev = ["pre-commit", "tox", "pytest-asyncio"]
@@ -1378,7 +1411,7 @@ description = "The easiest way to use deep metric learning in your application.
 name = "pytorch-metric-learning"
 optional = false
 python-versions = ">=3.0"
-version = "0.9.92"
+version = "0.9.94"
 
 [package.dependencies]
 numpy = "*"
@@ -1398,13 +1431,13 @@ description = "Python type inferencer"
 marker = "python_version == \"3.7\""
 name = "pytype"
 optional = false
-python-versions = "<3.9,>=3.5"
-version = "2020.8.10"
+python-versions = "<3.9,>=3.6"
+version = "2020.11.12"
 
 [package.dependencies]
 attrs = "*"
 importlab = ">=0.5.1"
-ninja = "*"
+ninja = ">=1.10.0.post2"
 pyyaml = ">=3.11"
 six = "*"
 typed_ast = "*"
@@ -1415,7 +1448,7 @@ description = "World timezone definitions, modern and historical"
 name = "pytz"
 optional = false
 python-versions = "*"
-version = "2020.1"
+version = "2020.4"
 
 [[package]]
 category = "dev"
@@ -1424,7 +1457,7 @@ marker = "sys_platform == \"win32\""
 name = "pywin32"
 optional = false
 python-versions = "*"
-version = "228"
+version = "300"
 
 [[package]]
 category = "dev"
@@ -1448,8 +1481,12 @@ category = "dev"
 description = "Python bindings for 0MQ"
 name = "pyzmq"
 optional = false
-python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*"
-version = "19.0.2"
+python-versions = ">=3.5"
+version = "20.0.0"
+
+[package.dependencies]
+cffi = "*"
+py = "*"
 
 [[package]]
 category = "dev"
@@ -1457,7 +1494,7 @@ description = "Jupyter Qt console"
 name = "qtconsole"
 optional = false
 python-versions = "*"
-version = "4.7.5"
+version = "4.7.7"
 
 [package.dependencies]
 ipykernel = ">=4.1"
@@ -1509,7 +1546,7 @@ description = "Alternative regular expression module, to replace re."
 name = "regex"
 optional = false
 python-versions = "*"
-version = "2020.7.14"
+version = "2020.11.13"
 
 [[package]]
 category = "main"
@@ -1517,13 +1554,13 @@ description = "Python HTTP for Humans."
 name = "requests"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "2.24.0"
+version = "2.25.0"
 
 [package.dependencies]
 certifi = ">=2017.4.17"
 chardet = ">=3.0.2,<4"
 idna = ">=2.5,<3"
-urllib3 = ">=1.21.1,<1.25.0 || >1.25.0,<1.25.1 || >1.25.1,<1.26"
+urllib3 = ">=1.21.1,<1.27"
 
 [package.extras]
 security = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)"]
@@ -1567,7 +1604,7 @@ description = "SciPy: Scientific Library for Python"
 name = "scipy"
 optional = false
 python-versions = ">=3.6"
-version = "1.5.2"
+version = "1.5.4"
 
 [package.dependencies]
 numpy = ">=1.14.5"
@@ -1586,7 +1623,7 @@ description = "Python client for Sentry (https://sentry.io)"
 name = "sentry-sdk"
 optional = false
 python-versions = "*"
-version = "0.16.5"
+version = "0.19.3"
 
 [package.dependencies]
 certifi = "*"
@@ -1597,6 +1634,7 @@ aiohttp = ["aiohttp (>=3.5)"]
 beam = ["apache-beam (>=2.12)"]
 bottle = ["bottle (>=0.12.13)"]
 celery = ["celery (>=3)"]
+chalice = ["chalice (>=1.16.0)"]
 django = ["django (>=1.8)"]
 falcon = ["falcon (>=1.4)"]
 flask = ["flask (>=0.11)", "blinker (>=1.1)"]
@@ -1645,7 +1683,7 @@ description = "Python documentation generator"
 name = "sphinx"
 optional = false
 python-versions = ">=3.5"
-version = "3.2.1"
+version = "3.3.1"
 
 [package.dependencies]
 Jinja2 = ">=2.3"
@@ -1668,7 +1706,7 @@ sphinxcontrib-serializinghtml = "*"
 
 [package.extras]
 docs = ["sphinxcontrib-websupport"]
-lint = ["flake8 (>=3.5.0)", "flake8-import-order", "mypy (>=0.780)", "docutils-stubs"]
+lint = ["flake8 (>=3.5.0)", "flake8-import-order", "mypy (>=0.790)", "docutils-stubs"]
 test = ["pytest", "pytest-cov", "html5lib", "typed-ast", "cython"]
 
 [[package]]
@@ -1677,13 +1715,13 @@ description = "Type hints (PEP 484) support for the Sphinx autodoc extension"
 name = "sphinx-autodoc-typehints"
 optional = false
 python-versions = ">=3.5.2"
-version = "1.11.0"
+version = "1.11.1"
 
 [package.dependencies]
 Sphinx = ">=3.0"
 
 [package.extras]
-test = ["pytest (>=3.1.0)", "typing-extensions (>=3.5)", "sphobjinv (>=2.0)", "dataclasses"]
+test = ["pytest (>=3.1.0)", "typing-extensions (>=3.5)", "sphobjinv (>=2.0)", "Sphinx (>=3.2.0)", "dataclasses"]
 type_comments = ["typed-ast (>=1.4.0)"]
 
 [[package]]
@@ -1774,7 +1812,7 @@ description = "Manage dynamic plugins for Python applications"
 name = "stevedore"
 optional = false
 python-versions = ">=3.6"
-version = "3.2.0"
+version = "3.2.2"
 
 [package.dependencies]
 pbr = ">=2.0.0,<2.1.0 || >2.1.0"
@@ -1793,11 +1831,11 @@ version = "3.5.4"
 
 [[package]]
 category = "dev"
-description = "Terminals served to xterm.js using Tornado websockets"
+description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
 name = "terminado"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "0.8.3"
+python-versions = ">=3.6"
+version = "0.9.1"
 
 [package.dependencies]
 ptyprocess = "*"
@@ -1828,8 +1866,8 @@ category = "main"
 description = "Python Library for Tom's Obvious, Minimal Language"
 name = "toml"
 optional = false
-python-versions = "*"
-version = "0.10.1"
+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+version = "0.10.2"
 
 [[package]]
 category = "main"
@@ -1837,11 +1875,13 @@ description = "Tensors and Dynamic neural networks in Python with strong GPU acc
 name = "torch"
 optional = false
 python-versions = ">=3.6.1"
-version = "1.6.0"
+version = "1.7.0"
 
 [package.dependencies]
+dataclasses = "*"
 future = "*"
 numpy = "*"
+typing-extensions = "*"
 
 [[package]]
 category = "main"
@@ -1849,7 +1889,7 @@ description = "Model summary in PyTorch, based off of the original torchsummary.
 name = "torch-summary"
 optional = false
 python-versions = ">=3.5"
-version = "1.4.2"
+version = "1.4.3"
 
 [[package]]
 category = "main"
@@ -1857,12 +1897,12 @@ description = "image and video datasets and models for torch deep learning"
 name = "torchvision"
 optional = false
 python-versions = "*"
-version = "0.7.0"
+version = "0.8.1"
 
 [package.dependencies]
 numpy = "*"
 pillow = ">=4.1.1"
-torch = "1.6.0"
+torch = "1.7.0"
 
 [package.extras]
 scipy = ["scipy"]
@@ -1873,7 +1913,7 @@ description = "Tornado is a Python web framework and asynchronous networking lib
 name = "tornado"
 optional = false
 python-versions = ">= 3.5"
-version = "6.0.4"
+version = "6.1"
 
 [[package]]
 category = "main"
@@ -1881,26 +1921,24 @@ description = "Fast, Extensible Progress Meter"
 name = "tqdm"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*"
-version = "4.48.2"
+version = "4.52.0"
 
 [package.extras]
-dev = ["py-make (>=0.1.0)", "twine", "argopt", "pydoc-markdown"]
+dev = ["py-make (>=0.1.0)", "twine", "argopt", "pydoc-markdown", "wheel"]
 
 [[package]]
 category = "dev"
-description = "Traitlets Python config system"
+description = "Traitlets Python configuration system"
 name = "traitlets"
 optional = false
-python-versions = "*"
-version = "4.3.3"
+python-versions = ">=3.7"
+version = "5.0.5"
 
 [package.dependencies]
-decorator = "*"
 ipython-genutils = "*"
-six = "*"
 
 [package.extras]
-test = ["pytest", "mock"]
+test = ["pytest"]
 
 [[package]]
 category = "main"
@@ -1916,7 +1954,7 @@ description = "Run-time type checker for Python"
 name = "typeguard"
 optional = false
 python-versions = ">=3.5.3"
-version = "2.9.1"
+version = "2.10.0"
 
 [package.extras]
 doc = ["sphinx-rtd-theme", "sphinx-autodoc-typehints (>=1.2.0)"]
@@ -1928,7 +1966,7 @@ description = "Backported and Experimental Type Hints for Python 3.5+"
 name = "typing-extensions"
 optional = false
 python-versions = "*"
-version = "3.7.4.2"
+version = "3.7.4.3"
 
 [[package]]
 category = "main"
@@ -1948,11 +1986,11 @@ description = "HTTP library with thread-safe connection pooling, file post, and
 name = "urllib3"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4"
-version = "1.25.10"
+version = "1.26.2"
 
 [package.extras]
 brotli = ["brotlipy (>=0.6.0)"]
-secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "pyOpenSSL (>=0.14)", "ipaddress"]
+secure = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "certifi", "ipaddress"]
 socks = ["PySocks (>=1.5.6,<1.5.7 || >1.5.7,<2.0)"]
 
 [[package]]
@@ -1961,7 +1999,7 @@ description = "A CLI and library for interacting with the Weights and Biases API
 name = "wandb"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "0.9.6"
+version = "0.9.7"
 
 [package.dependencies]
 Click = ">=7.0"
@@ -2033,7 +2071,7 @@ marker = "sys_platform == \"win32\""
 name = "win32-setctime"
 optional = false
 python-versions = ">=3.5"
-version = "1.0.1"
+version = "1.0.3"
 
 [package.extras]
 dev = ["pytest (>=4.6.2)", "black (>=19.3b0)"]
@@ -2061,14 +2099,14 @@ marker = "python_version < \"3.8\""
 name = "zipp"
 optional = false
 python-versions = ">=3.6"
-version = "3.1.0"
+version = "3.4.0"
 
 [package.extras]
 docs = ["sphinx", "jaraco.packaging (>=3.2)", "rst.linker (>=1.9)"]
-testing = ["jaraco.itertools", "func-timeout"]
+testing = ["pytest (>=3.5,<3.7.3 || >3.7.3)", "pytest-checkdocs (>=1.2.3)", "pytest-flake8", "pytest-cov", "jaraco.test (>=3.2.0)", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy"]
 
 [metadata]
-content-hash = "c8511d6e4ae5708277c07f553830c11d3dc5cd77ca7f99bb47cd94dbb21d4b13"
+content-hash = "9e2ebd8ad14a53756cf4d2b967e4dfb52d1c70caa0a6df6b5e42600237337631"
 lock-version = "1.0"
 python-versions = "^3.7"
 
@@ -2103,17 +2141,21 @@ argon2-cffi = [
     {file = "argon2_cffi-20.1.0-cp38-cp38-win32.whl", hash = "sha256:77e909cc756ef81d6abb60524d259d959bab384832f0c651ed7dcb6e5ccdbb78"},
     {file = "argon2_cffi-20.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:9dfd5197852530294ecb5795c97a823839258dfd5eb9420233c7cfedec2058f2"},
 ]
+async-generator = [
+    {file = "async_generator-1.10-py3-none-any.whl", hash = "sha256:01c7bf666359b4967d2cda0000cc2e4af16a0ae098cbffcb8472fb9e8ad6585b"},
+    {file = "async_generator-1.10.tar.gz", hash = "sha256:6ebb3d106c12920aaae42ccb6f787ef5eefdcdd166ea3d628fa8476abe712144"},
+]
 atomicwrites = [
     {file = "atomicwrites-1.4.0-py2.py3-none-any.whl", hash = "sha256:6d1784dea7c0c8d4a5172b6c620f40b6e4cbfdf96d783691f2e1302a7b88e197"},
     {file = "atomicwrites-1.4.0.tar.gz", hash = "sha256:ae70396ad1a434f9c7046fd2dd196fc04b12f9e91ffb859164193be8b6168a7a"},
 ]
 attrs = [
-    {file = "attrs-19.3.0-py2.py3-none-any.whl", hash = "sha256:08a96c641c3a74e44eb59afb61a24f2cb9f4d7188748e76ba4bb5edfa3cb7d1c"},
-    {file = "attrs-19.3.0.tar.gz", hash = "sha256:f7b7ce16570fe9965acd6d30101a28f62fb4a7f9e926b3bbc9b61f8b04247e72"},
+    {file = "attrs-20.3.0-py2.py3-none-any.whl", hash = "sha256:31b2eced602aa8423c2aea9c76a724617ed67cf9513173fd3a4f03e3a929c7e6"},
+    {file = "attrs-20.3.0.tar.gz", hash = "sha256:832aa3cde19744e49938b91fea06d69ecb9e649c93ba974535d08ad92164f700"},
 ]
 babel = [
-    {file = "Babel-2.8.0-py2.py3-none-any.whl", hash = "sha256:d670ea0b10f8b723672d3a6abeb87b565b244da220d76b4dba1b66269ec152d4"},
-    {file = "Babel-2.8.0.tar.gz", hash = "sha256:1aac2ae2d0d8ea368fa90906567f5c08463d98ade155c0c4bfedd6a0f7160e38"},
+    {file = "Babel-2.9.0-py2.py3-none-any.whl", hash = "sha256:9d35c22fcc79893c3ecc85ac4a56cde1ecf3f19c540bba0922308a6c06ca6fa5"},
+    {file = "Babel-2.9.0.tar.gz", hash = "sha256:da031ab54472314f210b0adcff1588ee5d1d1d0ba4dbd07b94dba82bde791e05"},
 ]
 backcall = [
     {file = "backcall-0.2.0-py2.py3-none-any.whl", hash = "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"},
@@ -2128,8 +2170,8 @@ black = [
     {file = "black-19.10b0.tar.gz", hash = "sha256:c2edb73a08e9e0e6f65a0e6af18b059b8b1cdd5bef997d7a0b181df93dc81539"},
 ]
 bleach = [
-    {file = "bleach-3.1.5-py2.py3-none-any.whl", hash = "sha256:2bce3d8fab545a6528c8fa5d9f9ae8ebc85a56da365c7f85180bfe96a35ef22f"},
-    {file = "bleach-3.1.5.tar.gz", hash = "sha256:3c4c520fdb9db59ef139915a5db79f8b51bc2a7257ea0389f30c846883430a4b"},
+    {file = "bleach-3.2.1-py2.py3-none-any.whl", hash = "sha256:9f8ccbeb6183c6e6cddea37592dfb0167485c1e3b13b3363bc325aa8bda3adbd"},
+    {file = "bleach-3.2.1.tar.gz", hash = "sha256:52b5919b81842b1854196eaae5ca29679a2f2e378905c346d3ca8227c2c66080"},
 ]
 blessings = [
     {file = "blessings-1.7-py2-none-any.whl", hash = "sha256:caad5211e7ba5afe04367cdd4cfc68fa886e2e08f6f35e76b7387d2109ccea6e"},
@@ -2141,38 +2183,46 @@ boltons = [
     {file = "boltons-20.2.1.tar.gz", hash = "sha256:dd362291a460cc1e0c2e91cc6a60da3036ced77099b623112e8f833e6734bdc5"},
 ]
 certifi = [
-    {file = "certifi-2020.6.20-py2.py3-none-any.whl", hash = "sha256:8fc0819f1f30ba15bdb34cceffb9ef04d99f420f68eb75d901e9560b8749fc41"},
-    {file = "certifi-2020.6.20.tar.gz", hash = "sha256:5930595817496dd21bb8dc35dad090f1c2cd0adfaf21204bf6732ca5d8ee34d3"},
+    {file = "certifi-2020.11.8-py2.py3-none-any.whl", hash = "sha256:1f422849db327d534e3d0c5f02a263458c3955ec0aae4ff09b95f195c59f4edd"},
+    {file = "certifi-2020.11.8.tar.gz", hash = "sha256:f05def092c44fbf25834a51509ef6e631dc19765ab8a57b4e7ab85531f0a9cf4"},
 ]
 cffi = [
-    {file = "cffi-1.14.2-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:da9d3c506f43e220336433dffe643fbfa40096d408cb9b7f2477892f369d5f82"},
-    {file = "cffi-1.14.2-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:23e44937d7695c27c66a54d793dd4b45889a81b35c0751ba91040fe825ec59c4"},
-    {file = "cffi-1.14.2-cp27-cp27m-manylinux1_x86_64.whl", hash = "sha256:0da50dcbccd7cb7e6c741ab7912b2eff48e85af217d72b57f80ebc616257125e"},
-    {file = "cffi-1.14.2-cp27-cp27m-win32.whl", hash = "sha256:76ada88d62eb24de7051c5157a1a78fd853cca9b91c0713c2e973e4196271d0c"},
-    {file = "cffi-1.14.2-cp27-cp27m-win_amd64.whl", hash = "sha256:15a5f59a4808f82d8ec7364cbace851df591c2d43bc76bcbe5c4543a7ddd1bf1"},
-    {file = "cffi-1.14.2-cp27-cp27mu-manylinux1_i686.whl", hash = "sha256:e4082d832e36e7f9b2278bc774886ca8207346b99f278e54c9de4834f17232f7"},
-    {file = "cffi-1.14.2-cp27-cp27mu-manylinux1_x86_64.whl", hash = "sha256:57214fa5430399dffd54f4be37b56fe22cedb2b98862550d43cc085fb698dc2c"},
-    {file = "cffi-1.14.2-cp35-cp35m-macosx_10_9_x86_64.whl", hash = "sha256:6843db0343e12e3f52cc58430ad559d850a53684f5b352540ca3f1bc56df0731"},
-    {file = "cffi-1.14.2-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:577791f948d34d569acb2d1add5831731c59d5a0c50a6d9f629ae1cefd9ca4a0"},
-    {file = "cffi-1.14.2-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:8662aabfeab00cea149a3d1c2999b0731e70c6b5bac596d95d13f643e76d3d4e"},
-    {file = "cffi-1.14.2-cp35-cp35m-win32.whl", hash = "sha256:837398c2ec00228679513802e3744d1e8e3cb1204aa6ad408b6aff081e99a487"},
-    {file = "cffi-1.14.2-cp35-cp35m-win_amd64.whl", hash = "sha256:bf44a9a0141a082e89c90e8d785b212a872db793a0080c20f6ae6e2a0ebf82ad"},
-    {file = "cffi-1.14.2-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:29c4688ace466a365b85a51dcc5e3c853c1d283f293dfcc12f7a77e498f160d2"},
-    {file = "cffi-1.14.2-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:99cc66b33c418cd579c0f03b77b94263c305c389cb0c6972dac420f24b3bf123"},
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@@ -3083,13 +3229,13 @@ typed-ast = [
     {file = "typed_ast-1.4.1.tar.gz", hash = "sha256:8c8aaad94455178e3187ab22c8b01a3837f8ee50e09cf31f1ba129eb293ec30b"},
 ]
 typeguard = [
-    {file = "typeguard-2.9.1-py3-none-any.whl", hash = "sha256:e258567e62d28f9a51d4f7c71f491154e9ef0889286ad2f37e3e22e4f668b21b"},
-    {file = "typeguard-2.9.1.tar.gz", hash = "sha256:529ef3d88189cc457f4340388028412f71be8091c2c943465146d4170fb67288"},
+    {file = "typeguard-2.10.0-py3-none-any.whl", hash = "sha256:a75c6d86ac9d1faf85c5ae952de473e5d26824dda6d4394ff6bc676849cfb939"},
+    {file = "typeguard-2.10.0.tar.gz", hash = "sha256:d830132dcd544d3f8a2a842ea739eaa0d7c099fcebb9dcdf3802f4c9929d8191"},
 ]
 typing-extensions = [
-    {file = "typing_extensions-3.7.4.2-py2-none-any.whl", hash = "sha256:f8d2bd89d25bc39dabe7d23df520442fa1d8969b82544370e03d88b5a591c392"},
-    {file = "typing_extensions-3.7.4.2-py3-none-any.whl", hash = "sha256:6e95524d8a547a91e08f404ae485bbb71962de46967e1b71a0cb89af24e761c5"},
-    {file = "typing_extensions-3.7.4.2.tar.gz", hash = "sha256:79ee589a3caca649a9bfd2a8de4709837400dfa00b6cc81962a1e6a1815969ae"},
+    {file = "typing_extensions-3.7.4.3-py2-none-any.whl", hash = "sha256:dafc7639cde7f1b6e1acc0f457842a83e722ccca8eef5270af2d74792619a89f"},
+    {file = "typing_extensions-3.7.4.3-py3-none-any.whl", hash = "sha256:7cb407020f00f7bfc3cb3e7881628838e69d8f3fcab2f64742a5e76b2f841918"},
+    {file = "typing_extensions-3.7.4.3.tar.gz", hash = "sha256:99d4073b617d30288f569d3f13d2bd7548c3a7e4c8de87db09a9d29bb3a4a60c"},
 ]
 typing-inspect = [
     {file = "typing_inspect-0.6.0-py2-none-any.whl", hash = "sha256:de08f50a22955ddec353876df7b2545994d6df08a2f45d54ac8c05e530372ca0"},
@@ -3097,12 +3243,12 @@ typing-inspect = [
     {file = "typing_inspect-0.6.0.tar.gz", hash = "sha256:8f1b1dd25908dbfd81d3bebc218011531e7ab614ba6e5bf7826d887c834afab7"},
 ]
 urllib3 = [
-    {file = "urllib3-1.25.10-py2.py3-none-any.whl", hash = "sha256:e7983572181f5e1522d9c98453462384ee92a0be7fac5f1413a1e35c56cc0461"},
-    {file = "urllib3-1.25.10.tar.gz", hash = "sha256:91056c15fa70756691db97756772bb1eb9678fa585d9184f24534b100dc60f4a"},
+    {file = "urllib3-1.26.2-py2.py3-none-any.whl", hash = "sha256:d8ff90d979214d7b4f8ce956e80f4028fc6860e4431f731ea4a8c08f23f99473"},
+    {file = "urllib3-1.26.2.tar.gz", hash = "sha256:19188f96923873c92ccb987120ec4acaa12f0461fa9ce5d3d0772bc965a39e08"},
 ]
 wandb = [
-    {file = "wandb-0.9.6-py2.py3-none-any.whl", hash = "sha256:0da3f73992cbb181bbb3080a233c387e28d92c7f6f0be9c67401a941d15402cb"},
-    {file = "wandb-0.9.6.tar.gz", hash = "sha256:6d712c3744e4af0d91f3872be0fc96b338706136a6980a653565c381ac3f4550"},
+    {file = "wandb-0.9.7-py2.py3-none-any.whl", hash = "sha256:21d6f17c868c5de6b400c878962c1933f0574f1088f981b99f393cfeb80410b0"},
+    {file = "wandb-0.9.7.tar.gz", hash = "sha256:b07a4cc7c317528273bd10ba903fd3fe851cab995d4ddaa7491b55e292f1c87d"},
 ]
 watchdog = [
     {file = "watchdog-0.10.3.tar.gz", hash = "sha256:4214e1379d128b0588021880ccaf40317ee156d4603ac388b9adcf29165e0c04"},
@@ -3120,14 +3266,14 @@ widgetsnbextension = [
     {file = "widgetsnbextension-3.5.1.tar.gz", hash = "sha256:079f87d87270bce047512400efd70238820751a11d2d8cb137a5a5bdbaf255c7"},
 ]
 win32-setctime = [
-    {file = "win32_setctime-1.0.1-py3-none-any.whl", hash = "sha256:568fd636c68350bcc54755213fe01966fe0a6c90b386c0776425944a0382abef"},
-    {file = "win32_setctime-1.0.1.tar.gz", hash = "sha256:b47e5023ec7f0b4962950902b15bc56464a380d869f59d27dbf9ab423b23e8f9"},
+    {file = "win32_setctime-1.0.3-py3-none-any.whl", hash = "sha256:dc925662de0a6eb987f0b01f599c01a8236cb8c62831c22d9cada09ad958243e"},
+    {file = "win32_setctime-1.0.3.tar.gz", hash = "sha256:4e88556c32fdf47f64165a2180ba4552f8bb32c1103a2fafd05723a0bd42bd4b"},
 ]
 xdoctest = [
     {file = "xdoctest-0.12.0-py2.py3-none-any.whl", hash = "sha256:82424d2cc4b6d6b96b7b7134c81e97a4594c536547c1954533128a6a26cf1cb2"},
     {file = "xdoctest-0.12.0.tar.gz", hash = "sha256:2d985d8d78d4444079d3b072965327ab06a5e6dcb4882f3561d7596eb4da6b13"},
 ]
 zipp = [
-    {file = "zipp-3.1.0-py3-none-any.whl", hash = "sha256:aa36550ff0c0b7ef7fa639055d797116ee891440eac1a56f378e2d3179e0320b"},
-    {file = "zipp-3.1.0.tar.gz", hash = "sha256:c599e4d75c98f6798c509911d08a22e6c021d074469042177c8c86fb92eefd96"},
+    {file = "zipp-3.4.0-py3-none-any.whl", hash = "sha256:102c24ef8f171fd729d46599845e95c7ab894a4cf45f5de11a44cc7444fb1108"},
+    {file = "zipp-3.4.0.tar.gz", hash = "sha256:ed5eee1974372595f9e416cc7bbeeb12335201d8081ca8a0743c954d4446e5cb"},
 ]
diff --git a/pyproject.toml b/pyproject.toml
index 95e32a6..c977270 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,8 +22,8 @@ sphinx_rtd_theme = "^0.4.3"
 boltons = "^20.1.0"
 h5py = "^2.10.0"
 toml = "^0.10.1"
-torch = "^1.6.0"
-torchvision = "^0.7.0"
+torch = "^1.7.0"
+torchvision = "^0.8.1"
 loguru = "^0.5.0"
 matplotlib = "^3.2.1"
 tqdm = "^4.46.1"
@@ -37,7 +37,6 @@ python-Levenshtein = "^0.12.0"
 defusedxml = "^0.6.0"
 pytorch-block-sparse = "^0.1.2"
 pytorch-metric-learning = "^0.9.92"
-faiss-gpu = "^1.6.3"
 omegaconf = "^2.0.2"
 
 [tool.poetry.dev-dependencies]
diff --git a/src/notebooks/04a-look-at-iam-lines.ipynb b/src/notebooks/04a-look-at-iam-lines.ipynb
index 576dd5e..0187518 100644
--- a/src/notebooks/04a-look-at-iam-lines.ipynb
+++ b/src/notebooks/04a-look-at-iam-lines.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -66,7 +66,7 @@
        "(28, 952)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -86,7 +86,7 @@
        "(97, 80)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -106,7 +106,7 @@
        "'He rose from his breakfast-nook bench____________________________________________________________'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -141,7 +141,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -151,7 +151,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -161,7 +161,7 @@
     },
     {
      "data": {
-      "image/png": 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sx/58urqdHjXOPjk9g0S4EQRBEARBEARhoSGijSC8B3grYVPp0FozOjo6Q5RJJ9I47XDWZbx30tk3H7vt9aVqy3j6GFtmE5gEQRAEQRAEQRAuNiLaCMJ7gPkIH6kEl3QeNHZisVhK4cN+zZ43xin4ONu057VJ1Z9U3jL2Z+1CkD03TjqBxh5aJQiCIAiCIAiCsBCQI7+FdxXvdS+JC9H/uYQs2UOTUnncGGHGWW80GrXEG/t1I/ikyl1jyjjtMPdNXeYZ893kunG5XDM8b5zt271wUnnpCIIgCIIgCIIgXAx+ozxtXC4XhYWFVFZWcvz4cSuXxnuNZcuWUVxcTEdHB319fdPulZeXU1JSQjAYpL29/eIYeBERL4nppPMcmY9HiTMRsfOaweVyEYvFpt1PVZcpYy+XKj/NbOFSzgTH9jrMc3Zhxo7xwnHmx7HXJetIEARBEARBEISFwG+UaGOOHG9sbOTMmTPvWdEmMzOTiooKsrOzZ4g2ubm51NfXAzAxMUFxcTFnzpwhEolcDFOFd5jzeai81TrnW87uhWM8b5xl5mqn/dQn53W7x835yptrqRImA7jd7mkeP4IgCIIgCIIgCBebeYVHXeywgXg8TigUoqKiIu1f0d8LxONxMjIyKC8vn7Fpdbvd5OTkUFdXR3Z2Nh/+8IcpKChIO2/5+fnk5eXh9XrfKfOFOeDxeN7yu5bqSOtUZd4OqfLJGGEGZve0OV+9c00IbPfSSVW/M9wq1XHg9nLmXbrYv3OCIAiCIAiCIAiGeSkfXq+XRYsWkZubi8dzcZx0pqamKC4ufk+LNsbDqLCwcMa9UChEJBKhtLSUaDTK8uXLycvLw+12p6zL5/NRXV1NWVnZr9NkYR54vV5qamrIzc1NW8btdpORkUFGRsY7aNncsee6mU8YlvOfue68nwqv10tBQQFut9sSXmYrP5voJAiCIAiCIAiCsBCYl/Lh8/lYv349DQ0N5Ofn/7psSovWmng8TlFRUVoR4r1AOBwGoKCgwLpmNqd+v5+RkRFycnIYHx/n8OHDKKVSem4opYhGozQ0NFghVcLFJzs7m0svvZTS0tKU991uN2VlZdTX17No0aI51ZlKwHi73iTpjvNOVW+qtZeqjFNssScSTicAGS+ZvLw8KisrcbvdM7xm7Lly0gk5JteOhEYJgiAIgiAIgrBQmHd41IoVK7jyyitZtmzZrJu+t7ohnEv4RGFhIT6fb17CzWx/rf9NCoUw+UEikUhKb6NgMMj4+Li18dy1axfDw8NEIpGU+U6Gh4fJzs5O6bWz0DGb83cbU1NTNDY2pp2TwsJCbr31Vr7whS9wxx13zDmMyPn97YgTzvfHrMV0iYTteWacpzjZnzGnTsXj8ZSeL6nq11qTmZlJVlYWbrd72nVnYmLz/qT6PTDX341rShAEQRAEQRCE30zmJdp4PB6eeOIJRkdHrQ0SJBLj2jc6xcXFLF26lNLS0nnntkiVqDTV8b5er3faX9TTkS5R6WxJU1OFZSwkPB4PLpeLQCBARkaGFQ5iNqwdHR3cd999xONx2tramJycnJbPw47WmqysLLKzsxdcPzMzM2e9/8lPfpLa2tp3nddVNBplyZIlFBYWzhDmlFJcccUVBAIB6wQ1t9s9bQzSrfkLIdbY6wOorKxk8+bNXHPNNdOuz3ZkuF1MSReS5MzT5PF4rPfdiC9m3Zu1X1paSmNj44z+2uspLS0lIyMjpX1ut3uaoCMIgiAIgiAIgnCxmVdiGr/fTzweZ2RkhJKSEm688UZ27NjB6tWr2bdvH93d3XR3d+PxeCgoKGBwcJAHHnggZV2zbRxThTLYBZSpqSny8/NZv349zc3NRCIRfvKTn9DZ2TmntpzeJnYxY7YwjlSbXvtf73/dYRWmfSOSDQ8PT7tuxikQCHD69GnrxChzIo4pY7fT7XZbSYhTiToXC7fbzaZNmxgbG+PkyZOEQqEZZZqbm3njjTcYGBggGAxeBCt/PcRiMaLRqCVG2IWNkpISMjMzaWpqYmRkhEceecQSP9LlkDHf7Z4kF2KelVIMDQ1RXl5OY2MjlZWV9Pb2TrtvbzfVe+h2u4lGo7OKuOb+bKdBxWIxPB4PhYWFKcXWgoICVqxYwTXXXMN3vvMdBgcH5ZQoQRAEQRAEQRAWPPMSbaamplBKEQqFrPCFsbExotEoPT099Pf3o5QiEong9/upq6uzNlTpcG7G7N+deSyM18H4+DhNTU0UFBTQ2dlJMBhk586dfPvb355RhzOfRSqPA601GRkZKY/7TffZ7Xazdu1a1q1bR0lJCX19fezfv5+DBw+ilKKkpIRwOMzk5OQFO5pca215HZh5cHoUmA2+EZFMn9J5WdjrNJ4L6UJT3kni8ThHjhyhqKhoVq8N42nxbsK5Bu3zu3nzZlauXInL5aKnp4eOjg7rmXRcSA8bO2atnD17lkgkYnn7pDqlKV3bLpeLiooKhoaG0q4554lSZs0ajGgaj8dxuVxkZWVNE/G01gSDQbq6ujhx4gSlpaUMDw8Ti8XOK8oKgiAIgiAIgiBcTOYl2sTjcWKxGPF4nImJCSKRCMPDw9TV1aG15ujRo9YGq7CwkMbGxmnCi1P0yMvLs0SNdH9FN9g3V5FIhDVr1tDS0sKpU6fIz89n3bp1VnhQU1MTlZWVjI6OcurUKfx+Pz6fj2AwaAkodg+exsZGNmzYwODgIIcPH6a7u3tau07bvF4vTU1NVFRU0Nvby9jYGAUFBSxZsoRjx47R3NxMTU0NXq+X1tZWenp6rE3t5OTkece5uLiYZcuWUVhYyPDwMPv27bM2tFprKzxqdHR0zrlDZsPlcpGdnU1DQwObNm1iaGiIp59+eprYppQiJyeH5cuXEwqFOHv2LJOTk3MWd8wGf65orRkZGSEej6cVvd7q5trlclFdXU0sFmNkZORte+lkZWURDoen9c/tdrN+/XqOHj1KMBicd98jkciMZ5RSbN68mdraWg4cOMC+ffusOXg73jNKKbKysojH40xNTc2pvLETEiLq1NTUtBPl0tniXKsej4cNGzbQ2tpKf3//tL6kC+ez3zdCIyQSdIdCIYqLi+np6Zm2fs1JW4cOHWJ0dDTl2Jr/imgjCIIgCIIgCMJCYV6ijV0gCIfDxGIxYrEYXV1dLFq0iLNnzxIIBHC5XNTX15Obm0ttbS25ubn09/czOjrK1NSUdaRxWVkZQ0NDdHV1pdw4pzrpxWzU8vPz6erqorOzk2XLllk5bvLz82lubmbRokWMjo4CcODAAerr6wkEAnR3d6OUori4mIKCArq6utiyZQtLliyx8sT09/enTNwLic1fSUkJa9eupa2tjSNHjhCLxWhubqa0tJT8/Hw2bdpEKBSivLwcSIg8AwMDlJWVcfjwYRYvXszQ0BDRaJS8vDwKCwsJBAJ0dXXh8/lYunQpK1asoL6+nmg0ypEjRyyvGkiIBABDQ0OzegWlymvixJQpKipi9erVrF27lszMTF555RUmJiYsr4bc3Fzq6+tZsmQJxcXFvPzyy3R0dBAIBNKuFyMG1dbW4vP56O3tZWhoaJowcD7vhrGxsRl1mvIulyvlqVjno7KyklWrVuFyuejs7KSzs5OJiQmUUhQWFlohNhMTEwwMDMwIhysoKCAvL89a9zU1NZw9e5bh4WFCoZCVO2XTpk309/fT29uLz+ejoKCAeDxOf3+/dQJYKkpLSyksLKSiooKysjL8fj+Tk5N4vV42bNjA2bNnOXDgAMePH7c8qswYlJeXEwqFmJiYmCb0pQo5NP1atGiRlfR4ZGSEnp6eGTa5XC5ycnKorKyku7ubqakpq/5YLDZn4cvpBed2uykuLiY3N5fBwcFpYYbpBBtnP8y/UCjE+Pg4NTU1+P1+SkpKGBoaIhAIkJeXR01NDYcPH2Z8fNwShzIyMvB4PMRiMcLhsOSzEQRBEARBEARhQfGW40qMB0QkEmFwcJCCggIrFCoSiaCUory8nA984AP84R/+IVdddRWVlZV4vV7Ky8u56aab2LhxI7fccgulpaVWklGzUTObKefGzOTLOXz4MKOjo5aXTmZmJh6Ph40bN6KU4qWXXmJwcJANGzaQkZHB2rVrufnmmykpKaGyspIdO3bw0Y9+lPLycpqamvj3f/93wuEwRUVF+Hw+q5/OMKvCwkLWrFmDUoq9e/cSCASIRqMcPHiQ3bt3s2jRIgoKCnjooYf4+c9/Tm1tLZdeeilVVVVce+215OTkcOedd7JhwwaamprYsWMHH//4x7n11lvx+XwsW7aMpqYmurq6OHnyJMuXLycrK2vaGGRkZBCPx+np6Zm2AXd6NKULT3HmwIlEIpSXl7N48WLuuecevF4vRUVFZGRkoJQiNzeX5cuXs337dnp7e1m7di1LliwhPz9/1k1udnY2K1eu5Pd+7/f4oz/6I66//nrKysqseczJySE/P5+ioiK8Xu+sYU6ZmZnk5+dTUlJCaWkp2dnZVh3nS1js5H3vex+1tbUsX76cbdu2UVNTAyTynqxdu5abbrqJ22+/ne3bt+Pz+ab1MTs7m9WrV3PjjTfi8/nYuXMnn/jEJ7jqqqsskS4rK4uNGzcSj8cpLCwkJyfHeua6666jpqZmWp1GfDLvwMaNG1m/fj3btm1j8+bN1NXV4fF4aGhoYOnSpTz22GMcOHDAEr/s78327dtZuXLljDFxCnrmu9frZfv27WzdupX3v//9XHvttdPWvyEnJ4dVq1Zx1113UV1dbdnq8Xgszy9TZ3Z2Nrm5uWRlZVn3zJoqLi4mPz/fuhcOh9m9ezd9fX0zvF9mCxMznlvRaNQKAQwGg4yNjbF8+XKWLl3K7bffzurVqyksLCQvL4+qqiorCXFWVpZ1raGhgUWLFpGTk2OJgSLeCIIgCIIgCIKwEJiXpw3MTOIbj8cJhUIUFRVZmx3zF/TMzEyqqqr4u7/7O/74j/+YyclJXC4XixcvRmvN9773Pe6++27KysoYGBiwks26XC4uu+wyenp66O3ttbwSTL11dXU88MADVl4KSISjFBQUcOWVV/L973+foaEhiouLrZCNJ554gm9961u0tLRQVlZGVlYWv/rVr1i3bh3d3d0UFBRQUVGB2+227LBvhs1f9isqKrj00ku57777rOsmUWpeXh6LFi3iwIED+Hw+/H4/ra2tltfF6tWrufLKK6mpqeHTn/400WiU1157jV/84hfcfPPNXHfddaxZs4Ynn3ySNWvW0NjYyGuvvWZ5TZjxHRgYYHJykrGxsVnDUFIlnXXmDDLzZHL6dHZ2smfPHsrLyxkaGsLj8bBixQquuuoqhoaG+OIXv8hTTz3Fvn376OvrS9u+2+1myZIl3HLLLfzLv/wLo6Oj3HLLLWRmZuL1emloaGDnzp0UFBSQn5/PE088QUtLCwMDAzPsVEqxZcsWtm3bRkNDA1lZWTzyyCP4/X7KysooLCxkZGQkZaJbMxb2/jY3N/Pwww9z8OBBK0TH6/Xy+7//+xw6dIgHH3yQJUuWWG3u3r2bUChENBqlqqqKnJwczp07xyWXXALAwYMHLY+pc+fOkZ2dzcaNG7n77ruZnJxk3bp1VFdX09fXR1dXF3/1V3/Fpz71KSs3VHFxMaWlpcRiMdrb28nNzaWvr4+jR4/S1tZmeWndfffd7Nu3jxMnTjA2NjYtX5Hp66WXXorH4+H48eMp17FzbLZs2UI4HObJJ5+kvr6eyy+/nNtuu40f/OAH08qvXLmS7du3c+LECbZt20Z3dzfRaJRFixaRm5vL6Ogovb29ZGdnc9NNN1FfX8+ZM2fYv38/586dY8uWLdx5551kZWVx+vRpnn/+ed544w3Gxsbo7u6ekW/KOX8ej2eaV5E9F5O5FggEGB0dpba2lq1bt/Lss8+ydetWlFL4/X6KioqIxWK43W6uuuoqtm3bRk5OjuV1c+rUKb797W9f9HxOgiAIgiAIgiAIhnmLNgaTBNZsnMxmyGy0TMjECy+8wOjoKC0tLRQVFbFt2zaysrI4dOgQf/Inf0JRUZGVD8R4Wng8Hq6++mqeffZZRkZGCIfDlkBkTtYZGRmx8n5MTk7S29tLXV0dmZmZRKNRVq9ezebNm1m0aBGXXXYZv/rVr7jnnnt4//vfT1VVFXv37uW5555jx44d+Hw+vvjFL/LII4+wd+9eYrHYjBwspr+ZmZlWKAcw43QfpRT19fW88MILxGIxuru7LU+KU6dO8bnPfY7PfOYzfOlLX6Kvr4/Dhw9z8OBB4vE4v/M7v4PH4+HGG28kFovR0tLCrl27rA2q2Xh3dHRYYktxcTF9fX3A/PLYQMJbKiMjg4KCAsLhMCdPniQejzMwMGDZvG3bNnbu3MnixYt5+umn+YM/+ANGRkYIBAKz5v7weDxkZmaitaa3t5dYLMaPfvQjMjIy2LFjB+vXr+fRRx+ls7OTwsJCvvCFL3DFFVfw3HPP8fLLLwOwbt06Wlpa2Lx5M2vXrqWzs5N/+7d/w+PxsHPnTurq6ojFYpw5c4b29vaU4TQGn8/HDTfcQGtrK2fOnGF0dHSa4HfrrbfS1tbGnj17WLp0KZdccgler5c33niDNWvWsHz5cnbt2kVtbS3Z2dn09PTw4Q9/mFdeeYUrrriCb37zmxw/fnxaziETbnPZZZfR19dHb28vN910Ex0dHWzfvp1QKMTixYupq6ujvr6e6upq7rrrLp566ik2b97M8ePH6erqwuVy0dDQwCOPPEJjY6MlMvh8Pnw+H5FIxArvysnJweVyzTiRySmCmXdt+/bt/OhHP2J4eJjm5mYaGhpoaGjg/vvvt0LyysrKWLp0KTk5OTz00EOWN53L5aKwsJCGhgbC4TADAwO43W42bNjAnj17aGho4Oqrr7ZyYH3lK19hYmKC5uZmtm7dysaNG7n//vvZsmULP/vZz6zTzoqLi6mqqiIzM5P9+/dz++23s3PnTh566CGef/55a30qpaYlEza/QWvWrOHRRx/l9ddfZ/HixXi9XkuYU0rxoQ99iHXr1vHII49w6tQpy2PJ3BcvG0EQBEEQBEEQFgrzFm1Shdvk5ubS1dVlbVLNBnFiYsI6lvrUqVOsWLGCVatWsXjxYsbHxzl06BBf/epX6ezsJC8vjyVLlrBq1Sq6u7u55ppr+OlPfzrtqGeToPU//uM/6OvrsxLUmoTIQ0NDNDU18clPfpKzZ8+yd+9e2tvbLU+dlpYWbr/9doqLi3G5XIRCIQYHB/mzP/szvv71r/Paa69ZYozH46GiooLOzs5p4UfRaJRwOExjYyOtra3T8ob4/X5Onz7Nxz/+cQ4dOmSFXrW3t1t1X3755QwPD/PSSy8RjUYZHR217J+cnKS5uZmWlhZaWlro7Oy0BCR7smZzvLFJfmxEm3RzNds1k68mHo9b9Sil8Hq9lvfI0aNH+da3vkV/fz9+v39Op2FFIhG6u7s5ePAg3/jGN/iHf/gHenp6WL9+Pfn5+bz++uucPHmSSCRCNBplfHycSy+9lGAwSGtrK8FgkGuvvZbjx49z2WWXMTg4yL59+yxx4pe//CVaay6//HKKi4vPaw8kQqxuueUWOjo6rDVhPLRWrlzJrl27qK6uZt26dfT39/Poo48yOTnJ2bNnueOOO3jppZcoKyujqKjIOu0oJyeHb37zm5w5c8YSDfLy8ti8eTMul8vKgVRdXU13dzd9fX08/vjjNDY28rnPfY5z587xwgsv8OKLL3LDDTdw6aWX8sILL1gipUkO3NbWxsTEBDfeeCPl5eWMjIxQUFDA5s2bue2229izZw9PPfUUtbW1HDlyxAotTJWk2Kxlt9tNZWUl4XCYqqoqSktLGR8fZ3x8nMbGRktMLC4uxufzMT4+TjgctvI9AYyOjhIIBKw5CAaD+P1+Ojo62Lt3L/X19Vx33XVs3bqV+++/n+HhYfbv38/Y2BhNTU3ceuutFBcX86tf/YrOzk5Wr17N6tWraWhoIDc3l8zMTMLhMLt27aK+vp4tW7bwzDPPWF5Kpj8mPMuE87366qtMTEzg9/stUcblcpGbm0tRURFut5vs7GwyMjKsEEeTp0tEG0EQBEEQBEEQFgpvy9MGEt4akUiEffv2WRsppRTj4+McOHCAoaEhACspcF9fH6dOnaK6uppz585x7tw5pqam0FozOTnJ+Pg4kBAmJiYmZhwXHo1G2bt3L36/39qkTU1NMTAwwMTEBD/5yU8YGxuzQqtMu7FYjLGxMVpbW2lqaiIejxMOhzlx4gRtbW2sXr2avr4+ampqKCkpoaysjEOHDtHV1TWtv/39/bS0tPBbv/VbVuJZk0h4aGiIgYEBHnroIVasWEEkEqGlpYUzZ87Q19fH1NQU9957L5OTkzz//PNAItGu8Rp45ZVXWLRoERUVFVRVVeF2u8nKyqK4uJjHH3/cEku01oTDYUZHRzlx4sR5Q0tmm0MjMpg5Mt4LkMhN4na7GR0d5cyZM0QikZTHoqciHo8zMjLC7t27AfjoRz/Ks88+y9q1axkbG7M8HJRSNDc309/fT0lJCZFIhNzcXCKRCJWVlZaHRDQaJRAIWOtrbGyMvr4+CgoKKCgowOPxTBMTnEQiEVpbW/nIRz7Cvffea42lEVUikQjBYJCJiQn6+voIBAKMjY0Rj8fx+/0UFhZyySWX4PP5iMViFBUVMTExwe7du+nu7iYQCFgC29jYGE8++aQlKng8HoaGhti/f7+V3Dg/P5+lS5eyZ88eDh48SDgcpqOjg6qqKlwuF+3t7fj9fksEDYfDnDt3jocffpjGxkbC4TC9vb20trYSjUbJyMhgamrKSg5uf29mE+8yMzPJzs6mubmZzMxM9u3bRywWY8uWLRw5cgRIeCllZWVNC380fRsZGWFoaIjs7GxLPJmamrKExnA4bNm+ZcsWOjs78Xq9VFVVUVRUZJ0YtXTpUpRSrFq1inA4TGtrK1u3bmXlypUcP34cr9fLpk2bqKmpIR6P88QTT8xY70a4MWKN1prBwUErWbaZn927d+NyudixYwdXX301vb297N+/n3379r3rjo8XBEEQBEEQBOE3m7ec08Yu2gwNDVkeI5DYEI6Pj9PS0mLl3jCn5vT09NDZ2Ul1dTVDQ0NW6FM4HKa/vx+Xy0V+fr513LMzh45SyvKGMZvIQCDAiRMnCIVCPPfccwBMTk5O28QaW1999VX6+/utE6L6+vp46KGHaGxsZMWKFYRCISs5r9/vn9H/0dFRDh48yMqVK1m3bh3j4+OMjY0RDAYt4em5555jzZo1TExM0N7ebvVlcHCQl156iUgkQnt7u+UB4fF4LJHLJGutrq6mqKiIYDBIMBicIcLEYjECgYAVpjQXkSbdPJp8NmacjHBmxICCggKqq6stj6Dx8fE5tRMKhSwPrJqaGq666iry8vKIRqPk5uZSXFxMfX09S5cu5ciRI0xOTuL3+60wFSPq9Pb2kpmZSWlpKWfPngUSITTBYJBwOIzP5yM/P98SCFMRi8Xo6OiwcurYT6GampoiHA5z5ZVXEggE8Pv9DA4OWuF3oVCI48ePs3TpUnp7e+nu7sbj8VBcXExRURF+v59QKGQJWn6/3xJttNacPn3a8lyamJjA7XYzODhIa2srBw4coLe3l9zcXEs0isfj7N+/f1rOIK01oVCIZ599liVLlhAIBJiYmGBwcJDu7m4qKioIh8NMTk5atszmMWKEt/b2djZs2GDl4zECydatW8nIyACw5qGgoMAKBzR9C4VCDAwMWDlnXC4XkUiEJUuWUFJSgsfj4cyZM3R3d7N582YOHz5MVlYWBQUFVo6furo6S4wpLi7mxIkTnDx50soxVV1dTV5eHqOjo1RUVHD55Zfz/PPPW++cM7H28PCwFR5m+hkMBunt7SUajXL69Gl8Ph/r1q2jtLSU4uJitmzZQk1NDU888cS0900QBEEQBEEQBOFiMi/Rxmxk7BteI8Y4E6KGQiFOnDgBYAkCp06dsjwYuru7ZwgqY2NjhMNhKzwlXTJdpw3j4+O0tbXhcrkYHR21TrEyG0t7Lo/W1lba2toALM+RXbt20dnZyapVqyw7Dx48yNjY2IxkxFNTU3R3d/Ozn/2M6667Dq/Xy6lTp+jt7bVOzRoaGuKFF16YZq/x7DEJc+31xmIx+vr6cLvdPPzww6xdu5aGhgaUUvT09Fh5dpx9SeXxkmqsUmG8EkzeHbsI19/fT25uLmNjYwQCASorK1m/fj0jIyMcOnTIEnVmw8ybye3y6quvctddd9HZ2Ynb7Wbt2rXk5eVxySWXMDg4SEtLC2fPniUrK4toNEo8Hufo0aPEYjEOHTrEpk2bWLVqFefOnSMWi9HU1MTIyAinTp3C6/VSV1fH8PDwrImZQ6EQe/futUKDICFIGK+sLVu20N7ezoEDB6zE2CYk74knnuCyyy6ju7ubY8eOUVpaisvlYuPGjUxOTlonmQGEw2GOHTtmjfHu3bvZsGEDq1atwuv1cu7cOcbHx/nBD37AqVOnLOHS7/dbfT5w4ICVw8nep7Nnz9LR0WH1ycxhe3s7VVVVTE5OEgwGpyUpNqeNOeuKx+M8++yzXH311Rw7doy2tja6urrIz8/H5/NRVFTE8PAwIyMjDA8PU1ZWZs2tIRaLWR5n5vdgcnKSlStXMj4+zunTp9m7dy8TExNcf/31rF27llAoxMmTJzl27Jh1RHpNTY2V0Hh0dJTh4WHeeOMNiouLufzyy8nIyODll1+moaGB9evXU15eTldXl9UnI1K53W76+/utULWcnByysrLo6+vj9OnTVtLtQ4cOcezYMfLy8li1ahUf/OAH+e3f/m2OHDnC6dOnRbQRBEEQBEEQBGFBoObqmQHgcrm0z+ez/tpuwiGMx4vZCJvNZGZm5rRwCbs3iH3DaT8VpqioiA9+8IPk5ubywx/+kPHx8WmJfs1mbFonkuKAXVQy9ZvNqr2MSeprDxGBN5MKm2OEjQhjt8++mXO73dM2wmYs7KKM3VvI7hVgTrUy9tvvmYTOziON7WOXdkJt4ou93lTijsm5UlhYCIDf77cSzLrdbsLhMFlZWSxevJgVK1bQ29vLnj17ZoSsOTEhTZmZmeTk5LBjxw62bt3Kgw8+SFtbG7W1tWzcuJFoNMozzzxDZ2en5aVlt90Ic5mZmdx0002sXr2awcFB+vv7qaur47vf/S51dXVs3bqVYDDI9773vVnz7bhcLlasWEFGRgYdHR2WJ5Xb7Z4RFmNfo/Y+2ddCRkYGWVlZ004xs4eqmfVhjqM3a8OEcZkj1Y1t5ghsMwb2f8Y+4wVmt9P0oampiTvvvJMXX3yRJ5980srlUl9fTygUYnh42Krf1GX6bn+Ps7Oz+djHPsaJEyd45ZVXCAaD1hHePT09lqhmb9/ehx07dtDc3ExrayuHDx8mHo+TmZnJl7/8Ze6//35OnjzJ+Pg4sVgMr9dLdXU1S5cu5ejRo0SjUfx+P36/3zoZasWKFQQCAYaHh2lqamLLli10dHRw3333EYlErD4UFRWxfPlympubuffee3G73dx8882cOXOGU6dOUVRUxKpVq2hra7O8xbKzs6muruayyy7jd3/3d/n7v/97du3aZXniCYIgCIIgCIIgvEPs1VpvdF6cd3iUcxNp9xaxCxWAJTjYsW+EnUKE2QgvXryYXbt2zdiAm7pMbgq7QAFvhvqYTaVduHGeBmXfpBuhwp4PxJS1eyw4RRj7Bt6UNW2ZsqnEHrNxN9edngupPIzs42gXgeynVzmFGec4O583otDQ0NC0eTHjbo4/b2tr48iRI3M+Cnn79u2sXLkSr9drhcD86Z/+qRVm09/fz759+6y1ZE+ybMcuhDzyyCO8/vrrrFy5kkgkwmOPPUYwGGR8fJzCwkJycnLOa1c8Hre8rOztOXPA2MfJPg8mbMZ8D4VClvDj9IIy9dqFN3M9Go1aophZK852jb1mLZgcMXZh1N4vpRQlJSXTQuYMlZWV1NXV8cILL8xIWm0XNu3j8dhjj/HZz36WlpYW/H6/lXjZvgZSrbd4PM6JEydYtWoV8GZolcvlYs+ePfh8PutdNGNoRBUzXk6R9tixY9aYt7W1kZ2dzR133MEDDzwwzVNvamqK3t5eBgYGrJDLZ555hnA4bIVKjo6Octddd1FRUWGJMibE80tf+hKvvPLKNGFLEARBEARBEAThYvKWTo9yhg6YDZX9mGH7d7NhtW9CjReCed6IMJmZmSxZsoTOzk7i8fg0rxPjIeMUh+z2OP86bq/bbG6NHfCmV445+cp+z9k/+xjMNjZ20cVuqxFZ7CKV3aPEWZfTq8aZTwjSizmz4RQs0j1nF4Pm45H10ksvWcleTR0mBMgpSJmTsOybdft8GTuCwSAdHR309vZam30jNrz44otWXecjVT9SrWl7v50eS/br6eo3Xmj2emDmHKXzWLF7G9lFPvs7Y54zQs6yZctob2+3jlg3dR45coSlS5dOW2d2Ycn+HRJzZRIG5+bmMjw8bHntzDaOxla/308gELDCD83a7+npsUTVcDhsHTlvF+eMLeadNP0wnkB+v5/e3l68Xi/Lly+nra3N+p2ZmJhgcnJyWt4dczy4y+VicnKS1157jUOHDuH1eq2wMSNShkIhpqamZk1oLQiCIAiCIAiC8E7ylk6PSuUd4hRUnCFBds8XpyBhyvh8PkpKSsjMzLROCrJ7vdiFELvYYBc+7Bs/Z96XVKFCZsPnFHTmumlz2mD37HGKN8YGpweM3UvJfj0dThFgrs/NVpfTE+it1GffbBvxzClspBIwnN4VzvGzj5NJPmsXO+Y7Z6lsTiWOzVYmlZfJbGKeE3sOFueY2MUnu9CZ6t0xZGRkUFtby6uvvmolZDbPGLFibGwspU1ZWVkz3rVoNEpLSwsNDQ2WUHK+8bWH5PX19U1LWB2LxRgfH2dwcBC/32+VjUaj1rvr/I0ArPEx92KxGIODg7zxxhts2LCBkydPWvftIp9zPdvF2MnJScvLydmu04tPEARBEARBEAThYjJv0cYujqTyeHGG0NhFG/s1Z51moxQMBnn++een5c2w5/0wmzPnRtrumeAMW3J6d6Syw3nP+X0+OG1zemqkI5Vtzs1nKhFkNhvmyoXwLDifqJEqr1GqNWPuO+fPfE41v/DW58wpNM7Wt1Tilr2eVM+YtZlqLp2eO3bBwS5qpfLasa/rrKwsAoGAleTYblssFuPs2bMzvGUMGRkZVFRUkJmZaYVglZWV4XK5KC8vx+fzzWlszf2pqSmOHDli5a0xdHV1MTExYXld2ftn71uqduy/BYFAgDNnznDFFVfw4IMPWvXBm94+pj772Nk9uOyJve2CjlmngiAIgiAIgiAIC4G3fOS3UzCBmYKO+ey8lsqTwXgYDA8P88wzz0zbSDk3u/bNqrnmTF7rbDuVmDSXPs6VdN4v9nupxiKVPc7PcxVqFiIX0n6nB1A6weft2nc+7GLD+YSe8wmBqbyO7N43zj6nssO8C62trfT19TE1NTWjfDAYTGtnLBYjIyOD7OxsAHw+H/X19QSDQSs0ai7CmLFnamqKjo6OGfb19/dPmzOncJWuvvz8fCuMLjs7m0WLFpGdnT3NS8fejvN5p0BkPGqcYyzeNYIgCIIgCIIgLDTmfeR3Og8W8z3VM06cngdGVIlGo0xMTMw4McqUcebfcG7QtNaWp0C6zVgqr5tUG+m5eMbYyzvtsNdjtyHVX/TPN26zeQfZr6US1NKRqm9vx7totnYMc7HLXtZeh93TIp2o9XZst9c71817Og+qdJ47s11zetmYe/Z6jdeIff2adycQCPBf//Vf1nPzIRAIcOzYMTIyMqyjvkOhENFolI6ODiYmJqa9s+mEMqcA4/RacYok9lPd7PmrTBnzbFVVFVprCgoKqKmpoba2lry8PB5//HErnMnehhkr+wlt5howzRvPjhnb3yRRVBAEQRAEQRCEdzfzOvLb7XZrk//Cvtl0JhU2my6TYBZmbpTM5tSeC8e+YXWGTDg3jM6Noz0Hit0eU4czFMMagGR5ezjW+Ui1Sbe3k06EOZ/XRSqxIF0Zu3dSqucX0sZzruFh53ve/t1wIfqZTvQ5X7vnE4vmKv44w8acYYH2+p1hUs73xn5vNtvS2WveB5Pc2B5KZN6/83mp2cMdnXalElw9Hs+M99X0VWtNaWkpVVVVFBUVEQwG6e/vZ2xsjHA4bOVPsv8mOU+JM9fNsetG2LWLu+b3ySQlDgQCsx4fLwiCIAiCIAiCcIFJeeT3vEQbpdQA0HEhrRIEQRAEQRAEQRAEQXiPU6+1LnNenJdoIwiCIAiCIAiCIAiCILwzyDEpgiAIgiAIgiAIgiAICxARbQRBEARBEARBEARBEBYgItoIgiAIgiAIgiAIgiAsQES0EQRBEARBEARBEARBWICIaCMIgiAIgiAIgiAIgrAAEdFGEARBEARBEARBEARhASKijSAIgiAIgiAIgiAIwgJERBtBEARBEARBEARBEIQFiIg2giAIgiAIgiAIgiAIC5D/C76tBn5G10ghAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -171,7 +171,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -181,7 +181,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -191,7 +191,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -201,7 +201,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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caODMMaVO4dkc+gtht3yO7kSbzXWuNJFLAd2hBrIjZfRxPde1Nt8Y0Gv0OTUTW6hQJ6OadOSc62LM2cSoXH0xs1+PAjLrg9lzvF4vbrvtNixatChrDHMJerk4W7SWLkDpgg69R39XLBYLAoEAEomEupb2V1/7drsdl112Gdra2pCfn6/6lZ+fj40bN8Lj8WS1Q4WtXELh/7TPMYZhGIZhGIb5XeRtE20u9v/s53refM7guTgl53Je/6eLFGZOVq5jFxP9ebmcVj3CwUxYoA6g2VjoKRsXsg/686jtNOJC70+uyJSLhRACLpdLpRzqQowulMmftD96e3qkUa7nSmi6k74GqWgjr6WpQWaCDoAz5pqOvVlf9PvM+k3R7dIjRuQ1NHKF/svPz8dNN92E2tpaOByOeT8bcs2BfIYufJiNhz629Hd9nqTwKIRAIBAAkBbH6Ljr42+xWGC321FbW4uVK1fC4/EoQS0/Px+tra3weDxnCIFURDL7LHg731uGYRiGYRiGYc6NixJpczH+pz9XJMF8kRZ6ZMjZ2p1PvJBOkqwbISM+5vsGX3di9W/dz3fcziYQ5eon/UedYN3OXI6e3W5X/abpHnQMzJz58+2jmciQTCaRTCaz0jpyCTULHee3Yq+ZkOJwOLB06VJUVlaa2iTtNotYoQ631WqFx+NBUVER3G438vLy4Ha7s6KOzAQHAFltmKXzyTopUtyhYgFN/aHrJi8vL6ttOvd2ux0ulwtOpzNrreRCT4Gif8v1lSuqx6x2kRACfr8fgUAA+fn5KgKFrlMzwVW3UaY20VRB+jzaXi6RUxdiaNuGYSAajcLlcsHlcql75E8h0vV1aLra6OgoioqKlK0WiwV5eXmoqKiA2+2GzWbLmns6ZvIzTH/vzzcqkWEYhmEYhmGYC8OCChEvhEvhf/TNogZ05ou6yNUWvZcW8qTXUuEgl8Ost5XrG/qFjqVui1m0TK4+SXIVUaZRF2bPo32RjiWty3G2SJdzYb7IByHeLDarF2qlfTNr72w2nYu9uebLrDaIzWbDXXfdhcHBQTz22GNIJpNZESK6WCEddNkvGUnR2NiI973vfaisrMSpU6dgtVoxOjqKF154AVNTU6o2De2DLlBQoUM+Qz+nR9aYrQ+r1YrPf/7z+OUvf4nu7m4kEglYrVYkk0kAwOLFi3Hrrbeit7cXe/fuhd/vV/d5vV4Eg0HEYjFlo7RDtmMmJKRSqTPeLVqPhwpfhmEgEAhgamoKiUQCFosFNptN2Ueh40HHXQouucQkfZz0wt1mQjB9hvw5NjaGa6+9FqFQKGsupA3yvQKARCKBvr4+rF+/Pmvdp1IpBAIBFBcXY2ho6AxhSa4rXfwym2+GYRiGYRiGYS4+b5toQ3mr/8NvtVpRVVWFlpYWvPDCCzmv051JM/HCTGA5V8FGj4Iw+/ZdXkcdSXmd3Lb3XJ97IdAjJHRyOd+5vv2nOw1RB9Rut5tGLsmCq2ZQMWGhSFto3+ROPTTKRzq2ZmKVmdj0VllIG9FoFOPj40ilUigtLcXY2FiWECjbkwINAGzcuBE33XQTVq5cicrKSthsNvT29uKJJ57Aww8/jJmZGVxxxRWoq6vD6tWr8eqrr54ROUWha1WPBJH3yAgNGk0iozzMxrexsRFVVVXw+XwIBALqGpvNhqamJmzevBnl5eUYGRlBKBRCMpmEy+XCn/zJn+D555/H0aNHEY1Gs8ZUj1qh80/HTBcZzNYxAHR3dyOZTJ4R7UPFrVziqlm71CaZ4iTbl4KQLiBK8YW2OTc3p+Y6FAqhtLQUU1NTcDgcSCQSSmgys2FmZgYOhwMOh0PtciVFG6fTqeaNzhkVCs0EKjn3DMMwDMMwDMO8Myz4/8gXKjbMF11yrs+yWq1YtGgR1q1bh23btpkKM7miVMx+p+S63+v14oMf/CAeffRRRKPRMwQaM8dQd8rm5uZgsVjQ0NCAqqoq7Nq1SzmDtbW1iMfjCAaDCIVCZ7RtFiGiP5NGlcw3hmZ9ptEc9Do9KocKT2aOv546oqepyGtoVAC16WzCzbmIbNRZ1+t05Lo/V38vtJCWy155PBwOw2azwev1YmJiQtlEf9I5OnXqFILBILZt24bKykrU1tYiEolg586dSgDx+XwoLS1FQ0MDdu3ahUQikXOt0ogKHbOIKDlfcm2biRdDQ0Nwu91wu92YmZlRzv9VV12FDRs24L//+79x6tQpjIyMqEiXeDyOxx57DDMzM2fMny5i6fNJ16VZ6h4VpOS1o6Oj8Hq9KC4uht/vV2vULJJMbytXFJT+TpltPS+PmwlRsn05rqlUCg6HAwUFBfB4PJiZmYHNZjtjLOS1s7OzKC0tzYqYkWlsMl0uHo+fcW+uujXnsiMcwzAMwzAMwzBvLwsWbRbq1J7L9bmcZXlsbm4ObrcbS5cuhc1mU06o7uzkSjvQrzFzzCgWiwUulwttbW34zW9+g9HRUXVcdyJlu7qgIb/ptlgsKC8vx/Lly3H06FEEAgEYhoG2tjb4fD50d3cjEonkHDPar3ONDDEbi1wOokSm2ixevBhHjhyZV/Ayi16QfdWvk04+vcdMQJhvncixNEtFo7sX6VFFuRxRs1on1K4LSa7nSNtisZgSNXQhzsyZj0Qi6O/vh81mw/T0NAzDQH19PQKBgBqbqakpBINBLF68WN1HnXSdXNEo1Aa6vnO9A/Ln0NAQnE4nnE4nAMDpdGL58uVYsWIFDhw4gNOnT2NqakqlQQHpuevv7weAMyJ/zia46qJbrjVOxyIUCqGpqQnT09Po7+/PWeh3IeKebqd8lv5u0GfYbDY1VnNzc0rANQwDiURCCcYejwfBYDBrLmW7spZWMplUdaWsVivsdjvy8/MxNzcHp9MJu92OkpISRKNRzM7OIpFIwOPxYM2aNaioqEBeXh4CgQD6+vrQ2dmJeDzOW34zDMMwDMMwzDvM/4jYd+mk2O12JdqYXSOdIbvdDqfTiWQyqQQRs6iSs0Wr2Gw2VFZWYmJiIivdiaI7lbrDKISA0+lEQUEBioqKVORBVVUVUqkURkZGsmzS29YFl1wCznxjR9vL9SyLxYKCggKsW7cOJ0+eVGkTtA3dgdft0evcUNFG71euvlKsVitaWlpQWlqKrq4uDA0NAUjPb0VFBcbGxrLu1aNGzme8LiZCCFWrRa+RIs/rYzU3N6cc+UAggFAopLauBtL9nJ2dRSQSgdvtViIlFR9oCpBeb0WI9I5WFRUVGB8fRzQaPSNSRBczdHvHxsbgdrvhcrngdrtRXV2N1tZWTExM4NChQ4hGo2cIcGZiLJ3HXGtoPvHETLCR/U0kEqisrMTg4KCKfCkqKsKSJUswMzODsbExRCKRrD7PJ2BIAYUKzWYipvxptVpRXFwMt9uNgoICOBwOzM3NYXZ2FkNDQ+r9C4fDAAC3263aMQzDtI4PFZ6cTifKy8vR0NCgPjtXr16N5uZmJJNJ9PT0oK+vD5dddhmKioqQn58Pt9uNJUuWoLq6GvF4HF1dXZfke8MwDMMwDMMw7ybeNtHmbNET53q9dDij0SgmJyfhdDqzIlPMnEa3242qqiqEw2EMDw+rWjK50mqosyOd/1QqhcHBQVRXV+PkyZOm4oe0T9ohnWCr1YpEIqHai0ajCIVCKCoqwtDQEIQQiMVicDqdWc6Y3m+9fzLV4Xyg6U26wyzEm+lL1dXVKCwsxOTk5BnPN4vUMPvmX/5No5rM5omiO6BCCJSVleHmm29GW1sbHn30UZVOU1NTg+uvvx6/+tWvEAwG1XPMHHsaIaHvZvROQQUJuQORXDfAm065HjUij8l/0WgUwWBQbRsuHXm5g5aci7y8PJSWlsLpdCIUCmFmZgahUEg9V74fQqR3fqqvr8fatWtx/PhxnD59OktkkehFpWWfrFYrZmZmUFpaioKCAlRWVmLt2rUoKSnBr3/9a0QikSwxxmKxwO12w+PxYGJiIqv+i1yXdN2bzZ9ui460Sy9MXVxcjPz8fBXtsmHDBrS1tWF8fByHDx/GyMgIZmdnVVFxwzBQXFyMcDiMSCSibHM6nUpwou8IrW8jj8nrS0tLsXjxYni9XhQWFqpIq3g8jh07dmBiYgI2mw3hcBgWiwUlJSUYGBhQ0S+09o2sXSPHsqqqCh6PB7W1tWhsbFRpVu9///tRX18Pr9eL06dP46WXXsJ73vMePPXUUxgcHITT6cQdd9yBtrY2DA0NYWBg4AxxlmEYhmEYhmGYi8vbtuX3Qv4nfz6HS55LJpMIBoMoKSkx3ZKXXp9IJFBTU4OlS5eiqKjINFrBLPKGtpdIJDAxMaG20DWLLqB/y2/PJbJQq91uV2kOJSUlSmCYnJyEYRhwuVymbUtHjLZNowXoDkK50kekjbSQrC5gyGemUilEo1FMTU2htrb2jGghSSqVyorCociIBboFshQS9G2ZdQGFRslYLBY4HA7cdtttiEQiyMvLg9PphMViQVlZGT72sY/h05/+NOrq6uByuc4QAeTvuRx8MyFJHzPdXjrfOvp1sk6LzWaD3W6Hw+FQ80Rrn8hrHQ6H6of8p88Z3V6apiglEomsrZ9lv+X1Uoz46le/in/+53/G5z73OVx99dUqdYn21+VyoaGhAffffz9cLhc+/elPY/ny5UpYpHbp/aZ9C4VC8Hg8WLx4MVpbW7FmzRo88cQTqogunXubzYbVq1fj9ttvh9vtPqOItLxOClN0G3PDMLL6TW2RYondbkdhYaESR2QUHhVYXS4X6uvr8cUvfhFHjx7FihUr8MADD+BTn/oUbrjhBtTV1cFisaCurg5f+cpXcOONN6K0tBQOhwP5+fm48sor0dDQoNao1WqFzWYzjfgqKChAa2srPvvZz2LdunVq3BYtWgSv14twOIybbrpJ2T49PQ2Xy4Xm5maUl5erNUHXp3zHm5ubsW7dOnzxi1/EV7/6VbS1teHAgQMQQmDJkiUoKirCf/7nf6K9vR1NTU24/vrr8cQTT+Dw4cOYnJyEy+XC0qVLUVFRgf7+ftjtdtNaRwzDMAzDMAzDXDwueKQNddzP9g2tPK870dShlQ5YNBrFxMQEqqurVfqAHlkh24rFYtizZw/y8/OVIyWhEQN6sVHqrMs6KkVFRVmpG9Q2fdtdKhrIv1OpFCKRCKLRKIqLi9W5qakpVFdXq2/6AajdZmg6ihRIcokQEioEyGfLMaG77OiCEyUWi6Gvrw+VlZU4fvx4VgFbeg91FmlEDd0dS58XORbzRe7Q7abz8vLQ0NCAnTt3Yu3atUgmk6ooq0zfoHVgZD8BqPoeegSGHrGRi6VLl2Lp0qXIy8tDR0cHOjs7Te+Ru5pdfvnlGB0dRVdXF6amplBWVoYbbrgBbW1t8Hg8KCwsxJ49e/Dcc8+hp6cHVqsVDodD2Wa325GXl6dso8Vi6bhIsUOmU0nnvaSkBKlUKivNStYoqq2txSc/+Ul8+9vfxujoKG6//XbccMMN8Pl8OHjwYNZ2z5dddhnWrVuHI0eO4LXXXsMnPvEJlJSUwOl0IhwOZ0WsURGRikhCCASDQVRVVWHt2rXo7OzE1q1bMTk5eUY0kOy7w+HA1VdfjWeeeQbRaFQJTlL8aG1txec+9znE43E899xz6rrq6mrceeedeOaZZzA8PIx4PJ61npYsWYK7774bS5YsgcfjgcViQUdHB77//e+jtrYWJSUlcLvdKCsrQ0tLC5588kkcOnQImzZtgtfrhcvlUkLWT37yE3zmM59BRUUF7rjjDrhcLuzZswe33XYb7rnnHhQUFOALX/gCDh06hHA4nLVWUqkUbDYbXC4X1q1bhy1btuDHP/4xBgcHEY/H4XA4sHr1ahQVFaGvrw9XXXUV1q5di/vvvx8bNmyAw+HA/v37cejQIfh8PrUDlIxKkuu6o6MDJ0+exM9+9jMcOXIEk5OTKC4uRiwWQ0tLC7Zt2wafz4c9e/YoAfT06dOwWCxYunQp/uqv/gqhUAiPPfYYRkdHcxYoZhiGYRiGYRjm4rFg0cbj8SAej8NqtaK2thabNm3CwYMHceLECSXWSM4WWq+flw64dABpusHc3BySySTKysrOSBuhbUknMh6Pq+KsEunMUqGBpgbpTsrc3BwWLVqUtWMLTTOi0TU0bYU6sFarFaFQCIFAAIsXL1b2BYNB5OXlobi4WF0vnS8qijidTni9XrS0tKC9vR2RSOQMsUpudU3HgopmVFyh0Qj0b5qGdsMNN+DVV19VKV4SmWYh7aTROtJus6K30lGnYy/vk7bTY/n5+WhtbUVnZyeuvPJK9PT0YHx8HPn5+SgvL1fRB7Qfcj5TqZR6nlkNEnm9x+PB8uXL0dTUhMnJSezYsQPJZBK33XYbbr75Ztjtdvh8Ptx777344he/iImJiaxaMFarFWVlZbj//vvR0dGBj3/849ixYwf279+PtWvX4o/+6I/wL//yL+js7ITb7cby5cuxYcMG+Hw+JcpIgc/pdCI/P1+JGbqYCUA56HK85FqXxWXlvXJOpK2NjY145ZVXMD4+jnA4jJ07d2J2dha33HILjh07psbc6/Wivr4eJSUlOH78OL785S/jmWeeQU9PT1ZdG7mu6E5LdEyEEAiFQli8eDHcbrcqbCu3B5fCEn1PT5w4Aa/XmxWNJCNqmpqa8NGPfhSPPPIIli5dirKyMmzYsAFvvPEGfu/3fg9jY2NYsWIFEomESj0UQqCgoACf+cxnsGPHDjzzzDNIpVIoKSlBXl4e3G433vOe96C8vFzt3LVs2TK88cYbqK6uRmNjI0ZHRzEyMgKr1YoNGzbg1VdfRVNTEx566CHceuutWLZsGWZmZnD77bfjS1/6Eu677z6sXr0aPp8PPT09We+GFGVLS0uRl5eH7u5u9Pb2qrlOJBI4evSoqnNTV1eHcDiMhx56CL/+9a+xZs0aWK1WDA8PZ403FZqBtOgajUYxMDCAiYkJxGIxxONxRKNRLFq0CCMjIwiHw6o2WDweh8vlwn333YctW7bg1Vdfxa5du3D69Gkkk0nE4/HzTsdkGIZhGIZhGObCsCDRRgiB6667DlNTUyguLkZDQwPKy8txzTXX4Etf+hJmZmayBBAgO5qltrYWZWVlSKVS6OrqQjQaPSPShjrgtDirTAVZtGiRuhYAKisrsXTpUhQWFuKFF15Qz5Yij3QCqaNIo1BkO1arVRULltfL1BM9jUpGOQBQER2yPSlc0B1wZIRJfn6+Ei4AqPSZ8vJyvO9970NTUxO++93vIhAIqPYLCgrwgQ98QPXjwIEDatcgh8OBsrIyhMNh9e2+dNj1aBj9G3NdSJP3RSIRlJWVZfWbzj91RvUIKRrtQ8UgmuaiP1uPdrJYLPB6vbj66quxf/9+fPCDH8RPfvITtY11U1MTAoEArFYrgsGg6f10nqi4JyMePB4PrrzyShQXF8PlcmHTpk3o6elBRUUFrrvuOrzxxhsYGBiA3W5Hb2+vKgZL14/X68WHPvQhHD9+HHNzc8jLy4PVaoXf78epU6fQ39+PiooK7N+/H2NjY5iamlJjoYtX0m757lBxSB8vfexoyhRNFUokEkgkEqivr0d3dzdisRiSySTGx8cxOTmJa665Bnl5eUpA2rx5My677DJUVFSgrq4Or7/+uhJ74vG4slNGeVA76RjL9CgZ+STr69B1KcdR2h2NRpFIJFBWVoaZmRklTLpcLlx//fUYGxtDZ2cnRkdHsXjxYlWLprq6Gi+++CK2bNmCZDKp6vXYbDasWbMGqVQKw8PDGB0dRSqVUmKfbLO2tlZFObndbszOziIQCODBBx9Ea2srotEoAKClpQXXX389Tp06hcHBQfh8PpSVlaGtrQ0DAwPo6+vDCy+8gObmZuTn52d9dlFxRRYenpycVOKifIfkTk5NTU0IhUIYGxtDIpFAIBBAQ0MDKioqsurZ0PUNQI1zJBJBLBbLmqOZmRmUlZUpwVXicDiwdOlSGIaBBx98EL29vRgfH0ckEsmK4mEYhmEYhmEY5p1jwZE2hYWFWLJkCWpqauBwOLBv3z7U19ejtbUV7e3tZ2zjK52KiooKrF69GosWLUIsFsOSJUvwwgsvnOHcAMhyEIE3xYJoNIrS0lIlYNTX12Pz5s2oq6vDgQMH4HA4kEql0NTUhHg8jqmpKczOzqrjhYWFaGpqQmVlJaLRKLq7u9Hd3Q2r1YrNmzejsrISLpdLfdPvcDiUo0MFCdovPUpIHqO1WeQuOh6PB06nE7W1tVi6dClKS0sBAPX19ViyZAkKCgpwxRVXYPfu3WqXKbfbjcbGRjz//PMIBoNKeCguLkZLSwtaWloQi8Vw9OhRGIaB0dFRjI2NZaVE0Toiuv15eXnK4UulUgiHwygsLITFkt5muKysDEIITExMnLE1uZkQpAtEeqSPTCmqrKyE3+9HX19flsBlt9tRXFyMFStWoKurC4lEAj6fD9FoFPX19VixYgUsFgsGBgbU7j60fT2aSLdTCIF169ahrKxM7ULW0NCAtWvXYt26daoIrcPhwOWXX45oNJqVagUALpcLVVVVaGhoQG9vLzZv3oyTJ0+ip6cHkUgEg4ODePbZZ7F69Wps3rwZ+/fvV446FSmlwCGL2upjqY+12dhKQYSKpDLqJpVKoaGhAQcPHlTPjkajmJmZQSAQQElJCcbHx1FQUIC2tjZUVFRgZGQEY2Nj2Lt3LwYGBs6oq6QLdFSMozb5/X40NTWhoaEBNTU16OvrU+stkUhkpdIlk0kMDg5i0aJFGBoaUkWShRCoq6tDe3s7AoEA4vE48vLy4PV6UVNTg1QqhampKRQUFGDz5s0wDAM7duyAzWZDYWEhEokEQqGQmudEIoHS0lK0tbVh7969qK2thdPpVGlSUvjp7+/H+vXrMTc3h9HRUfj9ftxwww34xS9+gXA4jKGhIVRXV2PlypU4ePAgkskkRkZG4PV61bNoBOHc3JyqOyRTrgBkjWdJSQlaWlqwfv16vPzyy2pNBINBJBKJLMFFT02UYyUFGvo5lEgkMDY2hpKSEng8HthsNiWWV1VVob6+Hh0dHeju7lbimUytq6+vx+uvvw6GYRiGYRiGYd45FizaTE5Ooq2tDUuWLEFXVxf27dun0qSOHj2q0jXcbjdmZmYwOjoKIQSam5vh9XqV475x40a0t7cjGAwCgKphIQuYJhIJ9Pf3qxSdubk5hMNh5OXlwW63o6amBps2bcLq1auRSCRUrRAAWLZsGaxWKzo7O9HZ2QmXy4Xi4mKsXLkS9fX1KC8vhxAClZWVGBkZQVNTEzZs2ACXy4VYLAa73Y7KykrVZz3tR48o0R1p6eQ6nU4sWbIEy5YtQ0tLCxobG5VzbLfbUVBQoEST4eFhnD59GldddRWOHz+OcDgMh8OB0tJSWCwWHD58GKlUCrFYDIWFhWhsbMTll1+OwsJC5OXloaamBpOTk2q77snJSSV8rV69GlNTU5iYmEAoFFJ219TUoLy8HKFQSJ1LJpMq8qCxsRH19fWw2+0YGhpCe3u7ij7QnfVcaXA0XcxisaC8vByrVq1Cc3MzBgcHEYvFMDw8rCJkXC4XSkpK4PV6sXr1anR0dCAQCMAwDJUmFovFsHfvXhUloqfU6dE3ejrdpk2b0N3dDb/fj+rqaoyOjuK6665DfX09vve972FsbAzr16/H+vXrEQgE4HK5VPSHYRgoLS1Fc3Mz3G43Vq1aBa/Xi1/+8pfo7OxEKpVCKBTCjh07sHjxYqxfvx4A0NHRAZ/PlzX+UrSRIkUuzMZWHpMpUbSejXyfEokEli1bhtnZ2azonWAwiMHBQZSWlmJiYgJer1dtR79nzx4cOXIE4XBYCRBm82m25uUcz83NYXx8HOXl5SguLsZVV10Fr9er6tUMDg5idnY2S7Q5cOCAqu8k+5pIJDAyMoITJ06oejUyYqm6uhrBYBBlZWWYnp7GqlWrlHgZCoUQDAZRVFSkPi+sVitKS0tVBM2pU6fQ0NCAWCyG/Px8JJNJeL1eAEBTUxOKi4sxOTkJn8+HkydPYtmyZejs7EQ8HsfAwADWrFmD+vp6dHV1AYDaIltGwclxkXMh08ZSqZQSbcPhsBJgq6ursXTpUthsNmzfvl2tNZniJER6Zy+5m5TZ5xAATE9Pq8LXsVgMsVgMIyMjMAxDjYMUwGOxGJqbm1VEVCwWU2JtcXExCgoKsH///pzrkmEYhmEYhmGYt58Fp0cNDg4iPz8f8XgcPT09iMfjaG9vxx//8R+juLgYRUVF2LBhA5qamtDT04OHH34YDocDzc3NOHLkCE6dOgWv14vy8nIsXrwYHR0dcDqdKsWkpaUF99xzD0KhEH74wx9idHRUOaahUAhOpxPl5eW48847UV5ejoMHD2Lnzp3w+/3qG+1UKoXVq1cDALq7u+FwOHDjjTfitttuw1NPPYX29na0trbiuuuuw8svv4z77rsPBw4cwGuvvaYcwY0bN+LTn/40uru7lWhDC+XK6Av6rTY9b7FYUFRUhI0bN+Laa6/FypUrVSHiXbt2Yd++fSgvL0dzczMikQieffZZhEIhbNmyBUVFRZiYmMCiRYvQ0tKSte2w1WrF4sWLsXz5csTjcfzgBz9AfX09vv71r2P37t2qZsXOnTuRSqVQVVWFz3/+8zh8+DC2bduGo0ePqh1ubr31VpSVlWFsbAyHDx/GiRMn4HA4YLPZUFtbi4985COqgGpRURG+8pWvoKurS6XL0HUhCzebRV7I4263Gxs3bkRDQwOAdB2V6667Dr/4xS/U9R6PR4l7GzduxL/+678iGAyq9JLm5maMj4/jN7/5TVatFRpVQ9OFaGqUdHRra2tx6NAhxONxJBIJPP744/jBD36A7du3o6+vTwlh4+PjKCoqQlVVVZYoWFtbiyuvvFLtzPSP//iPGBgYQCwWU6JGLBbDI488grvvvhs333wzmpubsWPHDhw/flylngBAOBxGKpVSuznRSBa9PpT8SdecjA6R19P6JjMzM/B6vZidnc2al2g0irGxMbWzWnFxMQYGBtDd3Y329nYl+NC5o+ub7pJGoz5ozZr+/n50dHQgPz8f733ve3HNNddgZmYG7e3tmJycxMzMjGormUzi+eefx+rVq7P6HA6H8bOf/QzT09MqLWh4eBizs7OoqqpCLBbD5s2bceLECZXaeNlll2H37t3o6elBdXU1mpqakEwm4Xa7sWbNGrS1teHf//3fEYlE8Oqrr6KkpASlpaXo6+vD+vXr0dPTgw9+8IMYGRnB8PAwRkZG8Nvf/hZTU1MYHh5Wfevr60NZWRmGhoZURMzg4GBWmiKt5SSEwMjICIaGhrB582Z86EMfQldXlxInLRYLjh07hp/97GeYmZlRkUgyZTEej8Pr9WJqaiprW3T5GSSjdnw+n9ohKxKJqEi1gwcPqjQ/t9uNgYEBHDt2DB/4wAdw991349ChQ5iamlLbw8/NzeHb3/72GdF0DMMwDMMwDMNcXBYcaeP3+2Gz2eD3+5UjOzo6qiI6NmzYgNnZWRw4cAB33303tm/fjrKyMrWDknT+ksmkiqLZtGkThBAoKytDa2srnn/+eWzYsAH3338/HnvsMQwODiKRSChB5XOf+xz8fj+eeOIJ9PX1qYgA6ajLlAlZmwUAtmzZgu3bt2PXrl3w+/0oLS2Fz+dDXV0dqqur8d3vflfVw3A6nSgqKkJ/f396kEialnyOFIek42RWm2RqagpPP/00duzYgVWrVuF973sfvvGNbyCRSCA/Px9A2rn2+/0qfSaRSKgtrquqqrB582YVnSG3cG5tbUVlZSUef/xxxONxnDp1Cg8//DAmJyfR2tqKxsZG7Nq1C263G1u2bMHU1BRaW1sxOTmJEydOAADWrl2LVatW4bHHHsP09DSCwWBWYdzrr78ee/fuxZEjR+ByuXDdddfhAx/4AP7t3/5NjYecSzmf0kZdRJFO/sqVK7FmzRp0d3fjpZdeQkNDA6699lq1BbMsxFpZWYnCwkKcPn0ap06dQjweh9PpRHFxMRwOB3784x9jZGRE2aCnpsljsm4I8GZtDjk/JSUlCIfDiEajygk/ceIEotEoVq5ciUQiga1bt6KpqQm33HILHnroISSTSbhcLixZsgTLly/Htm3bsHXrVgSDwTNqGwHp6Iuf//zn2L17N+6880488MADePzxx7F7924kEgk1bjKSQq/HQ5HCCN3tSUbN9Pf3Iz8/H1NTU2ptzs7OoqOjA3fccYd6lmw7FAqht7cXDodD/V1ZWamc+lgspt5NvU/SZrkdtBQnpL1SuPntb3+r1vPWrVuVmEMLaUvRCQDGx8fxyiuvnFHg2ufzKUFK9iGRSGD//v249tprcezYMRw/fhz9/f246aab8JGPfAT79+9HIBDAI488gg9/+MOoqalBOBzG8ePH8eCDD8Lv98NisWBwcFDVLqqrq8Pf/M3f4D/+4z8wMjKCrVu34uTJk0ilUpiYmFCpnLIPW7duxYsvvgifz6dqBdFxous+lUqpejT79u1DR0cHbrjhBjQ2NmJychKPP/44BgYG4Pf7kUgksmpAWSwWTE1Nqd3murq61FjS9Z1KpWC327F3714Eg0EVKWWxWOD3+/GFL3wBQDp6ae/evXA4HBBC4PXXX1cRYcXFxQgEAujq6sLx48dVhBvDMAzDMAzDMO8cCxZtZE2M6elpjI2NIZlMIhqNwuFw4A/+4A/w5JNPYt++faquxLJly1BQUKCiT4QQiEQi2LNnDxobGzEyMoJ4PI4rr7wSNTU1GBgYwGuvvYbu7m78xV/8BQoLC7N2GFq1ahV6e3vxox/9SH3rLJ0cuSX0wMCAKiIsow/6+vowNjaGaDSqUq3Gx8fVDlH5+fnKoZyZmUFvby/sdjve+973qnoUtL6HXiuGOrV2uz3L6ZZjRutryNo0brdb1dwB0kKP0+mE2+1WhYrLysrgcrkQDodRUVGhdrSSNW6Ki4uVc2e1WlFdXY3i4mIUFhZi+fLlePzxx3HbbbcpJ9Lj8eDee+/FSy+9hNLSUoyPjyMQCKhdZbxeL1pbW/H9738fw8PDcDgcaG9vx4c//GHY7XYVUSL7TMcGMK/HkpeXh49+9KPYsWMH9u3bpyKG7rvvPuzZswfHjh1TETwulwurVq3CP/3TP6kaGzLFLB6PY/v27cphpYV45bOksEFTo6gg4nA48P73vx+7du3Ctm3bVNHlvr4+XH/99aiqqkJ3dzc6OzsRjUbxta99DY8++ihCoZCqTRSLxfD0008jGo0iLy8P+fn5mJ2dRTAYVM/Mz89HXV0drrrqKqxfvx4ejwcrVqzA7t271dqZnZ1VOwnR8ZLjKsdZrikq2KRSKYyPj+OHP/whAoGAWn+ygO3+/fvxh3/4hwiFQlkRSIFAACdPnlTP8vl8OHDgADZv3oxvfvOb2LZtG/x+P1asWAGPx4Nt27bhxIkTWXV3ZL0UKZjRVJ1UKgW/3591TkaK0fspdCt2/ZwcG9r3gYEB/PVf/7WK1JmYmMDx48dRV1eHq6++Gq+88gra29tx+PBh2O12Vc9HrjEpPAJpIaOrqwt/+qd/iqKiIgQCAZXKRdcRFVMmJydVH+lalDWGpL0y+o/ubDY5OYmnnnoKdrv9DMFRjpM8nkql0NfXB4/Hg/r6erz88ssqLZBG8gBpkfDAgQNZcy1TDuPxuBo7+QxpU39/PwYHBzE3N6eiz+Q46VF1DMMwDMMwDMNcXBYs2tBoknA4rCIMpOM0MjKCmZkZuFwu9Pf3o6ioSBXgdTqd6v7S0lLcfvvtOH78OMbHx1FcXIyKigq89tpryumcnJw8w4GJx+NYsmQJiouLVUqUEAIejwdtbW3Yv38/EokEpqenEY/H4XA4MDs7iwcffBChUEilioRCIfh8PoyPj2PXrl2499578fjjj2NmZgbV1dVYsmQJXnvtNWzZsiVLNJLQ9BUzB1Q6VLLIL3XKpXMkBZ68vDw1tjLiBkhHPMzOzqK5uRkAVMqDw+FATU0NKisrEQqF8MADD+DgwYOIRCIIBAJIJpNYtWoVVq9ejUOHDmFiYkLVosnPz8fixYvR1NQEp9OJyspKdHR04KWXXsJrr70Gi8WCmpoavPzyy2p+AShhjqbDyCgMGnGki1hSrGtsbMTs7CyGh4cRDoexZMkSXH755di5cyeuuOIKnD59GpFIRG1jPTQ0pGrECCFQX1+PyspKjI2NZRWv1lOx9Pov0i7p3FutVjz55JMoLCzEyMgIgsEg7HY7jh49igceeACnTp3Ciy++iKNHjyIajcLn8yESiaC1tRVHjhxBNBpFMBiEw+HARz7yERWtMz09rdL+LBYLYrGYigpJJpP4+c9/jr6+PgwODmbtLub3+9UuWFQk0NeV7KsUyGTfYrGYKjwt54JGeMidgGibenHhVCqF7du3o7u7G8uXL8fll1+OeDyO7u5uvPLKKxgeHs4S5aStdA6osEF3iaKijbyXik6yH7m2lqZinG47FXpSqRROnz6N/fv345577lFb1tO+UyFD1nGRawOAigakkUO0v3a7XUXC0J2zqFAj29Z3dqLvgzwv50qvD0TfIyGEEptdLlfW+6f/DiBLnJHPo8Xh5ZxLcU/WspGioNx1jKZ65qpXxTAMwzAMwzDM28+CRRsAWf/DLx2mrq4uFXUjz09MTCjnrqWlBW63G4ZhwOPxYP369bDb7aioqMDExIRKyZApVIlEAlNTU+qbXtnm5OQkOjs78fu///s4ffo0fD4f4vE4CgoKMDg4iHg8jlQqhRMnTqiIEPltMk2xkOkMs7OzePHFF3HXXXepWjp+vx8dHR0YHBxU6WDS8ZROKnU6JfQbdvqtdiKRQCQSgd/vV+dln9xuN7xer7o+EAiodmdnZzE0NITly5crRy4Wi6laIZ/61KfgdDoxOTmJrq4uBINB+Hw+NDQ04I477sDixYvxjW98A5FIBNPT0wiHw6rQb21trRJr2trasHHjRpw6dQrRaBQ2mw3T09MqkgKA2o1LOogyiokKVHQ3HD2CQKaChMNhLFq0CM3NzZiensbevXtx8803w+12IxgMoqSkBJWVlRgfH1f1Qerq6uByuTA9PQ2LxYKmpqas2jB6tA11qOXz5Zgmk0kcO3YMTqczy0F95pln0NfXpyJs/H4/UqkUotEojh49iquuugq9vb2YmJjAyZMn8cQTTyhh0u/3IxwOw+PxqGK6MgJMFo8OBALw+/2IRCJZwieNajBbR/Sdo3VldJGA/k3XmDymp1tRwQMAAoEAYrEYpqen0dvbi7m5OUxMTGB4eFgJjFQIoKKdhEY06but0T7IftB+zidUmYkTcs6puOD3+zE0NITKyko4HA5ltw4VyCi6QET/yfdfv0/+TXdwomMk29KFRilqyXeHjqEetSZFXrNIHApNI9ORgp/et2QyCZvNllXDiD6X1i9iGIZhGIZhGObis2DRRv7PvPymGkg7Lu3t7bDb7Sr6RW77a7FYEAwGcc0116C+vl6l+NTU1ODkyZPIy8tTKQMlJSVZYofc5lc+U+409NJLL+HKK69Uu0LJaAdZ+8ZiseDUqVPKKZZOj0TaFIvFkEwm0dnZiQMHDsDr9ar6FDI1ZufOnVn3Uscml/OkR3zI3Vr0nWV8Ph/6+/sxOzurrjtx4gQmJycRj8fh9/vVjlzSyU0mkyq1ZfXq1cjLy8Nrr72m0swGBgbQ29uLa6+9FiMjI2rnmIGBARXRMTMzg9dffx379u3DiRMnUFRUhOLiYqxduxaHDh2Cz+fD6OioSuWi9VOo/XqUhR5hQx3toqIilJWVYfny5bBaraioqMDhw4fR29uLj33sY3C5XLDZbIhEIvD5fKivr0dxcTEmJibg8Xjg8/mwf/9+NDQ04Oqrr8bs7Cw6OzvPsIUKNtQ26uz7/X5YrVZlYyqVwsmTJzE2Nga/36+ER+DNnY0+8YlP4LnnnsPU1BR8Ph92796N8vJyDA0NIRwOq7Ugt4uXDjxNn6ECl7TX6XTC4XAowU72h0LXkpnQYXYPTaWSf8u29HUq25fvkc/nUyJpKpXK2jmN2qSjrwfdXjpHNJKE9kWP6jB7pi6a0oiWSCSCWCwGm82mBFwqxuhCCRWSqA30nHwmTY+i46GnUeUS4MwEHPo78GYkDq0lpBeGNptvfQ5kP8xS0vRoOH0caTt0DBiGYRiGYRiGuficl2gzOzurHFWZ7tDe3g6LxZKVXhAMBmG1WhEIBJBIJLBixQqUl5cjLy8PExMT2LlzJ4C0s9XT06NSrqQ4MTAwoBxoIQTi8TjeeOMNnDhxAp2dnaioqIDD4UAoFEJ/f796thBC7fQCIOvbf5rOJa9NJBL4zW9+k5VWIoutbt26VUVdAMjqM3UiZfvyb90hls4wdd7eeOMN+P1+tXuWYRg4ePCgslv29+TJk1nbL/t8PkxMTGD37t1KIJP3Dw4Owm63o7CwEHv27FGiVWdnp4pk6uvrw49+9CMMDAwgmUxi+/btuPrqq3H99dfj6NGjeOWVV9Db26vEKumwTk1NZTl8si8y3cIsckGOy+TkJK655ho1X6Ojozh48CAAwOVyqa2ZBwcHsX37dlRVVWH58uWYnZ3FzMwMRkZGcOrUKQwPD+OWW25BZWWl2m5Zj+rJJdhQu/QoDSGEmh+KjCKrqalBQUGBEgMCgYBa1/r1AFSdHBn9oNffkWuloKAAFRUVEELg0KFDWWl3uoigCyE0BYfaTUVFXXTJtU7pvXRN0bap0ECLUevP1W3XhRo6D3qfzNLb5Hmr1aoKIEsxVwoTVqtV7SB1+vTpLNv1YtT0+dQOXeDSr6PRTvKfXPvyd104NJu/XMIiFaOocETHQrfZbO70Z8rPK10wlH2WQvd8a4lhGIZhGIZhmHeGBW/5PTc3h0OHDmFmZiZrV5pIJALgzf/hl9vgzs7Owul04pFHHsGNN96I+vp6dHV1YevWrZidnVUOd3t7Ow4cOJBVL+Lll1/OSkmamZnB008/rZyQrq4uJbDQb62lE0Lttlqtqqit7Id0PmlUBXWKkskkRkZGsr5hl/bSFBBdxJCpD9IBk2kntJhrKpXC8PAwRkdHsxwpGb1EHchIJHKGgyajiGhEh2EYqqhqZ2dn1rmBgQFlt6z/Ift54sQJ1NTUoKWlBS0tLfjWt76V1R9Zr2j37t3KNj2NggpZVESR47F9+3bYbDa1vfTx48fVmunt7YXL5YLdbkckEkFPTw9++tOf4o477sDw8DB6enoQi8VgsVgwPj6OPXv2qHo7Zs/SoxvoutRFE7M0FdqmxWJBWVkZJiYmsvpInV5ZR0Q+hzq9VNCiDrS8xm63o6qqCnl5eXjjjTdMhQx5vUy7oufoeTP7ZZ9pUWPdsdfT/HQxgdpP661Qh16PGDET9eYTAOQc6IINtUOOVWFhIbq7u9X6t1qtqnj2mjVr8Otf/1qJOtJeM3GRjhkVXnToe6OPofwM0VPIzOZPF1ponSs6vvqaoWNxNtsTiYTaFYxeS+eHrg/5mUej+eja0EVMhmEYhmEYhmEuLmIh/1NutVoNj8eTlVpChQqaLqR/m0vTRqQDZLValcNLnX2zNBLqANEIA+lo6NvkUgdW2ivRHWvpENP25DHq8Ei75POosEKdHen4yegAKgDIn/J3OS7yerNvygGoIqh03KWjJtNB6Njr7dDxB6AiROR1FRUV2LRpE+666y588pOfVJEWeuFX6WjqDid1/KlTKPtmt9vVLj6yPTnmV1xxBfr7+zE5OamKplJhSB9jOfa0Zonss1xXcv3oAoZsl64JOS56pIXT6UR5eTnuuusuPP3006r2klwPNL3KLNKIrjlaD4Uel++GEEIVi9YLEstnycgnPVpGF6pocV05N7TekLxXRp3pYg8VZXTxhq5hOq5UcKTPlu3S6+RP+TuNUJnvHqvVipqaGlx22WVobGxU9aeWL1+OiooKDA8P4+WXX0Z/f39WOpqcTyoeyT7Rd5GuYRqFJdcTjZrS+6HvfmW2FvR1qPdTHzM96ojOhS7A6MIybUPvq/45IT/n9Jo48tkzMzMs3jAMwzAMwzDM289+wzA26AcXLNrIQqvUYaUijIQ65tJJphEqUgCggoZsk26LrYsX+u451EmSzpMeKUGdWt0Rkn/Lb6n1SBrqlOvObK6xo4KD7ihJsUAXbaQzSKFOthwzHSoC6GMld0uyWq1obm6GzWaDz+dTO2vJtCzpjDc0NODLX/4yvvOd76hCv7qAQOuE0KgDOYb6/Mv5ktsb6wKaEAJ2u/0M4UOOCf2dpu5I5DG9rghFPleOBxXW5PhardasXcLkuLrdbjidTpUOKMUsuaZlu2aCpR4xQdul86QLUvQc7YO+tuU1dMt4OZeyf9Qu/X5ZhFYXoagAI59NRVI9okbeq78/Zn2Q99CIJykU0bWjP1ces9ls8Hg88Hq9aG5uhsfjQSgUwsjICPx+vyr8LdcpFSzoGpBjTYUq2h/6/sr75ecSnWOZammWSkbbkGNH+2i329XvegQXfbYuaOtjKp+li4J0/FKpVJawq88dnVc6b3Nzc2rHPYZhGIZhGIZh3lZMRZsFp0fRb2N1h4o6iPo3wACyHDSaXkBTLujv8rxEd3p08YSKC7oTRcUJGglAo1J0O2l0Cf1mWq8zQSMS6LPpGFHnkT5Lj8Cg9+h91aNGzCIh9KgIeczn82HFihW49tprcfjwYZw6dSor2kYWju7o6MDKlStx4sSJM6KYzJxuOV76mqDjSx14Gf1AhSu5o5Leb3m9XjfGLBVJilp6hJG8jjroehSPvF5/ztxcuoA0ja6R99lsNtUXOiZmfdefpUdj0LGjjjl1oqmoQuedvmN0fqhdcr3TtaE75mYpPFSM0+dSn1c9ekVPidLXBv2M0AUTXRSh5+bm5lRqXDgchsPhyNqpS+8bTaGjEXO0vzq6IEmFG12Ao/Olv9v62Ol9NxOqqI36nNJ29M9eSa70LinY0HuoQKffR/vHMAzDMAzDMMw7x4ILEedy1Oh5+TOXqACYO7XUWdNrMOhOC3VM9W+OdWdIOrxm4g51HvVvxHXxSP8GXBd7aB9oe/q32/R6+kwaWUKfRdugjqOe6kDHVDrN8prZ2VkMDg7C5XJh7dq18Hq9OHTokErJcbvdKCsrQ35+vtoBjNqrp9iYObz0HF0DdOwl9LiZYGMmLtC5ppFPuhNKr9OPUeedPksXDOW1et0c3RZduNLXu74NullEjm6zbj/9qTvTtC36t4Q643r7uhiY6z2m4yTHXn9n6HPNxkpPp6T90J+ttyHHkdqRSCQQDAazxAg6PvpY6Z8pudaNvh7pGjMTPcyEOtoHXeClv9PzZqKW2ftktn7Nnk2v12se6X3N1TfdZoZhGIZhGIZhLj4LFm2AbEHG7NtgiZlTT49RZ0x3sMz+lvfI66nYk+s+aoeZyGHmxNHn6Pbp/TJzoOXv9KfELMVJ/9Y/l8NsZqtZPRA6R/J8MpnE8PAwkskkSktL0dzcjFQqhYmJCbV1enV1NYLBIDo7O88oCkv7rTu7ueZKHzezPsk+6CKRjDbQHVV5XO/vuQofZms2l1Or/02fpafuma0BfWzoO2MW7UPbMmtbQtPLzMaY2k7T6szGSP+bXpNLbJP2m61T/f3Sj5v118wOs7nTRTK6LszeSTPbqVBi9jlhNt7zCW36XOrzptug99lsDGib9Jxel4veo68tOmZm45tLiNIFIjP7GYZhGIZhGIa5eCxYtNGda4lZWpP+Ta/uNFKnnDoNZnVy5HlZayOXQEDtocTjcbVdsLxPOv/67lP6t8+6gGGWWkNtoN/S03GRIgoVIqijRe2ixWKp80Wji2haEk3/kvfp0Upzc3MYGRnBs88+iyuuuAI33ngjhoaGUFlZieLiYvj9fjz//PPo7e01jWLRI4uoiKJfQ8dGHzuz+TdL7ZLjQvsi7zNLlaPottOIJrO1Mzc3p2qy0PupQy6PybGRO+7I+aLzLvtGd3vSBUd93vU0OzOHWRcPaJtS/JLXyHGlRXR1QUjuSKWvYWqX2bzQuaXt0ggo2RYdF3pMnwe6VqmNUnikNsr0On2nOP06s/EyEzj1/un/5HFav8asiDOdN2pHroicXOmaZtF29B2ga0W3mT6H3kc/HyS0FpJuu9n7zDAMwzAMwzDMxeW8ChHrzpZ0amQhV722iNyCVk8B0p09eZxui00L+epOV650K1lcVXcSpX3yJ91OHMguGixtNwwjq8Cq3m8zR40e14vu0sLD0lmWKR50Ny1aSFY63E6n84w0L8MwEI/Hs76hN6utQc9JQSE/Px8FBQWw2+2IRqOYmZlRIoys4yLHmc4JdQapU69HzMg+0f5LG+ar9aGTSCTOENHoM2g/9cgL3anVizZL9HVG7dGjYuQxOhZUGKJbx+sFfeWzzEQbXVjRxycXekSMPqbypy580V3g9PQ/eQ21RYoJtMC1WZoaXbu6DZJcqW+6aKO/T3S86c5retSdtN1MYAGQtcU1/VwxEytkkWop2FCBhNqnC2/0PdTty9Vns/pW9B79OB1H2RbdpY0e1+dAzrEsEq6LbXJ+uRAxwzAMwzAMw1wU3vruUUKIcQB9F9IqhmEYhmEYhmEYhmGYdzl1hmGU6wcXJNowDMMwDMMwDMMwDMMwFwfL2S9hGIZhGIZhGIZhGIZhLjYs2jAMwzAMwzAMwzAMw1yCsGjDMAzDMAzDMAzDMAxzCcKiDcMwDMMwDMMwDMMwzCUIizYMwzAMwzAMwzAMwzCXICzaMAzDMAzDMAzDMAzDXIKwaMMwDMMwDMMwDMMwDHMJwqINwzAMwzAMwzAMwzDMJQiLNgzDMAzDMAzDMAzDMJcg/z/An6hiiw6yywAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -211,7 +211,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -221,7 +221,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -231,7 +231,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -254,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -273,7 +273,7 @@
        "torch.Size([97])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -312,7 +312,7 @@
        "140"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -323,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
@@ -346,7 +346,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2880x2880 with 50 Axes>"
       ]
@@ -370,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -379,7 +379,7 @@
        "torch.Size([190, 1, 28, 5])"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -396,7 +396,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fddb02b4f90>"
+       "<matplotlib.image.AxesImage at 0x7fabf098e9d0>"
       ]
      },
      "execution_count": 16,
@@ -405,7 +405,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -434,47 +434,48 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "__init__() missing 1 required positional argument: 'pad_token'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-388038927ee3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtarget_transform\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCompose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAddTokens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"<sos>\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meos_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"<eos>\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'pad_token'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "target_transform = Compose([torch.tensor, AddTokens(init_token=\"<sos>\", eos_token=\"<eos>\")])"
+    "target_transform = Compose([torch.tensor, AddTokens(init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\")])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IAM Lines Dataset\n",
+      "Number classes: 82\n",
+      "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_', 80: '<sos>', 81: '<eos>'}\n",
+      "Data: (7101, 28, 952)\n",
+      "Targets: (7101, 97)\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "dataset = IamLinesDataset(train=True, init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\", target_transform=target_transform)\n",
+    "dataset = IamLinesDataset(train=True, init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\")\n",
     "dataset.load_or_generate_data()\n",
     "print(dataset)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 342,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data, target = dataset[0]\n",
+    "data, target = dataset[181]\n",
     "sentence = convert_y_label_to_string(target, dataset) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 343,
    "metadata": {},
    "outputs": [
     {
@@ -483,15 +484,15 @@
        "([], [])"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 343,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 1440x1440 with 1 Axes>"
+       "<Figure size 5760x1440 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -499,7 +500,7 @@
     }
    ],
    "source": [
-    "plt.figure(figsize=(20, 20))\n",
+    "plt.figure(figsize=(80, 20))\n",
     "plt.title(sentence)\n",
     "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
     "plt.xticks([])\n",
@@ -508,1077 +509,187 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.networks import VisionTransformer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tt = Transformer(3, 3, 512, 8, 2048, 0.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vt = VisionTransformer(6, 6, 256, 82, 8, 118, 512, 256, 0.1, 79, (28, 16), (1, 8), \"gelu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchsummary import summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 344,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "VisionTransformer(\n",
-       "  (slidning_window): Sequential(\n",
-       "    (0): Unfold(kernel_size=(28, 16), dilation=1, padding=0, stride=(1, 8))\n",
-       "    (1): Rearrange('b (c h w) t -> b t (c h w)', h=28, w=16, c=1)\n",
-       "  )\n",
-       "  (character_embedding): Embedding(82, 256)\n",
-       "  (position_encoding): PositionalEncoding(\n",
-       "    (dropout): Dropout(p=0.1, inplace=False)\n",
-       "  )\n",
-       "  (linear_projection): Linear(in_features=448, out_features=256, bias=True)\n",
-       "  (transformer): Transformer(\n",
-       "    (encoder): Encoder(\n",
-       "      (layers): ModuleList(\n",
-       "        (0): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (1): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (3): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (4): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (5): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (decoder): Decoder(\n",
-       "      (layers): ModuleList(\n",
-       "        (0): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (1): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (3): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (4): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (5): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (head): Sequential(\n",
-       "    (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "    (1): Linear(in_features=256, out_features=256, bias=True)\n",
-       "    (2): GELU()\n",
-       "    (3): Dropout(p=0.1, inplace=False)\n",
-       "    (4): Linear(in_features=256, out_features=82, bias=True)\n",
-       "  )\n",
-       ")"
+       "tensor(0.0631)"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 344,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "vt"
+    "data.mean()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 345,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==========================================================================================\n",
-      "Layer (type:depth-idx)                   Output Shape              Param #\n",
-      "==========================================================================================\n",
-      "├─Sequential: 1-1                        [-1, 118, 448]            --\n",
-      "|    └─Unfold: 2-1                       [-1, 448, 118]            --\n",
-      "|    └─Rearrange: 2-2                    [-1, 118, 448]            --\n",
-      "├─Linear: 1-2                            [-1, 118, 256]            114,944\n",
-      "├─PositionalEncoding: 1-3                [-1, 118, 256]            --\n",
-      "|    └─Dropout: 2-3                      [-1, 118, 256]            --\n",
-      "├─Embedding: 1-4                         [-1, 97, 256]             20,992\n",
-      "├─PositionalEncoding: 1-5                [-1, 97, 256]             --\n",
-      "|    └─Dropout: 2-4                      [-1, 97, 256]             --\n",
-      "├─Transformer: 1-6                       [-1, 97, 256]             --\n",
-      "|    └─Encoder: 2-5                      [-1, 118, 256]            --\n",
-      "|    └─Decoder: 2-6                      [-1, 97, 256]             --\n",
-      "├─Sequential: 1-7                        [-1, 97, 82]              --\n",
-      "|    └─LayerNorm: 2-7                    [-1, 97, 256]             512\n",
-      "|    └─Linear: 2-8                       [-1, 97, 256]             65,792\n",
-      "|    └─GELU: 2-9                         [-1, 97, 256]             --\n",
-      "|    └─Dropout: 2-10                     [-1, 97, 256]             --\n",
-      "|    └─Linear: 2-11                      [-1, 97, 82]              21,074\n",
-      "==========================================================================================\n",
-      "Total params: 223,314\n",
-      "Trainable params: 223,314\n",
-      "Non-trainable params: 0\n",
-      "Total mult-adds (M): 23.93\n",
-      "==========================================================================================\n",
-      "Input size (MB): 0.10\n",
-      "Forward/backward pass size (MB): 0.86\n",
-      "Params size (MB): 0.85\n",
-      "Estimated Total Size (MB): 1.81\n",
-      "==========================================================================================\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "==========================================================================================\n",
-       "Layer (type:depth-idx)                   Output Shape              Param #\n",
-       "==========================================================================================\n",
-       "├─Sequential: 1-1                        [-1, 118, 448]            --\n",
-       "|    └─Unfold: 2-1                       [-1, 448, 118]            --\n",
-       "|    └─Rearrange: 2-2                    [-1, 118, 448]            --\n",
-       "├─Linear: 1-2                            [-1, 118, 256]            114,944\n",
-       "├─PositionalEncoding: 1-3                [-1, 118, 256]            --\n",
-       "|    └─Dropout: 2-3                      [-1, 118, 256]            --\n",
-       "├─Embedding: 1-4                         [-1, 97, 256]             20,992\n",
-       "├─PositionalEncoding: 1-5                [-1, 97, 256]             --\n",
-       "|    └─Dropout: 2-4                      [-1, 97, 256]             --\n",
-       "├─Transformer: 1-6                       [-1, 97, 256]             --\n",
-       "|    └─Encoder: 2-5                      [-1, 118, 256]            --\n",
-       "|    └─Decoder: 2-6                      [-1, 97, 256]             --\n",
-       "├─Sequential: 1-7                        [-1, 97, 82]              --\n",
-       "|    └─LayerNorm: 2-7                    [-1, 97, 256]             512\n",
-       "|    └─Linear: 2-8                       [-1, 97, 256]             65,792\n",
-       "|    └─GELU: 2-9                         [-1, 97, 256]             --\n",
-       "|    └─Dropout: 2-10                     [-1, 97, 256]             --\n",
-       "|    └─Linear: 2-11                      [-1, 97, 82]              21,074\n",
-       "==========================================================================================\n",
-       "Total params: 223,314\n",
-       "Trainable params: 223,314\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (M): 23.93\n",
-       "==========================================================================================\n",
-       "Input size (MB): 0.10\n",
-       "Forward/backward pass size (MB): 0.86\n",
-       "Params size (MB): 0.85\n",
-       "Estimated Total Size (MB): 1.81\n",
-       "=========================================================================================="
+       "tensor(0.1638)"
       ]
      },
-     "execution_count": 214,
+     "execution_count": 345,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "summary(vt, [(1, 28, 952), (97,)], device=\"cpu\")"
+    "data.std()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 346,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x = vt.preprocess_input(data)"
+    "from torchvision import transforms"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 118, 256])"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 347,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x = vt.encoder(x)"
+    "n = transforms.Normalize((0.5,), (1.,))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 390,
    "metadata": {},
    "outputs": [],
    "source": [
-    "trg = torch.tensor([10, 62, 22, 24, 31, 14, 62, 55, 50, 62, 54, 55, 50, 51, 62, 22, 53, 74,\n",
-    "        62, 16, 36, 44, 55, 54, 46, 40, 47, 47, 62, 41, 53, 50, 48, 79, 79, 79,\n",
-    "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
-    "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
-    "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
-    "        79, 79, 79, 79, 79, 79, 79])[None, :]"
+    "ra = transforms.RandomAffine((-1.1, 1.1), scale=(0.5, 1))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 399,
    "metadata": {},
    "outputs": [],
    "source": [
-    "t, tm = vt.preprocess_target(trg)"
+    "d = ra(data)\n",
+    "d = (d > 0.15) * d\n",
+    "d = (d - d.mean()) / d.std()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 400,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "VisionTransformer(\n",
-       "  (slidning_window): Sequential(\n",
-       "    (0): Unfold(kernel_size=(28, 16), dilation=1, padding=0, stride=(1, 8))\n",
-       "    (1): Rearrange('b (c h w) t -> b t (c h w)', h=28, w=16, c=1)\n",
-       "  )\n",
-       "  (character_embedding): Embedding(82, 256)\n",
-       "  (position_encoding): PositionalEncoding(\n",
-       "    (dropout): Dropout(p=0.1, inplace=False)\n",
-       "  )\n",
-       "  (linear_projection): Linear(in_features=448, out_features=256, bias=True)\n",
-       "  (transformer): Transformer(\n",
-       "    (encoder): Encoder(\n",
-       "      (layers): ModuleList(\n",
-       "        (0): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (1): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (3): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (4): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (5): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (decoder): Decoder(\n",
-       "      (layers): ModuleList(\n",
-       "        (0): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (1): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (3): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (4): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (5): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (head): Sequential(\n",
-       "    (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "    (1): Linear(in_features=256, out_features=256, bias=True)\n",
-       "    (2): GELU()\n",
-       "    (3): Dropout(p=0.1, inplace=False)\n",
-       "    (4): Linear(in_features=256, out_features=82, bias=True)\n",
-       "  )\n",
-       ")"
+       "([], [])"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 400,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 4320x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "vt.eval()"
+    "plt.figure(figsize=(60, 20))\n",
+    "plt.title(sentence)\n",
+    "plt.imshow(d.squeeze(0).numpy(), cmap='gray')\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 351,
    "metadata": {},
    "outputs": [],
    "source": [
-    "t"
+    "import torchvision"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d  = torchvision.transforms.functional.gaussian_blur(d, 7, (0.75, 0.75))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([[[-0.4344,  0.4939,  0.0382,  ..., -0.0808, -0.0290,  0.4399],\n",
-       "         [-0.4350,  0.5146,  0.0356,  ..., -0.0634, -0.0289,  0.4271],\n",
-       "         [-0.4303,  0.5245,  0.0435,  ..., -0.0755, -0.0267,  0.4240],\n",
-       "         ...,\n",
-       "         [-0.4477,  0.5377,  0.0596,  ..., -0.0866, -0.0283,  0.4457],\n",
-       "         [-0.4475,  0.5435,  0.0606,  ..., -0.0900, -0.0293,  0.4440],\n",
-       "         [-0.4488,  0.5476,  0.0689,  ..., -0.0914, -0.0276,  0.4411]]],\n",
-       "       grad_fn=<AddBackward0>)"
+       "([], [])"
       ]
      },
-     "execution_count": 211,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 5760x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "vt.decoder(t, x, tm)"
+    "plt.figure(figsize=(80, 20))\n",
+    "plt.title(sentence)\n",
+    "plt.imshow(d.squeeze(0).numpy(), cmap='gray')\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "tensor([[[-0.4179,  0.4755,  0.0407,  ..., -0.0609, -0.0870,  0.4562],\n",
-       "         [-0.4262,  0.4845,  0.0361,  ..., -0.0497, -0.0847,  0.4459],\n",
-       "         [-0.4237,  0.4900,  0.0409,  ..., -0.0573, -0.0812,  0.4434],\n",
-       "         ...,\n",
-       "         [-0.4477,  0.5053,  0.0394,  ..., -0.0489, -0.0815,  0.4589],\n",
-       "         [-0.4469,  0.5069,  0.0407,  ..., -0.0500, -0.0808,  0.4573],\n",
-       "         [-0.4464,  0.5079,  0.0416,  ..., -0.0510, -0.0801,  0.4570]]],\n",
-       "       grad_fn=<AddBackward0>)"
-      ]
-     },
-     "execution_count": 213,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "AttributeError",
+     "evalue": "module 'torchvision.transforms.functional' has no attribute 'gaussian_blur'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-37-2a6f5c80ce06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorchvision\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgaussian_blur\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'torchvision.transforms.functional' has no attribute 'gaussian_blur'"
+     ]
     }
    ],
    "source": [
-    "vt(data, trg)"
+    "torchvision.transforms.functional.gaussian_blur"
    ]
   },
   {
diff --git a/src/notebooks/Untitled.ipynb b/src/notebooks/Untitled.ipynb
index 208f098..ca0b848 100644
--- a/src/notebooks/Untitled.ipynb
+++ b/src/notebooks/Untitled.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -87,7 +87,7 @@
        "'CNNTransformer'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -98,14 +98,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-11-16 20:07:51.973 | DEBUG    | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n"
+      "2020-11-18 20:31:23.104 | DEBUG    | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n"
      ]
     }
    ],
@@ -115,581 +115,206 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "checkpoint_path = \"../training/experiments/VisionTransformerModel_IamLinesDataset_CNNTransformer/1102_221553/model/last.pt\"\n",
-    "model.load_from_checkpoint(checkpoint_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.eval()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data, target = dataset[1006]\n",
-    "sentence = convert_y_label_to_string(target, dataset) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data1 = (data - data.mean()) / data.std()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([98])"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "target.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2880x1440 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(40, 20))\n",
-    "plt.title(sentence)\n",
-    "plt.imshow(data1.squeeze(0).numpy(), cmap='gray')\n",
-    "plt.xticks([])\n",
-    "plt.yticks([])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('Horbwargethers sis tHater alate Bate Bath Con Hats the Bateries.<eos>',\n",
-       " 0.2612667977809906)"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.predict_on_image(data1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data, target = dataset[110]\n",
-    "sentence = convert_y_label_to_string(target, dataset) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([], [])"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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YL7Twpl+JZ+OtgGlJqTsB1ItEwARBEARBEMTVo9guIgRBEL+UcM5/f/5SBFEaxth7oeWJef5a9+V6hWm7Uv0xgB+RYEMQBEEQBPHWQKINQRAEQSgwxl6AlsPod4skgf61R0++PA4ttOqd17g7i0YPbzOjDFruIDpOx+k4Hb/ejx/gnL9kcpwgiF8xKDyKIAiCIAiCIAiCIAjiOoQSERMEQRAEQRAEQRAEQVyHLCo8ijHGLZbiOg/nHIwx+XkB9S2onFperVt8v5rYbDbYbNq0pFKpBffP2Dez6xY7XmJ+rFYrGGPI5/PI5ymKgbh+uNrPu1rf1Xj3qXUU6yc9UwRBEARBEATxtjHJOQ8YDy5KtLFYLHA6nQC0f/ALI0J8Fv/AVwUMcVw1NowGh1pHLpeT361WKywWC3K5HPL5fEGbNpsNnPOC/0r1w2q1FrSpnhNCFGMMt9xyC1auXImRkRE8++yzSKfT4JzLOi0WCywWi+yPaHflypWoqanBhQsXEI1Gkc1mTdsRfTUbv/Gz8btxbKJ9oyFnZtip86OWMf4VfTSjmBEq6hRzotZX6lrj/VosFosFH/7wh+FwOHD+/HmcOHHCdH4XwkIM7GJjuFrtmRnlxb5fSRvF6i/V7mLbKjWu+YSGhQq9alkz4UE8z2ZjKiYsq8+Y8bjxOTOOS7wPjHWJNszqFP00mxdjW6IPal+M/RBjFs+g+Kv2zfhdFeBzudycvgNAPF4s7QdBEARBEARBEFeZQbODVxQeZRRehKhhFFHEOWEsmBk/xmuEUCPqFCKOEXHeKAgVExXUcmq7xnJutxtOpxOzs7PweDyy71arVQo/qrAk6nA6naivr8fWrVtN50o1CoV3iHrMeI3oo/rXWLbY8WLU1tZiw4YNqK2tnXONmQhXVlYGh8Nhet/MDE31+EL6o4phdrsdpby4zLBarchms8hkMgAgPaSuhIUIBsXW4dVqz0zMUz9fiVdaqfquRjtmdZj1o9h6eLOeKGbihrFts7VpVk6UNX6fb07UZ8YoEBn7aFan+h5T+yoEYlUUNbZlHIsqQBvHo15vbNf4nlvss0gQBEEQBEEQxFvDov9lbiYwmIk4ZmXV86rBrh4zljN6zQgjw0wkms+4KvaLtrjGZrPB6/XC4/EUeBUZrzUzxqanp5HP57Fnzx5p9BQzII3GmXH+FsJ8hiBQaMAxxuBwOKRwsxBxpaWlBatXr0ZDQ0PRe1zqs5lnjxmMMaxfvx719fVwOBwlx61e43Q6kcvlkE6nkc1mYbfbF3TtQuq+llRXV8Pv98u5uJL+lBIz1DLz1b3Qto1r3WKxoKmpCatWrYLP5ytaT7H3SbF6za5dyDNf7J20kPG/mfWw2Oe7mIeN2lfVY8is3lLtGUX0Ul4+1/o5IAiCIAiCIAjiTYo2xl+DjSKB2bXzGRTGX5PNyqjl5uuj8bjxGjWkx+VyobKyEj6fDx6PR4Y4qe2q16vXTk1NIRKJYM2aNXC73XNEG/V6EYqgzsl8fRd1LBSzelOpFABg9+7dc+6VmZdAS0sLtm/fjrVr15p6By3UAC4lEIl6WlpaEAgECoSy+bDZbHC5XNIDyhgCV6wfCynzVhqsou6qqirU1tbC6/UW3INVq1ahubkZbre75HyXOl6s3bdiXMY5EwLhunXrsGPHDgQCgZLPqtmcz7fWzCgl2ho9X1wuF8rKygq8s+YTjMwEjvnaNgok6vdSdavnxDp3uVylhj+n30J49vl8sFgssNlsUlBW/1Of+1LvVYIgCIIgCIIg3n6uSLRR//Fvs9nmCDeqEaIeV8uJY0YjwfhdCC3qr8umA9HrVo0wYz3Ca0ccF+EA4pjT6YTL5YLT6YTFYkEkEikI1TL+Cq0eSyQSiEajSCQSqK+vNxVB1GvNxmz0QFKN1vnCIsyOGecyHo9jdnZWetqYGZHqMc45GhoasHTpUun1of5Sb1wX6rwXu1/Fftk/evQoRkdH595YE0QduVwOPp8PlZWVsNvtyGazBf0S/RQhHzabrahwaBQgS3kjGPsxnxipllefi1tvvRUHDhzA+vXrC9btmjVrpIBlXEfFRLZSqGKF8ZlYCGo5o+BgVi8AlJWVYdmyZVi2bNmc0DczTw71mSglgBjLq2vROC9mubTE95aWFixZsgQVFRUFbZo9Y2bvJHUcZs+EGg5qrN+Y26rYfRDnKysr0dTUhLq6ujnvWbG+zep1Op1oaGjAzTffDKvVCr/fD7/fD6fTKcuJNQ+8kYRdfVcSBEEQBEEQBHFtWXQSENUAFuEoIjzFzPAxJuw1GjyqaAK8YYwZE2iKMkZDxygMiD4Z8zWYXSe8XtQcDqlUCj09PfjFL34hBRR1DGp9qpHEGEM8Hkd/fz927tyJrq4uU1FKbds4ZjPjzUz8MdalGoxGI1FNdprJZJBKpWCz2VBZWYloNGpqyIvrstksPB6PNPRSqVRBwlNj/0U7xntq/GuEc45IJGJ6rhSpVApVVVUAAI/Hg3Q6PWe9lZWVwefzobGxER6PB6dPn0Y8Hjc1mOczVI1i20IxE9+EIMYYQ2NjI6LRKLq6ugAAjzzyCJxOJ5LJ5Jx1YVzPpTBeayY2LKQOFWNCcGO9Yi36/X7U1NTAZrNhcnISAOTzYCZazCcmqWvMWMasj8UQ6zeTyUixbGpqquD5MYoxajvFRCDje0/ti/G9oSb9FaKJeG8lk0kwxmSZbDYLxhjq6+uxfPlyPProo8hkMrKMqCOfz0vBRdSdz+fh8Xhw8OBBPP/889ixYwfy+Ty6u7sxMjIi+ygSvYu2xDxRXhuCIAiCIAiCuPYsWrTZtm0bDh48iCVLliCVSiEYDCKXy8Fms2FwcBAvv/wyBgYGMDMzM+eXZTWBr/prOGNMehqo3jBGUcS484kwAB0OB6qrq7F//37k83k4nU688sorGBgYQDqdBgAZWpBOp6VBpAo3jDGk02nZN5Hc1u/3IxKJyDJWqxUulwubNm1CMBjEyMiINHaSySTGxsawYsUKWK1W01/oRf/VcCO1jJm3QTHPEPV6UVZ4GzHGpMiiGrHpdBqhUAjl5eWYnp4GoAldom9C1AE0w9btdoNzLsU6ETJWyovGzJBVUftqJkIVq08tI4xWu92OTCaDbDZbMLe7du3C0qVLwRjD9PQ0ZmZmsGLFClRXV+Opp57C7OxsSW+HYn02Oybmx8zDw3hvVaN4amoK0WgUANDU1ISenh7k83mk02kZymZGKS+UUsLY1cD4HIq61eccAMrLy2G1WpFIJOQ6KyYWqfNSTAxS21c/G8+ZXW8UPS0WC6anp7FlyxYAQGdnp7xnxh2dir1/VBGmmKiqeumZzaMov3TpUqxcuRLpdBpHjhyZ8w4MhUI4deoU2tra5HtJvIuMIo3an1wuh2w2C5/Ph0AggGAwiDvvvBOJRAIjIyMFQo0q0og+X601QxAEQRAEQRDElbNo0cbhcOCJJ57A2rVr0djYiGeeeQZTU1Ow2Wxobm7Gnj17sHPnTly8eBGnT5+WIoDYulsYAuLXYovFAo/Hgw0bNsBiseD06dOynNmOT3a7HUuXLsXo6CiSySQaGxuxfv161NXV4eTJk4hGo7jrrrvg8XjgdDrhdruxbds2VFZWYnBwEH19fYjFYjKURvQLgDSW7XY7nE4n1q1bh/Xr1+OnP/0pYrGY7E8ikUBXVxfuuOMONDU1oaurC9FoFKlUCpcvX8aOHTvmeAqpXkdGQcBut6OyshKTk5NFxQHjVuNVVVVYuXIlotEoxsbGsGrVKmSzWeRyOfj9fqxZswbd3d2oq6tDX18fLl26hOnpaaTTacRiMQQCATDGcNNNNyGVSiGZTCKXy6G/vx/d3d1gjCEUCsFiscDlciGTyRRsJ2zsnyrQuFwutLS0wO124/z58wXeFWI+jNcLjN4LVqsVgUAAK1asgN/vRzQaxalTp5BOp2GxWJBKpRCJRBAOh8GYlkvl/vvvR39/P06ePIlEIgG/34/169ejrKwMTz31FFKpVEnhQO2TxWJBRUUFysvLMTk5KUVAtcyWLVuwceNGlJeXIxQK4dy5c+js7JR1er1etLS0YPny5aivr0dZWRmefvppzMzMwO/3w2q1YmJiAlardY6R73A4UF5eDp/PB5fLhbGxMUxPTxc8S2aeN6U8UdTzizXMVZGzVJnly5fD5XJhcnLSdNeiUv0Qa10dn9maU6831iMEDWNYoyCRSKCqqgo+nw8Oh0N6aameWiIEyZj8XLSnrnmze2H0SjIrwzmHzWZDQ0NDgeeMmveKc45MJiPPq946oj61DeHpxhhDNptFJBKBx+NBKBRCU1MTqqur5VoToqeZ8ESiDUEQBEEQBEFcexYt2gwMDCCZTKK6uhq1tbWw2+24fPkyAC1nSiQSQUNDA1paWtDQ0IDJyUm43e4CAyabzSIcDqO3txfRaBSbN29GIBDA5cuXC35ldjgc0nVfGBOcc8TjcaxYsQKRSASbNm1Ca2srjh8/jlAohM2bN8NmsyGVSsHhcKCpqQkrVqzA8ePHEQ6HpcGuGinCi0RN0im8bfbu3YtnnnlGemYI4ygWi6Grqwv79u1DKBRCLBZDMpnE+Pg46urqZN9V484sDwxjDD6fD7fffjt++tOfIpFImM57IBBAZWUl3G430uk0PB4PWltbkUwmEQgEMDExAYvFgkAggJUrV2LVqlVgjOHUqVOYnJxEIpGQv6xPTU2hpaUFt956K7q6ujA6OorZ2Vk4HA40NDQgFAohEokgHo9Ljw81KbMR1WgUoRz19fXzXmM0poWxqXo97Nq1Cy0tLdIrxel0oqWlBb29vSgvL0dZWRkSiQTsdjvcbjf27t2LXC6HRCKBxsZG+Hw+OJ1OhMNhdHV1IRQKFQ2fMQoBNpsNdXV12Lt3L2644Qb09PRIr51wOIxQKISamhps3LgRg4ODmJ2dhcvlwpIlS+Rzsnr1aqxfvx4WiwXRaBS9vb1YsWIF9u/fj9HRUZn/aHJyEg6HQ4auBYNB2O12rF69Gps2bZLb0N9333147bXXcO7cOYyOjs7xyFHXdTFhRdwvp9NZ4Hm2GMT98Xq98Hq9sNlsiMfjMvSspqYG+XxehuAZ+2X0whLilshRVF1djfr6eszMzMBms+H06dOYnJwsENw45ygvL8fatWsxMjKC6elpeL1eNDc3w+/3I5PJoK2tDZFIpOCeC/Ezl8vB5XLB4/FIDxY1P47L5UIgEEBzczM6OzsRDocL5k+ty2yOjfNt5kEkhEe32w2Hw1Eg2BiFFFVIMt5f4zOotpXL5VBWVoZQKASn0wmHwyG95kSdZgngSbQhCIIgCIIgiGvPokQbxhjGx8dhtVoxMzODVCoFn88n/8E/OTkpd1HasWOHFAWGh4eRTCbhdDphs9mQy+WkB8frr7+OdevWYXp6GuFwuCDEZe/evYjFYhgeHkYwGJS/vsdiMWzduhU+nw9NTU1wuVwIBoPYtGkTlixZgrGxMYTDYZSXl6OlpQWZTAbT09MYHx83DVdwu91oaWlBdXU1WlpaAADNzc3SQN+5cyfOnz+PiYkJJJNJOd7e3l68613vKsjtE4lE4Ha74XK5ZH4KMe5EIlFgcIoQh/LycmzevBk/+9nPYLFYsHz5cnDOEQ6HMTU1JecegBRrvF6vrJ8xhmAwKJONigS2DQ0N6OnpkV4EgObhNDU1hT179sDj8eCpp57C4OAg0uk0ysrK4HK5UFdXh+np6TleTk6nE7W1tdIwVz1scrmcnJtly5ahsrISly5dkn0vFuridDqRyWTAOZft5/N5RCIRtLa2YtOmTYjFYhgbG0M8HkdNTQ3sdjtsNhtaW1tlLpxAIICqqips374dr776qhQTXC4X4vE4BgYGMDAwUNDn+UKJbDYbKioqsHTpUuzbtw+RSATJZBLJZFJuD3/nnXeit7cX7e3tmJqaQiAQwMaNG7Fz50709vZix44dsNlsGBkZQX9/PyKRiBQqp6enUVVVJedHrIft27fjxIkTaG5uxtq1a1FeXo5wOIxoNCrXRzKZhN1ux+TkpPQyMhuHKooIgbC6uhoVFRVSaOnv75f3zmq1wuFwyHw6xd4Dfr8fzc3NKCsrk8fFGurs7ER5eTmSyWTRXEWq6OF0OrF+/XrpKVVeXo5AIIBly5ZhfHwcfr9feomp3miMMWzevBktLS2oqalBPB6H1WqFx+OR4kuxnZry+TzC4TBsNhuqq6tlmJq4pq6uDtXV1aipqZHjfPHFF6Wg4nK5UFFRgbKyMgwPD8Pr9SKRSMgwPeOaNwo9Qry2WCxIJpOyThFWaQwNNd5TM489o1ij5p8SibpzuRwcDocU7IqFgonvBEEQBEEQBEFcWxYl2gjBQ+SEEEKB6h2Tz+cxOzuLWCyGyspKlJeX4/nnn0cymURZWZk0uD0eD2677TZEIhHU19cjGo1KMUANc7l8+TKefPJJTE5OSmMrk8mgoqICLpcLdrsd+XweK1aswG233YYjR47g1KlTSCQS2Lx5M5qbmzExMYFNmzZhfHxcGirAGwac2+3Ghg0b0NTUJHNLrFq1CqdPn8ZTTz2FO+64A06nE52dnZicnJS/yttsNgSDQWnw5nI5ZDIZZDIZuQOVMHB9Ph/GxsYQi8XkfArDym63w+v1SuPqpptugsvlQnt7O44dOwYAmJycRDAYRDwex549e9DY2Ijvf//7SCaTuHTpkszHE4vFpMdTU1PTnFCKVCqFqakp3HPPPXjooYcwPj6O2dlZAMDs7CwuXryIzZs3o7e3F9XV1SgrK5NeS3a7Hdu3b0ckEkEmk5EJqPP5PJLJJCYnJxGLxdDc3AzGmAwlEvNcXl6OVCol74HdbkcgEJBCksfjQVNTE5xOJ86fP48DBw5gdnYWL7zwAoLBIMrKypDNZjE7O4tAIIANGzbgzJkzWL58OZYtW4ZgMIiqqiqMj4+jtbUVly5dwtDQEKampqTHlrqWSyEM4FQqhXA4jGQyiRMnTuD1119HIpGAzWZDY2Mjtm/fjm9/+9syDCgSiWB0dBQf+9jHcOLECSxbtgyHDh1Cf3+/bP/8+fMYHh6W4oLT6SzwfNizZw9GRkawf/9+5HI5PPPMM+jt7QUAHD16FPv27YPP58PGjRsxPj6OkydPmoqR6lgAyLC51atXo7q6GslkUt6/oaEhzM7OoqysDE1NTRgaGpoj3Ij16nK5sHXrVqxbtw6RSASXL1/GzMwM6urqsHPnThn2FY1GC9a7Wb+Eh829994rc0TFYjEEg0HMzs5ieHhY7vQUCoUwMzODmZkZANruSHfeeSfa2tqwefNm+P1+jIyM4MyZM+jo6EAqlZJhfYAmBPt8PjnXU1NTcDqdaG5uRn9/v/Syq66uxsaNG+H3+5HNZpFKpbB//34cP35czrPH48GKFSuwfPly/PznP8emTZswPDwsQ+isVivcbjfi8Tjy+bzcXlz853K5MDo6Cs65fI6Awt32gLmhZA6HAy6XS3rOqeKNKrqJZ16IPyJPVTqdll5p8XhcevYIb5tiIZwEQRAEQRAEQVwbFu1pI4STmZkZzM7Oorq6uiD8R3hAbNq0CS+99BKWLl0q8zGo3htWqxXPPPMMtmzZAr/fj+rqalRXVyMYDEqX/ampKdjtdrhcroLEw0LcuHTpEtavX4/bbrsNTqcT3/nOd6Qw43Q6pWF199134/Dhw3juuedMwxQikQieeOIJGapQXl6OI0eOIB6PY3JyEt3d3Th48CB27doFxpgUAWw2G1599VWEw2GZ0FMk/1TzSgQCAWzevBmxWAw///nPpWEkcDqdMnmxCNnYs2cPKisr8corrxQYYKItp9OJRCKBnp4eWY/wUqioqEAqlcLq1avneMRkMhmEw2GsWLFCGrZqnoyZmRmEQiFYrVbs3r0bfr8fw8PD8Pv9iMfjcLlc2Lt3LxKJBCKRCGKxGNLptFwP8XgcsVgMPp8PPp+vYO3ccccd6O/vR29vL+LxONxuN5YuXYpdu3bh+9//PqamprB27VrcdNNNmJiYwI033oiPfexj0vCfnZ1FMpmE1+vFwYMH8eKLLyIUCqGhoQGrVq1CX18fotEoenp60NnZCWDuTl2l1rYoL/5mMhlMTEzA7Xajt7cXq1evxvj4OIaGhuB0OrFmzRqcPXsW0WhU3lPhbeX3+/HAAw/g05/+NEZHR2UOGlF/OBzG9PQ0Ll26hObmZng8HnDOkc1mMTQ0hO3bt2P58uV4/fXXMTAwIL27ZmZm8Oyzz0qPnP3796O9vV16tBiNbdGew+HAb/7mb8Lv96OzsxOPPPII8vk8tmzZgjvvvBNPPfUU+vr6UFdXh/e973147LHHcPHixQLBw+FwwOv1Yv369Xj/+9+Phx56CD09PQUeX8PDw9i1axdaW1tx4cKFgrVudg+EyDswMIDly5cjl8shnU7LeQ6Hw+ju7saNN96ITZs2we1248yZM8jn81iyZAkCgQB6e3vh8/lQX18vPbGMOWjEjla/93u/B4fDgYGBAVRVVcHr9WJ6ehoOhwPZbBaNjY34jd/4DfT29uL8+fNIpVJYvny5DLES4ZQiCfbNN9+MsbExfPjDH8bx48fx4osvYmJiAvX19di6dSteeuklJBIJbNu2DbW1tXC73TKE66/+6q8Qj8flu0SIeGLntpqaGiQSCRkC5/F4sHTpUvluDYfDsFgs0kMtGAzC5XKhsbERwWAQsVhMzn1FRYV8d9tstoJQLPHeEveZIAiCIAiCIIjrh0XntBGGZyKRQCaTQUNDAywWCzKZjPwHf2NjI3bu3Im2tjb827/9m+lOJBaLBbt378bFixeRyWSwZcsW3HzzzRgcHEQ+n0dVVRWWLVsmQ3eMBpjIU/LEE09genpaesN0dHSAMYYlS5bA7/cDADo6OvDQQw9JA1I1TNQknwCkF4jD4ZC/PA8ODuIb3/gG7Ha7TFIMQO4qJerL5XKIRqP4oz/6I0xNTcnrg8EggsEgbrjhBjzxxBPSSBJG08zMDIaGhrBmzRp0dHTg6aefRmtrK1pbW7FmzRqZ1JZzDr/fL0M67rrrLgwNDckcNzfddBM8Hg+GhoZQU1Mjkw2rolo6nZa5UMSuUWpS23w+j/7+ftx1112Ix+M4e/YsGGNYu3YtDh8+jEOHDsly6j0V4+Gc4/z587j77ruxZ88evPzyy9Iz6c4778Tzzz+PsbExRKNRhMNhnDhxAhs3bpTJqsUY3/3ud2NsbKzAM8rtdqOurg7r1q3Dk08+ibGxMVgsFgSDQZSXl8NisWDVqlVYv349Lly4IMOu1HXjdrulOGexWGSon5o0Wp2LVCqFtrY2OJ1O7N+/H2NjYxgeHobD4ZC5a4yCjJjrZcuWIRQKmXqsiLUYjUbltuVClHzttdfw3ve+FxUVFQWeIgJ1XYncPiK8R21Hzbeyfft2NDY24vTp03j22Wfl/Xr99ddx4MABlJeXyxCaWCxWsG293W7Hhg0bcMcdd2DdunUIBAL4zGc+g56engKPD4vFgv7+fmzduhUTExPSy2Q+L6BkMokf/OAHYIzB4/HIxMvLly/HvffeC5vNhocffhibNm3Chg0bUFNTg+PHj+PjH/84Hn74YfT396OzsxNerxfLly/Hvn37cPDgQbS3t+PZZ5/FxMSETMj7ne98R4ZTtba2oqWlBfl8XnoeffnLX4bFYsHGjRuRTCYRj8dx8eJFfOtb3ypIUizeSa2trfjoRz+Kb3zjG3j3u98tQ+g2btyIvr4+fOlLX5JC2/nz5+F2u7Fjxw6k02kkk0n5bACQHjiinfvvvx9nzpzB2NgYli5dive85z3o6urC6dOnUVNTg3Xr1mHlypVYuXIl/H4/Xn75ZRw9ehTvfe978dxzz+G1115DJpNBJBKRnjuqACzEm9nZWZmYWE2+XGrrdIIgCIIgCIIg3h4WLdo0NDQgkUgU/IIrcro4nU6sWrVKeo2Ew2FcvHixILGm1WpFVVUV3vGOd6Cvrw+dnZ0y78rNN9+Mr3/965iZmcHp06fxz//8z7j99ttx2223wev14tVXX4XVasXNN9+My5cvIxKJIBKJYGhoCD/72c9QXV0Nl8uFaDSKI0eOyKS+In8EMHcLZhE6IAyWdDotw5uE90Aul5P/pVIpmZtGTZIs6hOhPqpxJMrV1NTIcmr4QzAYxCOPPIJPfvKTCAaDcDqdGBkZwejoKD74wQ8in8/j1KlTMrHt0NAQent7sW/fPnz+859HT08PQqEQxsfH0dHRgVAohObmZpkvRxUNhBAxMTGBj370oxgfH0d3d7cMm6itrcXdd9+NSCSCkydPory8HBs3bsSGDRtw5MgRGU5j9F4Rhh4AjIyMYHBwEDt37sT73vc+KfRwzmWC6mAwCAAyEa/YtntsbAzd3d1yy+79+/fLvCZ+vx+pVArHjh3D7Oys7Mfx48cxMDCAQCCAv/3bv8WBAwdw7733YnJyEtFoVHoBhUIhjI6OIhgMFoghqthiFBez2Sza2trwiU98Ak1NTQgEAjJMa3x8HDt37pQ7FVksFjQ1NWH79u34wQ9+gO3bt8vt19V2hIi2YcMG6aWybNkyOYeDg4Nwu92orq7G0qVLsXr1aincieds5cqVaGlpwczMDCYmJgrug7Edi8WCW2+9FX19fejt7ZXHxfbiTU1N8Hq9YIwhkUigt7cX27Ztk/mnbrjhBgQCAYyNjeHGG29ELBbDwMCAFGzUthsaGuByudDZ2YlMJgOPxyOfezUxt+ifCOkpKyuTYZViTL29veju7sZHPvIROBwOHD9+HC0tLdiwYQN+53d+B4lEAt3d3fJ5nJ6exuuvv46uri44HA7cc889OHDgAF5++WV0dHQgl8shHA7L3ZRyuVxBm1u2bEFnZyceffRRRKNRZLNZZLNZpNNpJBKJglCiTCaDmZkZZLNZDAwMYHBwEPX19fjQhz6Eo0eP4tChQ8jlcvjsZz+LL37xizh9+jQikQhqa2sxPT0t58HhcGD16tVYsmSJ9BYaHh4G5xxerxdLlizBtm3b4Pf7cejQIbS3tyOVSqG1tRXbtm1DMBjE1772Nfl+ESF2Ys5FWJQQFsWYOOeoqKjArbfeiu7ubrS1tSGfz8vwK5vNhsnJyVL/KyAIgiAIgiAI4m1g0TltMpkMGhsb0dTUhDVr1mDJkiW48cYbpRGSyWRw5swZTE1N4ZZbbkFvby86OzuRTqfh9XrR0NCA2tpajI6OorOzUxqtp06dQl9fH3w+Hzjn8pf6sbExrFy5EmvWrMGOHTswOzuLtrY2XLhwAVNTUzKHjBrWI8QVowcEMHdHFtUTpVguBzUpqDHcSNQlMG4LrB4TBqxxx5lkMom2tjY89NBDMkGoMBrdbjf8fj88Hg+SySRefPFFTE5OIpvNoq+vDxaLRSaFTiQS0gNqZGQEn/rUp6QHhhhnPp9HLBbDj3/8YzgcDjzwwAMIBoPS+8XlcmFwcBDt7e0IhUJyO3fGGOrq6qRBqRrexrnNZDI4deoUZmZmsHv3bjz44IPI5XJoa2tDXV0dDh48WLDV+NGjR2Xo3Pj4OI4dO4ZMJoPHH38cNTU1cDgcGBkZQW9vLxKJBKanpwt2mBLbrQsPosceewxWq7Ug547Yzl14N5h5fxULoYrH43j00Ufx4IMPwufzwe12Y2pqCmfOnMHBgwfx7ne/W4ptNpsNoVAIPT09OHfuHNauXSvFRdGG1WrFunXrsHz5cvT19QEAWlpa0NjYiMHBQUxPTyOVSuG5555DPp/HAw88gPHxcYyOjoIxBrvdjnA4jJGREYyMjEhvDWPCW3VcgUAAbW1tMtRMlGlpaZGiiUgKHY/HUVlZCavVihtuuAE1NTWYmprC9PQ07HZ7wbMl6hEC4Z49e3Dy5Ek4HA5s3boVjY2NBetDfebUZ6aqqgpr166V4ZButxtOpxMVFRU4cuQIIpEIEokEJicnMT09jT179uDhhx+WW7+rAqrILdXe3o53vetdqKmpkWWEmCqeE5F7KpfLIRgMYu3atTh48CC6u7sxOjqKUCgkExyLhMGinXg8jjNnzqCnpwczMzOIx+OIRqMyrMvtdgPQPP3E+vZ4PKitrZWeYWKHrWg0Kj2LOjo6cOzYMUxPT8PlcqG2thY2mw3d3d2YmZkBYwzbtm3D1NQULl68iEgkIj2Wmpub4fV64XA4Ct5PXq8XVqtVho6JMLSuri685z3vQSwWQyQSwbZt29DY2Ihz587J3bIIgiAIgiAIgrh2LNrTJhaLweFwwOPxyHwm4XBY5nIReUdGRkZgs9kQCATg8Xjk9tG5XA7Dw8MYGBiQhozIEyNyNIhfsi0WC2KxGKanpxEKhRAIBJBMJtHV1YVgMCh/MVbziRgFFqD09sfivED8Qq2KHMZy4pjwlilm7Is6nE4n3G63NLiMiLCq8+fPF+ymJOr1eDyoqqpCKpWSiU4ByN22zASU2dlZHD9+XIpi6hiSySR+8pOfwOPxoKWlRYaOCIFneHgY4+Pj0itncHAQyWSyIIxIbU+MU20/FAqho6MDFosFVVVVyOVy6Ovrk+tBrBexe5EIg5qdnZUhX+FwGC0tLUin0wgGgzIBsnGs2WxWJqhljKGvr2/OPRfG+ny5bczIZDI4ceIE6urq0NHRIRP4TkxM4JlnnpHeFzMzM5ienpbG/qFDh7BlyxYAkIl+hRFeVlaGkZERDA8Py12HNm/ejEuXLiGTyaCjo0N6qYndoUToDQBMTExgYmICU1NTcwRII4wxXLp0CfX19WhoaEA0GgVjDDU1Ndi0aRPa29thsVhkmFU6nUZtbS3Wrl2LjRs3or+/H8PDw3C73RgaGkJtbS2am5sxMDCATCYjw9aWL1+OsbExDA0NyVAfM2HTKGoyxjA9PS3DdVwuF2ZnZ2Gz2TAxMYHu7m4ZwlNfX4+mpia8/vrr6OnpAeccjY2N8Hg8sFqtchtysUPWyMgIQqGQbEe8K3K5nBRjRFjY2NgYnnnmGZmcvKWlBXV1dQUhTPl8XiZhTyaTeOqpp+R9efnll2Uy71QqBZfLhVwuh0AggHQ6jfLyclRVVSEYDGLVqlWorKyUeaFCoZDcfU/sGJdMJuFwOFBRUVGwGxbn2pbqQmhVc2iJRN5ia2+LxYLKykqZqNxms8lk7plMBpcuXUJfXx/27duHeDwud8EiwYYgCIIgCIIgrg8W7WkzOzsr/8FfUVGBaDSKrq4upFIpKaKIJMITExPYsmULamtrZYLWkZERjI2NFYTZqMa1MfRGiDzDw8MFnitmHjPqebXPajmBKG8UPEQdwpPALK+Dsc5ixrI45vV64ff7paeEsYwoJ3ZkUsfCOZe/xIv+qONVd4YyiigihEg1joXXyWuvvSa9NoQhJ+6JGLNoSwhqxcYpUHeeyefzCIVCOHbsmDQoRUJVYYACmBO2JnL89PT0SDHPbK7Ue2V2T4rdi/nGYAbnHJcvX8YjjzxSEB6XSqXw+OOPo6GhATabTQpf4h7+4he/wJIlS7BkyRKUlZVhZmYGdrsdFRUVGB4eRmdnJxKJhLxPW7duhc1mQyqVwiuvvCLDu9ra2uD1eqXQILxKjAmtS/HKK69gx44dWLt2LRhjSKVSMj/S0aNHUVVVBZ/Ph3A4jJmZGVRVVWHv3r1SgJ2YmEBlZSWOHj2KTZs2Yfv27fD5fDI5dXV1Nfx+P5588knMzs6Cc45z585JLxbjnBv7HQwGEQqF4HA4pGgqxAVRvqWlBWvWrEF5eTkef/xxGbLkcrng9/vlbnIVFRWoq6tDPp/H0aNHpXeY8Z5OTk5KMUoIlz/60Y/Q2NgoRR/h9SOEagCIRqPI5XLo7+/HK6+8Isfy0ksvIZVKIRaLIZvNIplM4uzZs1i9ejW8Xi/sdjvi8Tg6OjqkeBmNRpFMJuW29mfPnkVPT498x9psNiSTSTidTmzZskUm27ZarXJL8mAwCLvdjvr6ejgcDoyNjcHpdKKqqgo2mw1utxvBYFB6IAphXaynZ599Fp/61KeQTqdx7tw5nD17FqOjo4t6RgiCIAiCIAiCeGtY9O5RAAoMfPErtSrCAJoxHg6HcfjwYWlIq8ab6rWieqyUEnBEH4yeNOo5IQKomHnaGI+J+oShKLbxVvuqlhX9M55Xc7sIr52amhpUV1fL3XiMoVXiPzMvIcDco0edU1HGKFyYlVfnXOTwSaVSBQKUcf7V+TKKZMXuj0CEqqh1mIWtGee21Lwbj6mCTLEQHJXFCjeqCGU8nkgk0N/fP0c45FzLsfL1r38da9asQXNzM1wuFy5fvoznnnuuIC9UMplEX18fLl++LNfOhQsX5nhOFROpzLxZjHN08uRJuFwubN68Ga2trQgGg2hvb5eJsW+88UbY7XaZqDocDmPVqlX42te+Jr1/QqEQHnvsMRw+fBgPPvggdu/ejUQigcuXL6OzsxOPP/64FGw453JrbrP5VPum7mAkdjMT5cTa93q92L17N3w+H06fPo2hoSHk83nkcjl0dnbCbrfLbemFN5kIhyom6I2Pj+O5556T4+acy8TD4vlQ+yyS9zqdTrnNvdg1DoBcBwBk2N4XvvAFHDhwAPX19Th//rzMwSNEkUwmI9djNpuVYVvZbFaGb507dw5erxf3338/jh49iq6uLmQyGaxbtw5Op1PuOLdq1So8/fTT6O/vl15CwWAQly5dwtDQELLZLLq7u3H58uWC5O7xeByMMen1Njg4WFSwJgiCIAiCIAji7YUtxni1WCxc7L7j9XqxZcsW3HffffjjP/7jgl1+gMLtwcUOS6rRZCYGiOtUzIxSM+NVbddIqTGK+oVB9oEPfACVlZX44Q9/iMnJyTmJVs36ZBSdxDitVivcbjcOHjyI7du348tf/rIMZzD2Qa1H7OCiii/C+wBAgTdMMfFjPvFCFXlKzZPRI2m+LZyN81/ME8S4DszqnO/aYsJRKeYTOBZyrfq9VNtma1m9zijymLGQe7eQsajXqM+mOKeGA1qtVni9XqRSKSnoqePhnMud1MTzLf6K8+rYjWKfcY7UdWom4Ho8HnzoQx9CNpvFuXPncO7cuTkisXEtGEVMY3tqG8XugXHtq3Olzod4vxnXg9idTJwzevHZbDbpTSM8l44dOybDSH/rt34LTqcTnZ2d6O3tRWVlJZYuXYrZ2VkEg0H4fD60tLSgsrIS/f39MnQPeENQFm2Ld476DuFcS4R83333yR3ZLly4UODVp3q6EQRBEARBEATxlnKac77DeHDROW3UXZRELgY1B4tR0BBGoJq41Ch4GJPCGg2l+QxUszAns7LzGcj5fF7mnxEGl9kOQ8bvan2qlw1jDNu3b4fD4cALL7yAWCxm6r1iDOsyGrdiXlXPI9XQNRuX2g+z/qvbFxfzVDHOo2oMGw3ZYl5Jxe6N2Xkj8wk6peo1tmGs80owu3Y+QbBUnxYjNpnVu5hrzbYNF/1QQ+zEd5HfSG1TXRcifMesX2b9Vdd6KaFRFZJE6NPHP/5xXLp0CSdOnMDIyMgcwcasf2YhksX6pn42CjJGzzjhFaR6AYn3hBCxjAKNQPRJ3dEJ0LwSX331VTn3IqeXSEQt3p2Tk5OYmJiQ142OjqK9vX3OWFThS92O3dh/sXvUli1b8JWvfAWRSMR0bgmCIAiCIAiCuHYsWrQRoTWpVAqdnZ34h3/4hwIDSTUYxH/CUBAYRQegMCmpeszM08VYzuxX/FIGdjEhw+Vywev1wuVyIZVKFQgb6jXG72oYhVrnjh07UF1djd7eXrS3t8/J32IUUMz6Jo6pYR7C6FLLCsPRWEbFeM18xqxRUDF6iJiJOcZ5mC9psyrcGcdfSmRbjFFpJkpdLUp5aSxUjDI7Z6zfeG6xYyj1bBS7z2YYn7PFtG0Um8yeefFdJND9wAc+gK6uLpw8eRKRSKTgORBryiwZt9GzaCFzZ3wG1eNm3lFG4dMsd4+ZoCmuE+9SkZTb2GY8Hi/4Luoxbp1u9JwyjsEo+DLGUF1djQ0bNmDr1q345je/KUPZVI+rxd5ngiAIgiAIgiCuPosWbYA3jIBEIoFkMmn6y38xzwvBQoxno3BQzHg3E2EWYngaf/UXv4Ank0kp2izEi8PoxSBCrWZmZnDhwgVMTk4iHo/PEXzMhKz52iomeJn1Vf3VX63fKESp/TBeY5w3tYzxnLFOM0PZrN5SuTMWskYWytX2HjATthbb1mKfgcXUbVYPUNpLqZiwIjDWsRgvFmMdZucA7Tmsr6/Hrl27MDU1hddeew3hcFiGYBZ7BszeP/PNnfEZLHU/jaKnkWLhWkZR2zj/Ru88cS6bzcryakikcXyinlLjVMXTQCCAjRs3YsOGDTh8+LDMWaQKT6p3DkEQBEEQBEEQ144rFm1UY0P8YqwacqpRYvZLujinflfrnq99s7/G8/PVYfTqyGQyaGtrkwl6jUZQKW8F4/l8Po+xsTEkEgkpABmvK2ZAFqu7WPvFBBaz+ooJKWbXlhJaFlJ/sfKleLPixNvNm+nj1RJ23sr2SglSZuvX7Nxi+m+329HS0oLVq1fD5XLh3LlzGBkZKXivqOJhsTW8EIp5dZl5183n9WX2jjOKrGZtqv035vMxe6dybu7VZnz3qH0QZUQusrVr12LJkiWYmJjA2bNnkU6nTb0AfxmeP4IgCIIgCIL4VWfRoo1qbBgNHMaY3Pbb7DqguABwJf0wshhDw8zrYHZ2Fi+88AIAFN2FyuyY8RdyzrWdYEKhUMFcCWPLzLhdiHdQKU8As34u1AvEaFTO96v9fMdL9YtCLn65KSbYXA1sNhvq6uqwbt061NfX45VXXsGFCxdknhfjun6za7SU6GR2Xm3TKKIa61ERYowaVmWsX71GfDZLFm707FPfC2bCklqX2GGqtbUVyWQShw8fRiKRkHnK1P4KEZsgCIIgCIIgiGvLokWbYsaKMWTG7JdnM6Hnahp/pepaSIgJ5xzpdHrBdRYrZ2ZUic9moUrFwpvUfpvNVakQFdW4MxOEjAaemTdAsbJvBvr1/tqx2OdtseXnK1usPsYYrFYrampqcMsttyAej+PJJ5/E5OTkHKHvzayf+UTRxV5v9jyaecYYd48SYyoWiijeBequVOI6NXeVWqcxgbMoo+4i1dzcjAMHDqCjowMvv/wywuGwDIcy8+ohgZUgCIIgCIIgrj1XHB6lflaNAzOjZrH/+F+IsWgMLTDrW7E+Fzt/JQbhfL+4q+dUY8rMQCrlFWNWZqFeBKXEs2I5MczqulrG80L5ZQjRmK+Pb/UYFvusLIZiAovR22Mh7RQTH4Vg4/P5cPvtt+O1117D0NAQZmdnC9Z+qbxHZm2Z9X+++2RWxkxkNZ4zS76uks1mYbVaC3LEiJBSMTa1DiHMCAFGHFcTLxvff2q/jMmaLRYLGhsb8Yd/+Id4/PHH0d7ejnA4LIUhMxGomMckQRAEQRAEQRBvL29atAGKG3LCsDAzMszqKVb/YvpT6tf8t8IImc/TRfVeMeaNMNYzXx+LhVNcqTdEsX4XOz+fQb7Yc/Ohrpfr1YBciBh4tblSD5iFXlPq+TSu68XcI7MyFosFTqcTa9euxRNPPIGZmRkpGBjfGQvt/0LW4mLeE2bhjMIzRT1mfD7VZ99Yhxoqabfb54i3RqFKfBd1GIUeIfaIvqn1VVRU4JOf/CQOHTqECxcuIBaLyXAoxpgUk9X3tSoSEQRBEARBEARx7WCLMf4sFgsXOywVVHIVw2cWytUO9Xg7xJ5SXgrztfVWz/H1LIwQby/qNtULwUyUWMg1oi2PxwOr1YpYLGYaJng9rktV0DATeVREiJPRw41zDpvNVnSnK9GOWl5N/m5sR9w3IdqInaJuu+02ZDIZHD58uEAUE303E5RFHeq24wRBEARBEARBvKWc5pzvMB68Ik8bI+If/qV4K0SBxdapbp87n2FYKl/MlfJmvDJK/fp/NbgeDWPi2nAl4mWpZ6XUtfl8Hul0WobpvJVczWfGzOttIZ5rRm8acVz1yhHn1ETExrJG0cjMQ87n86GlpQUzMzNob2/HzMyMDJ0yetypYrJZnh2CIAiCIAiCIK4Nb0q0Mf66fj3/Q59zjhUrVsDtdmN0dBThcLikkXi9jgO4su2NCWKhFAvfK1XemOtloddwzpHJZK7rdwdgvj14sfNmIorRy8YsB5halzH3lTiuijvq9cZQLLfbjerqarhcLly4cAHDw8NzQqxEeePuXGqfCYIgCIIgCIK4tiw6aYEwCq61kXUlRsU73/lOHDx4EKtWrYLL5VrQNS6X623L7bAQb6WysjI4nU7KN0FcNxgFA2BhO0mJv5zzAg+Qt0owWGxOn2LnjOeFGCI8YIzvRmOiX1He6EmjljHz9GOMQYSn5vN506Tmog81NTUoKyvD2NgY+vr6kM1m5TVmY1SFpnw+L8sTBEEQBEEQBHFtuWLLfyG/xhsNs2IIQ0NN7nm1sVgsWLNmDc6cOYNsNotAIGDqHWC8Zu/evaisrJRJPt8siwmBMuP222/H/v370dzcXHCcvG+IYpgJDfOVN/tcDKPYspCcNMbwoLcjPFGt2yh6mr2rjCKU+E8VM0QZVYBRv4swJ/H+yOfzBUmAzZIVi1w3AOSuU8IjJpPJzGnH2Gen04l4PI7h4WHpYSPGoIpCap+Nu0hdrfcdQRAEQRAEQRBvjkUlImaMBQEMvnXdIQiCIAiCIAiCIAiC+LVjKec8YDy4KNGGIAiCIAiCIAiCIAiCeHugxCgEQRAEQRAEQRAEQRDXISTaEARBEARBEARBEARBXIeQaEMQBEEQBEEQBEEQBHEdQqINQRAEQRAEQRAEQRDEdQiJNgRBEARBEARBEARBENchJNoQBEEQBEEQBEEQBEFch5BoQxAEQRAEQRAEQRAEcR1Cog1BEARBEARBEARBEMR1CIk2BEEQBEEQBEEQBEEQ1yH/D2H6pkip88OuAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(20, 20))\n",
-    "plt.title(sentence)\n",
-    "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
-    "plt.xticks([])\n",
-    "plt.yticks([])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('Boyis cheed iitrincy- tarisaing one', 0.3990435302257538)"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.predict_on_image(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[1, 28, 952], [92]]"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "experiment_config[\"train_args\"][\"input_shape\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "=========================================================================================================\n",
-      "Layer (type:depth-idx)                                  Output Shape              Param #\n",
-      "=========================================================================================================\n",
-      "├─Sequential: 1-1                                       [-1, 158, 1, 28, 6]       --\n",
-      "|    └─Unfold: 2-1                                      [-1, 168, 158]            --\n",
-      "|    └─Rearrange: 2-2                                   [-1, 158, 1, 28, 6]       --\n",
-      "├─Linear: 1-2                                           [-1, 158, 512]            86,528\n",
-      "├─PositionalEncoding: 1-3                               [-1, 158, 512]            --\n",
-      "|    └─Dropout: 2-3                                     [-1, 158, 512]            --\n",
-      "├─Embedding: 1-4                                        [-1, 92, 512]             41,984\n",
-      "├─PositionalEncoding: 1-5                               [-1, 92, 512]             --\n",
-      "|    └─Dropout: 2-4                                     [-1, 92, 512]             --\n",
-      "├─Transformer: 1-6                                      [-1, 92, 512]             --\n",
-      "|    └─Encoder: 2-5                                     [-1, 158, 512]            --\n",
-      "|    |    └─ModuleList: 3                               []                        --\n",
-      "|    |    |    └─EncoderLayer: 4-1                      [-1, 158, 512]            3,150,848\n",
-      "|    |    |    └─EncoderLayer: 4-2                      [-1, 158, 512]            3,150,848\n",
-      "|    |    |    └─EncoderLayer: 4-3                      [-1, 158, 512]            3,150,848\n",
-      "|    |    |    └─EncoderLayer: 4-4                      [-1, 158, 512]            3,150,848\n",
-      "|    |    └─LayerNorm: 3-1                              [-1, 158, 512]            1,024\n",
-      "|    └─Decoder: 2-6                                     [-1, 92, 512]             --\n",
-      "|    |    └─ModuleList: 3                               []                        --\n",
-      "|    |    |    └─DecoderLayer: 4-5                      [-1, 92, 512]             4,200,960\n",
-      "|    |    |    └─DecoderLayer: 4-6                      [-1, 92, 512]             4,200,960\n",
-      "|    |    |    └─DecoderLayer: 4-7                      [-1, 92, 512]             4,200,960\n",
-      "|    |    |    └─DecoderLayer: 4-8                      [-1, 92, 512]             4,200,960\n",
-      "|    |    └─LayerNorm: 3-2                              [-1, 92, 512]             1,024\n",
-      "├─Sequential: 1-7                                       [-1, 92, 82]              --\n",
-      "|    └─LayerNorm: 2-7                                   [-1, 92, 512]             1,024\n",
-      "|    └─Linear: 2-8                                      [-1, 92, 512]             262,656\n",
-      "|    └─GELU: 2-9                                        [-1, 92, 512]             --\n",
-      "|    └─Dropout: 2-10                                    [-1, 92, 512]             --\n",
-      "|    └─Linear: 2-11                                     [-1, 92, 82]              42,066\n",
-      "=========================================================================================================\n",
-      "Total params: 29,843,538\n",
-      "Trainable params: 29,843,538\n",
-      "Non-trainable params: 0\n",
-      "Total mult-adds (M): 118.22\n",
-      "=========================================================================================================\n",
-      "Input size (MB): 0.10\n",
-      "Forward/backward pass size (MB): 2.73\n",
-      "Params size (MB): 113.84\n",
-      "Estimated Total Size (MB): 116.68\n",
-      "=========================================================================================================\n"
+      "2020-11-18 20:34:49.381 | DEBUG    | text_recognizer.models.base:load_from_checkpoint:379 - Loading checkpoint...\n"
      ]
     }
    ],
    "source": [
-    "model.summary(experiment_config[\"train_args\"][\"input_shape\"], 4)"
+    "ckpt_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1116_082932/model/best.pt\"\n",
+    "model.load_from_checkpoint(ckpt_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
-    "t=[12,1,1,1,1,1,4,4,4,4,4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t[t!=79]"
+    "model.eval()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x = torch.arange(10)\n",
-    "value = 5\n",
-    "x = x[x!=value]"
+    "data, target = dataset[1]\n",
+    "sentence = convert_y_label_to_string(target, dataset) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([0, 1, 2, 3, 4, 6, 7, 8, 9])"
+       "torch.Size([98])"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x"
+    "target.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
-    "t = torch.rand(98)"
+    "data = data * (data > 0.1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(1.7656e-43)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t.cumprod(dim=0)[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
-    "pred_tokens = torch.Tensor([1,2,21,31, 89, 89])"
+    "from torchvision import transforms"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
-    "pred_tokens = torch.stack([pred_tokens, pred_tokens])"
+    "ra = transforms.RandomAffine((-1.1, 1.1), scale=(0.5, 1))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.,  2., 21., 31., 89., 89.],\n",
-       "        [ 1.,  2., 21., 31., 89., 89.]])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pred_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
-    "eos_token_index = torch.nonzero(\n",
-    "            pred_tokens == 89, as_tuple=False,\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n"
-     ]
-    }
-   ],
-   "source": [
-    "if eos_token_index.nelement():\n",
-    "    print(eos_token_index[0][0].item())"
+    "data = ra(data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
      "data": {
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       "text/plain": [
-       "tensor([[0, 4],\n",
-       "        [0, 5],\n",
-       "        [1, 4],\n",
-       "        [1, 5]])"
+       "<Figure size 4320x1440 with 1 Axes>"
       ]
      },
-     "execution_count": 32,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "eos_token_index"
+    "plt.figure(figsize=(60, 20))\n",
+    "plt.title(sentence)\n",
+    "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
+   "execution_count": 133,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "8"
+       "('and Came came into Mr. I. I. \"Amering whin<eos>', 0.32183724641799927)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "eos_token_index.nelement()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.models import accuracy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred = torch.Tensor([1,2,21,31, 80, 80]).unsqueeze(0)\n",
-    "target = torch.Tensor([1,2,1,31, 80, 80]).unsqueeze(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred = torch.stack([pred, pred])\n",
-    "target = torch.stack([target, target])"
+    "model.predict_on_image(data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
-    "target = torch.tensor([0, 1, 2, 3])\n",
-    "pred = torch.tensor([0, 2, 1, 3])"
+    "data, target = dataset[110]\n",
+    "sentence = convert_y_label_to_string(target, dataset) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.5"
+       "([], [])"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy(pred, target)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "acc = (target.argmax(-1) == pred.argmax(-1)).float()\n",
-    "\n",
-    "# return float(100 * acc.sum() / len(acc))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "tensor([[1.],\n",
-       "        [1.]])"
+       "<Figure size 1440x1440 with 1 Axes>"
       ]
      },
-     "execution_count": 54,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "acc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_acc = (pred == target).sum().item()/target.shape[-1]"
+    "plt.figure(figsize=(20, 20))\n",
+    "plt.title(sentence)\n",
+    "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3.3333333333333335"
+       "('Boyis cheed iitrincy- tarisaing one', 0.3990435302257538)"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "train_acc"
+    "model.predict_on_image(data)"
    ]
   },
   {
diff --git a/src/text_recognizer/datasets/transforms.py b/src/text_recognizer/datasets/transforms.py
index d1ca127..1ec23dc 100644
--- a/src/text_recognizer/datasets/transforms.py
+++ b/src/text_recognizer/datasets/transforms.py
@@ -4,7 +4,7 @@ from PIL import Image
 import torch
 from torch import Tensor
 import torch.nn.functional as F
-from torchvision.transforms import Compose, ToPILImage, ToTensor
+from torchvision.transforms import Compose, RandomAffine, ToTensor
 
 from text_recognizer.datasets.util import EmnistMapper
 
@@ -66,9 +66,14 @@ class AddTokens:
         return target
 
 
-class Whitening:
-    """Whitening of Tensor, i.e. set mean to zero and std to one."""
+class ApplyContrast:
+    """Sets everything below a threshold to zero, i.e. increase contrast."""
+
+    def __init__(self, low: float = 0.0, high: float = 0.25) -> None:
+        self.low = low
+        self.high = high
 
     def __call__(self, x: Tensor) -> Tensor:
-        """Apply the whitening."""
-        return (x - x.mean()) / x.std()
+        """Apply mask binary mask to input tensor."""
+        mask = x > np.random.RandomState().uniform(low=self.low, high=self.high)
+        return x * mask
diff --git a/src/text_recognizer/line_predictor.py b/src/text_recognizer/line_predictor.py
index 981e2c9..8e348fe 100644
--- a/src/text_recognizer/line_predictor.py
+++ b/src/text_recognizer/line_predictor.py
@@ -6,7 +6,7 @@ import numpy as np
 from torch import nn
 
 from text_recognizer import datasets, networks
-from text_recognizer.models import VisionTransformerModel
+from text_recognizer.models import TransformerModel
 from text_recognizer.util import read_image
 
 
@@ -16,7 +16,7 @@ class LinePredictor:
     def __init__(self, dataset: str, network_fn: str) -> None:
         network_fn = getattr(networks, network_fn)
         dataset = getattr(datasets, dataset)
-        self.model = VisionTransformerModel(network_fn=network_fn, dataset=dataset)
+        self.model = TransformerModel(network_fn=network_fn, dataset=dataset)
         self.model.eval()
 
     def predict(self, image_or_filename: Union[np.ndarray, str]) -> Tuple[str, float]:
diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py
index 53340f1..bf89404 100644
--- a/src/text_recognizer/models/__init__.py
+++ b/src/text_recognizer/models/__init__.py
@@ -2,16 +2,11 @@
 from .base import Model
 from .character_model import CharacterModel
 from .crnn_model import CRNNModel
-from .metrics import accuracy, accuracy_ignore_pad, cer, wer
 from .transformer_model import TransformerModel
 
 __all__ = [
-    "accuracy",
-    "accuracy_ignore_pad",
-    "cer",
     "CharacterModel",
     "CRNNModel",
     "Model",
     "TransformerModel",
-    "wer",
 ]
diff --git a/src/text_recognizer/models/metrics.py b/src/text_recognizer/models/metrics.py
deleted file mode 100644
index af9adb5..0000000
--- a/src/text_recognizer/models/metrics.py
+++ /dev/null
@@ -1,107 +0,0 @@
-"""Utility functions for models."""
-import Levenshtein as Lev
-import torch
-from torch import Tensor
-
-from text_recognizer.networks import greedy_decoder
-
-
-def accuracy_ignore_pad(
-    output: Tensor,
-    target: Tensor,
-    pad_index: int = 79,
-    eos_index: int = 81,
-    seq_len: int = 97,
-) -> float:
-    """Sets all predictions after eos to pad."""
-    start_indices = torch.nonzero(target == eos_index, as_tuple=False).squeeze(1)
-    end_indices = torch.arange(seq_len, target.shape[0] + 1, seq_len)
-    for start, stop in zip(start_indices, end_indices):
-        output[start + 1 : stop] = pad_index
-
-    return accuracy(output, target)
-
-
-def accuracy(outputs: Tensor, labels: Tensor,) -> float:
-    """Computes the accuracy.
-
-    Args:
-        outputs (Tensor): The output from the network.
-        labels (Tensor): Ground truth labels.
-
-    Returns:
-        float: The accuracy for the batch.
-
-    """
-
-    _, predicted = torch.max(outputs, dim=-1)
-
-    acc = (predicted == labels).sum().float() / labels.shape[0]
-    acc = acc.item()
-    return acc
-
-
-def cer(outputs: Tensor, targets: Tensor) -> float:
-    """Computes the character error rate.
-
-    Args:
-        outputs (Tensor): The output from the network.
-        targets (Tensor): Ground truth labels.
-
-    Returns:
-        float: The cer for the batch.
-
-    """
-    target_lengths = torch.full(
-        size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long,
-    )
-    decoded_predictions, decoded_targets = greedy_decoder(
-        outputs, targets, target_lengths
-    )
-
-    lev_dist = 0
-
-    for prediction, target in zip(decoded_predictions, decoded_targets):
-        prediction = "".join(prediction)
-        target = "".join(target)
-        prediction, target = (
-            prediction.replace(" ", ""),
-            target.replace(" ", ""),
-        )
-        lev_dist += Lev.distance(prediction, target)
-    return lev_dist / len(decoded_predictions)
-
-
-def wer(outputs: Tensor, targets: Tensor) -> float:
-    """Computes the Word error rate.
-
-    Args:
-        outputs (Tensor): The output from the network.
-        targets (Tensor): Ground truth labels.
-
-    Returns:
-        float: The wer for the batch.
-
-    """
-    target_lengths = torch.full(
-        size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long,
-    )
-    decoded_predictions, decoded_targets = greedy_decoder(
-        outputs, targets, target_lengths
-    )
-
-    lev_dist = 0
-
-    for prediction, target in zip(decoded_predictions, decoded_targets):
-        prediction = "".join(prediction)
-        target = "".join(target)
-
-        b = set(prediction.split() + target.split())
-        word2char = dict(zip(b, range(len(b))))
-
-        w1 = [chr(word2char[w]) for w in prediction.split()]
-        w2 = [chr(word2char[w]) for w in target.split()]
-
-        lev_dist += Lev.distance("".join(w1), "".join(w2))
-
-    return lev_dist / len(decoded_predictions)
diff --git a/src/text_recognizer/networks/__init__.py b/src/text_recognizer/networks/__init__.py
index 2cc1137..078d771 100644
--- a/src/text_recognizer/networks/__init__.py
+++ b/src/text_recognizer/networks/__init__.py
@@ -4,6 +4,7 @@ from .crnn import ConvolutionalRecurrentNetwork
 from .ctc import greedy_decoder
 from .densenet import DenseNet
 from .lenet import LeNet
+from .metrics import accuracy, accuracy_ignore_pad, cer, wer
 from .mlp import MLP
 from .residual_network import ResidualNetwork, ResidualNetworkEncoder
 from .sparse_mlp import SparseMLP
@@ -12,6 +13,9 @@ from .util import sliding_window
 from .wide_resnet import WideResidualNetwork
 
 __all__ = [
+    "accuracy",
+    "accuracy_ignore_pad",
+    "cer",
     "CNNTransformer",
     "ConvolutionalRecurrentNetwork",
     "DenseNet",
@@ -23,5 +27,6 @@ __all__ = [
     "sliding_window",
     "Transformer",
     "SparseMLP",
+    "wer",
     "WideResidualNetwork",
 ]
diff --git a/src/text_recognizer/networks/metrics.py b/src/text_recognizer/networks/metrics.py
new file mode 100644
index 0000000..af9adb5
--- /dev/null
+++ b/src/text_recognizer/networks/metrics.py
@@ -0,0 +1,107 @@
+"""Utility functions for models."""
+import Levenshtein as Lev
+import torch
+from torch import Tensor
+
+from text_recognizer.networks import greedy_decoder
+
+
+def accuracy_ignore_pad(
+    output: Tensor,
+    target: Tensor,
+    pad_index: int = 79,
+    eos_index: int = 81,
+    seq_len: int = 97,
+) -> float:
+    """Sets all predictions after eos to pad."""
+    start_indices = torch.nonzero(target == eos_index, as_tuple=False).squeeze(1)
+    end_indices = torch.arange(seq_len, target.shape[0] + 1, seq_len)
+    for start, stop in zip(start_indices, end_indices):
+        output[start + 1 : stop] = pad_index
+
+    return accuracy(output, target)
+
+
+def accuracy(outputs: Tensor, labels: Tensor,) -> float:
+    """Computes the accuracy.
+
+    Args:
+        outputs (Tensor): The output from the network.
+        labels (Tensor): Ground truth labels.
+
+    Returns:
+        float: The accuracy for the batch.
+
+    """
+
+    _, predicted = torch.max(outputs, dim=-1)
+
+    acc = (predicted == labels).sum().float() / labels.shape[0]
+    acc = acc.item()
+    return acc
+
+
+def cer(outputs: Tensor, targets: Tensor) -> float:
+    """Computes the character error rate.
+
+    Args:
+        outputs (Tensor): The output from the network.
+        targets (Tensor): Ground truth labels.
+
+    Returns:
+        float: The cer for the batch.
+
+    """
+    target_lengths = torch.full(
+        size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long,
+    )
+    decoded_predictions, decoded_targets = greedy_decoder(
+        outputs, targets, target_lengths
+    )
+
+    lev_dist = 0
+
+    for prediction, target in zip(decoded_predictions, decoded_targets):
+        prediction = "".join(prediction)
+        target = "".join(target)
+        prediction, target = (
+            prediction.replace(" ", ""),
+            target.replace(" ", ""),
+        )
+        lev_dist += Lev.distance(prediction, target)
+    return lev_dist / len(decoded_predictions)
+
+
+def wer(outputs: Tensor, targets: Tensor) -> float:
+    """Computes the Word error rate.
+
+    Args:
+        outputs (Tensor): The output from the network.
+        targets (Tensor): Ground truth labels.
+
+    Returns:
+        float: The wer for the batch.
+
+    """
+    target_lengths = torch.full(
+        size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long,
+    )
+    decoded_predictions, decoded_targets = greedy_decoder(
+        outputs, targets, target_lengths
+    )
+
+    lev_dist = 0
+
+    for prediction, target in zip(decoded_predictions, decoded_targets):
+        prediction = "".join(prediction)
+        target = "".join(target)
+
+        b = set(prediction.split() + target.split())
+        word2char = dict(zip(b, range(len(b))))
+
+        w1 = [chr(word2char[w]) for w in prediction.split()]
+        w2 = [chr(word2char[w]) for w in target.split()]
+
+        lev_dist += Lev.distance("".join(w1), "".join(w2))
+
+    return lev_dist / len(decoded_predictions)
diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py
index 55a9572..a883b45 100644
--- a/src/training/run_experiment.py
+++ b/src/training/run_experiment.py
@@ -21,8 +21,9 @@ from training.trainer.train import Trainer
 import wandb
 import yaml
 
-
+import text_recognizer.models
 from text_recognizer.models import Model
+import text_recognizer.networks
 from text_recognizer.networks.loss import loss as custom_loss_module
 
 EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments"
@@ -77,13 +78,12 @@ def _load_modules_and_arguments(experiment_config: Dict,) -> Tuple[Callable, Dic
     dataset_ = dataset_args["type"]
 
     # Import the model module and model arguments.
-    models_module = importlib.import_module("text_recognizer.models")
-    model_class_ = getattr(models_module, experiment_config["model"])
+    model_class_ = getattr(text_recognizer.models, experiment_config["model"])
 
     # Import metrics.
     metric_fns_ = (
         {
-            metric: getattr(models_module, metric)
+            metric: getattr(text_recognizer.networks, metric)
             for metric in experiment_config["metrics"]
         }
         if experiment_config["metrics"] is not None
diff --git a/src/training/trainer/train.py b/src/training/trainer/train.py
index 223d9c6..8ae994a 100644
--- a/src/training/trainer/train.py
+++ b/src/training/trainer/train.py
@@ -3,6 +3,7 @@
 from pathlib import Path
 import time
 from typing import Dict, List, Optional, Tuple, Type
+import warnings
 
 from einops import rearrange
 from loguru import logger
@@ -23,6 +24,9 @@ torch.manual_seed(4711)
 torch.cuda.manual_seed(4711)
 
 
+warnings.filterwarnings("ignore")
+
+
 class Trainer:
     """Trainer for training PyTorch models."""
 
-- 
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