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"""Emnist mapping."""
from typing import List, Optional, Sequence, Union
import torch
from torch import Tensor
from text_recognizer.data.emnist import emnist_mapping
from text_recognizer.data.mappings.base import AbstractMapping
class EmnistMapping(AbstractMapping):
"""Mapping for EMNIST labels."""
def __init__(
self, extra_symbols: Optional[Sequence[str]] = None, lower: bool = True
) -> None:
self.extra_symbols = set(extra_symbols) if extra_symbols is not None else None
self.mapping, self.inverse_mapping, self.input_size = emnist_mapping(
self.extra_symbols
)
if lower:
self._to_lower()
super().__init__(self.input_size, self.mapping, self.inverse_mapping)
def _to_lower(self) -> None:
"""Converts mapping to lowercase letters only."""
def _filter(x: int) -> int:
if 40 <= x:
return x - 26
return x
self.inverse_mapping = {v: _filter(k) for k, v in enumerate(self.mapping)}
self.mapping = [c for c in self.mapping if not c.isupper()]
def get_token(self, index: Union[int, Tensor]) -> str:
"""Returns token for index value."""
if (index := int(index)) <= len(self.mapping):
return self.mapping[index]
raise KeyError(f"Index ({index}) not in mapping.")
def get_index(self, token: str) -> Tensor:
"""Returns index value of token."""
if token in self.inverse_mapping:
return torch.LongTensor([self.inverse_mapping[token]])
raise KeyError(f"Token ({token}) not found in inverse mapping.")
def get_text(self, indices: Union[List[int], Tensor]) -> str:
"""Returns the text from a list of indices."""
if isinstance(indices, Tensor):
indices = indices.tolist()
return "".join([self.mapping[index] for index in indices])
def get_indices(self, text: str) -> Tensor:
"""Returns tensor of indices for a string."""
return Tensor([self.inverse_mapping[token] for token in text])
def __getitem__(self, x: Union[int, Tensor]) -> str:
"""Returns text for a list of indices."""
return self.get_token(x)
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