1 files changed, 0 insertions, 265 deletions
diff --git a/text_recognizer/data/utils/build_transitions.py b/text_recognizer/data/utils/build_transitions.py
deleted file mode 100644
index 65485f0..0000000
--- a/text_recognizer/data/utils/build_transitions.py
+++ /dev/null
@@ -1,265 +0,0 @@
-"""Builds transition graph.
-
-Most code stolen from here:
-    https://github.com/facebookresearch/gtn_applications/blob/master/scripts/build_transitions.py
-"""
-
-import collections
-import itertools
-from pathlib import Path
-from typing import Any, Dict, List, Optional
-
-import click
-import gtn
-from loguru import logger
-
-
-START_IDX = -1
-END_IDX = -2
-WORDSEP = "▁"
-
-
-def build_graph(ngrams: List, disable_backoff: bool = False) -> gtn.Graph:
-    """Returns a gtn Graph based on the ngrams."""
-    graph = gtn.Graph(False)
-    ngram = len(ngrams)
-    state_to_node = {}
-
-    def get_node(state: Optional[List]) -> Any:
-        node = state_to_node.get(state, None)
-
-        if node is not None:
-            return node
-
-        start = state == tuple([START_IDX]) if ngram > 1 else True
-        end = state == tuple([END_IDX]) if ngram > 1 else True
-        node = graph.add_node(start, end)
-        state_to_node[state] = node
-
-        if not disable_backoff and not end:
-            # Add back off when adding node.
-            for n in range(1, len(state) + 1):
-                backoff_node = state_to_node.get(state[n:], None)
-
-                # Epsilon transition to the back-off state.
-                if backoff_node is not None:
-                    graph.add_arc(node, backoff_node, gtn.epsilon)
-                    break
-        return node
-
-    for grams in ngrams:
-        for gram in grams:
-            istate, ostate = gram[:-1], gram[len(gram) - ngram + 1 :]
-            inode = get_node(istate)
-
-            if END_IDX not in gram[1:] and gram[1:] not in state_to_node:
-                raise ValueError(
-                    "Ill formed counts: if (x, y_1, ..., y_{n-1}) is above"
-                    "the n-gram threshold, then (y_1, ..., y_{n-1}) must be"
-                    "above the (n-1)-gram threshold"
-                )
-
-            if END_IDX in ostate:
-                # Merge all state having </s> into one as final graph generated
-                # will be similar.
-                ostate = tuple([END_IDX])
-
-            onode = get_node(ostate)
-            # p(gram[-1] | gram[:-1])
-            graph.add_arc(
-                inode, onode, gtn.epsilon if gram[-1] == END_IDX else gram[-1]
-            )
-    return graph
-
-
-def count_ngrams(lines: List, ngram: List, tokens_to_index: Dict) -> List:
-    """Counts the number of ngrams."""
-    counts = [collections.Counter() for _ in range(ngram)]
-    for line in lines:
-        # Prepend implicit start token.
-        token_line = [START_IDX]
-        for t in line:
-            token_line.append(tokens_to_index[t])
-        token_line.append(END_IDX)
-        for n, counter in enumerate(counts):
-            start_offset = n == 0
-            end_offset = ngram == 1
-            for e in range(n + start_offset, len(token_line) - end_offset):
-                counter[tuple(token_line[e - n : e + 1])] += 1
-
-    return counts
-
-
-def prune_ngrams(ngrams: List, prune: List) -> List:
-    """Prunes ngrams."""
-    pruned_ngrams = []
-    for n, grams in enumerate(ngrams):
-        grams = grams.most_common()
-        pruned_grams = [gram for gram, c in grams if c > prune[n]]
-        pruned_ngrams.append(pruned_grams)
-    return pruned_ngrams
-
-
-def add_blank_grams(pruned_ngrams: List, num_tokens: int, blank: str) -> List:
-    """Adds blank token to grams."""
-    all_grams = [gram for grams in pruned_ngrams for gram in grams]
-    maxorder = len(pruned_ngrams)
-    blank_grams = {}
-    if blank == "forced":
-        pruned_ngrams = [pruned_ngrams[0] if i == 0 else [] for i in range(maxorder)]
-    pruned_ngrams[0].append(tuple([num_tokens]))
-    blank_grams[tuple([num_tokens])] = True
-
-    for gram in all_grams:
-        # Iterate over all possibilities by using a vector of 0s, 1s to
-        # denote whether a blank is being used at each position.
-        if blank == "optional":
-            # Given a gram ab.. if order n, we have n + 1 positions
-            # available whether to use blank or not.
-            onehot_vectors = itertools.product([0, 1], repeat=len(gram) + 1)
-        elif blank == "forced":
-            # Must include a blank token in between.
-            onehot_vectors = [[1] * (len(gram) + 1)]
-        else:
-            raise ValueError(
-                "Invalid value specificed for blank. Must be in |optional|forced|none|"
-            )
-
-    for j in onehot_vectors:
-        new_array = []
-        for idx, oz in enumerate(j[:-1]):
-            if oz == 1 and gram[idx] != START_IDX:
-                new_array.append(num_tokens)
-            new_array.append(gram[idx])
-        if j[-1] == 1 and gram[-1] != END_IDX:
-            new_array.append(num_tokens)
-        for n in range(maxorder):
-            for e in range(n, len(new_array)):
-                cur_gram = tuple(new_array[e - n : e + 1])
-                if num_tokens in cur_gram and cur_gram not in blank_grams:
-                    pruned_ngrams[n].append(cur_gram)
-                    blank_grams[cur_gram] = True
-
-    return pruned_ngrams
-
-
-def add_self_loops(pruned_ngrams: List) -> List:
-    """Adds self loops to the ngrams."""
-    maxorder = len(pruned_ngrams)
-
-    # Use dict for fast search.
-    all_grams = set([gram for grams in pruned_ngrams for gram in grams])
-    for o in range(1, maxorder):
-        for gram in pruned_ngrams[o - 1]:
-            # Repeat one of the tokens.
-            for pos in range(len(gram)):
-                if gram[pos] == START_IDX or gram[pos] == END_IDX:
-                    continue
-                new_gram = gram[:pos] + (gram[pos],) + gram[pos:]
-
-                if new_gram not in all_grams:
-                    pruned_ngrams[o].append(new_gram)
-                    all_grams.add(new_gram)
-    return pruned_ngrams
-
-
-def parse_lines(lines: List, lexicon: Path) -> List:
-    """Parses lines with a lexicon."""
-    with (lexicon).open("r") as f:
-        lex = (line.strip().split() for line in f)
-        lex = {line[0]: line[1:] for line in lex}
-    return [[t for w in line.split(WORDSEP) for t in lex[w]] for line in lines if line]
-
-
-@click.command()
-@click.option("--data_dir", type=str, default=None, help="Path to dataset root.")
-@click.option(
-    "--tokens",
-    type=str,
-    default="iamdb_1kwp_tokens_1000.txt",
-    help="Path to token list (in order used with training).",
-)
-@click.option(
-    "--lexicon", type=str, default="iamdb_1kwp_lex_1000.txt", help="Path to lexicon"
-)
-@click.option(
-    "--prune",
-    nargs=2,
-    type=int,
-    help="Threshold values for prune unigrams, bigrams, etc.",
-)
-@click.option(
-    "--blank",
-    default=click.Choice(["none", "optional", "forced"]),
-    help="Specifies the usage of blank token"
-    "'none' - do not use blank token "
-    "'optional' - allow an optional blank inbetween tokens"
-    "'forced' - force a blank inbetween tokens (also referred to as garbage token)",
-)
-@click.option("--self_loops", is_flag=True, help="Add self loops for tokens")
-@click.option("--disable_backoff", is_flag=True, help="Disable backoff transitions")
-@click.option("--save_path", default=None, help="Path to save transition graph.")
-def cli(
-    data_dir: str,
-    tokens: str,
-    lexicon: str,
-    prune: List[int],
-    blank: str,
-    self_loops: bool,
-    disable_backoff: bool,
-    save_path: str,
-) -> None:
-    """CLI for creating the transitions."""
-    logger.info(f"Building {len(prune)}-gram transition models.")
-
-    if data_dir is None:
-        data_dir = (
-            Path(__file__).resolve().parents[3] / "data" / "processed" / "iam_lines"
-        )
-        logger.debug(f"Using data dir: {data_dir}")
-        if not data_dir.exists():
-            raise RuntimeError(f"Could not locate iamdb directory at {data_dir}")
-    else:
-        data_dir = Path(data_dir)
-
-    # Build table of counts and the back-off if below threshold.
-    with (data_dir / "train.txt").open("r") as f:
-        lines = [line.strip() for line in f]
-
-    with (data_dir / tokens).open("r") as f:
-        tokens = [line.strip() for line in f]
-
-    if lexicon is not None:
-        lexicon = data_dir / lexicon
-        lines = parse_lines(lines, lexicon)
-
-    tokens_to_idx = {t: e for e, t in enumerate(tokens)}
-
-    ngram = len(prune)
-
-    logger.info("Counting data...")
-    ngrams = count_ngrams(lines, ngram, tokens_to_idx)
-
-    pruned_ngrams = prune_ngrams(ngrams, prune)
-
-    for n in range(ngram):
-        logger.info(f"Kept {len(pruned_ngrams[n])} of {len(ngrams[n])} {n + 1}-grams")
-
-    if blank == "none":
-        pruned_ngrams = add_blank_grams(pruned_ngrams, len(tokens_to_idx), blank)
-
-    if self_loops:
-        pruned_ngrams = add_self_loops(pruned_ngrams)
-
-    logger.info("Building graph from pruned ngrams...")
-    graph = build_graph(pruned_ngrams, disable_backoff)
-    logger.info(f"Graph has {graph.num_arcs()} arcs and {graph.num_nodes()} nodes.")
-
-    save_path = str(data_dir / save_path)
-
-    logger.info(f"Saving graph to {save_path}")
-    gtn.save(save_path, graph)
-
-
-if __name__ == "__main__":
-    cli()