
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__				:      List[Any]    		=     {
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__				:      List[str]    		=     ["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__				:      Any    		=     [
          """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MarkupLMForQuestionAnswering""",
          """MarkupLMForSequenceClassification""",
          """MarkupLMForTokenClassification""",
          """MarkupLMModel""",
          """MarkupLMPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
      from .feature_extraction_markuplm import MarkupLMFeatureExtractor
      from .processing_markuplm import MarkupLMProcessor
      from .tokenization_markuplm import MarkupLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )


else:
      import sys

      a__				:      int    		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure)


"""simple docstring"""
def 					a__ ( a :					str		):

  """simple docstring"""







  _snake_case  :    Tuple            = word.upper()
  _snake_case  :    List[Any]            = 0
  _snake_case  :    Any            = {}
  _snake_case  :    List[str]            = []

  for letter in word:
    if letter not in letter_nums:
      _snake_case  :    Optional[int]            = str(a		)
      next_num += 1
    word_pattern.append(letter_nums[letter]		)
  return ".".join(a		)


if __name__ == "__main__":
    import pprint
    import time

    _a   :      str       =				time.time()
    with open("""dictionary.txt""") as in_file:
        _a   :      Optional[int]       =				in_file.read().splitlines()

    _a   :      dict       =				{}
    for word in word_list:
        _a   :      Optional[Any]       =				get_word_pattern(word)
        if pattern in all_patterns:
            all_patterns[pattern].append(word)
        else:
            _a   :      List[Any]       =				[word]

    with open("""word_patterns.txt""", """w""") as out_file:
        out_file.write(pprint.pformat(all_patterns))

    _a   :      List[Any]       =				round(time.time() - start_time, 2)
    print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
    # Done!  9,581 word patterns found in 0.58 seconds.

