
from typing import TYPE_CHECKING

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


_lowerCamelCase					   ={
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase					   =["""SqueezeBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase					   =[
          """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SqueezeBertForMaskedLM""",
          """SqueezeBertForMultipleChoice""",
          """SqueezeBertForQuestionAnswering""",
          """SqueezeBertForSequenceClassification""",
          """SqueezeBertForTokenClassification""",
          """SqueezeBertModel""",
          """SqueezeBertModule""",
          """SqueezeBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_squeezebert import (
          SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SqueezeBertConfig,
          SqueezeBertOnnxConfig,
      )
      from .tokenization_squeezebert import SqueezeBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_squeezebert import (
                SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SqueezeBertForMaskedLM,
                SqueezeBertForMultipleChoice,
                SqueezeBertForQuestionAnswering,
                SqueezeBertForSequenceClassification,
                SqueezeBertForTokenClassification,
                SqueezeBertModel,
                SqueezeBertModule,
                SqueezeBertPreTrainedModel,
            )

else:
      import sys

      _lowerCamelCase					   =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






from __future__ import annotations

import collections
import pprint
from pathlib import Path





def     _a					(						lowerCamelCase							):
 return "".join(sorted(lowerCamelCase							)							)





def     _a					(						lowerCamelCase							):
 return word_by_signature[signature(lowerCamelCase							)]


_lowerCamelCase					   =Path(__file__).parent.joinpath("""words.txt""").read_text(encoding="""utf-8""")
_lowerCamelCase					   =sorted({word.strip().lower() for word in data.splitlines()})

_lowerCamelCase					   =collections.defaultdict(list)
for word in word_list:
      word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
      _lowerCamelCase					   ={word: anagram(word) for word in word_list if len(anagram(word)) > 1}

      with open("""anagrams.txt""", """w""") as file:
            file.write("""all_anagrams = \n """)
            file.write(pprint.pformat(all_anagrams))




