

from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						List[str]         =    '''T5Config'''






class  UpperCAmelCase__	( a__ ):






  """simple docstring"""


  a     =      """mt5"""
  a     =      MTaConfig






class  UpperCAmelCase__	( a__ ):






  """simple docstring"""


  a     =      """mt5"""
  a     =      MTaConfig






class  UpperCAmelCase__	( a__ ):






  """simple docstring"""


  a     =      """mt5"""
  a     =      MTaConfig





from __future__ import annotations

import collections
import pprint
from pathlib import Path
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return "".join(sorted(_A       )       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return word_by_signature[signature(_A       )]


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

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

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Tuple         =    {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))


