


"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}






class 						_A  (  __SCREAMING_SNAKE_CASE  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       """megatron-bert"""






    def __init__(					self				:    str				, __UpperCAmelCase				:    str=29056				, __UpperCAmelCase				:    Union[str, Any]=1024				, __UpperCAmelCase				:    Union[str, Any]=24				, __UpperCAmelCase				:    Dict=16				, __UpperCAmelCase				:    Dict=4096				, __UpperCAmelCase				:    Dict="gelu"				, __UpperCAmelCase				:    List[Any]=0.1				, __UpperCAmelCase				:    str=0.1				, __UpperCAmelCase				:    Union[str, Any]=512				, __UpperCAmelCase				:    Tuple=2				, __UpperCAmelCase				:    Any=0.02				, __UpperCAmelCase				:    List[str]=1e-12				, __UpperCAmelCase				:    Union[str, Any]=0				, __UpperCAmelCase				:    Union[str, Any]="absolute"				, __UpperCAmelCase				:    List[str]=True				, **__UpperCAmelCase				:    List[str]				, ):
      super().__init__(pad_token_id=__UpperCAmelCase				, **__UpperCAmelCase)

      a						:		List[Any]      							=			vocab_size
      a						:		List[str]      							=			hidden_size
      a						:		Tuple      							=			num_hidden_layers
      a						:		Optional[int]      							=			num_attention_heads
      a						:		Tuple      							=			hidden_act
      a						:		Union[str, Any]      							=			intermediate_size
      a						:		List[Any]      							=			hidden_dropout_prob
      a						:		Tuple      							=			attention_probs_dropout_prob
      a						:		int      							=			max_position_embeddings
      a						:		Tuple      							=			type_vocab_size
      a						:		str      							=			initializer_range
      a						:		int      							=			layer_norm_eps
      a						:		Optional[Any]      							=			position_embedding_type
      a						:		Optional[int]      							=			use_cache








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))




