
"""simple docstring"""
import copy

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


_a   :      List[str]       =				logging.get_logger(__name__)

_a   :      List[Any]       =				{}






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Any	=       """fsmt_decoder"""
    def __init__(     self    ,     snake_case_=0    ,     snake_case_=0  ):
      super().__init__()
      _snake_case  :    Tuple            = vocab_size
      _snake_case  :    List[Any]            = bos_token_id






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			List[str]	=       """fsmt"""
    __lowercase				:			Optional[Any]	=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
    def __init__(     self    ,     snake_case_=["en", "de"]    ,     snake_case_=4_20_24    ,     snake_case_=4_20_24    ,     snake_case_="relu"    ,     snake_case_=10_24    ,     snake_case_=2_00    ,     snake_case_=10_24    ,     snake_case_=40_96    ,     snake_case_=12    ,     snake_case_=16    ,     snake_case_=0.0    ,     snake_case_=40_96    ,     snake_case_=12    ,     snake_case_=16    ,     snake_case_=0.0    ,     snake_case_=0.0    ,     snake_case_=0.1    ,     snake_case_=0.0    ,     snake_case_=0.02    ,     snake_case_=2    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=False    ,     snake_case_=5    ,     snake_case_=1.0    ,     snake_case_=False    ,     snake_case_=True    ,     snake_case_=1    ,     snake_case_=0    ,     snake_case_=2    ,     snake_case_=2    ,     **snake_case_    ,     ):
      _snake_case  :    str            = langs
      _snake_case  :    Tuple            = src_vocab_size
      _snake_case  :    Tuple            = tgt_vocab_size
      _snake_case  :    Optional[Any]            = d_model  # encoder_embed_dim and decoder_embed_dim

      _snake_case  :    Any            = encoder_ffn_dim
      _snake_case  :    Optional[int]            = encoder_layers
      _snake_case  :    Union[str, Any]            = encoder_attention_heads
      _snake_case  :    List[Any]            = encoder_layerdrop
      _snake_case  :    List[Any]            = decoder_layerdrop
      _snake_case  :    Optional[Any]            = decoder_ffn_dim
      _snake_case  :    Tuple            = decoder_layers
      _snake_case  :    Tuple            = decoder_attention_heads
      _snake_case  :    str            = max_position_embeddings
      _snake_case  :    List[Any]            = init_std  # Normal(0, this parameter)
      _snake_case  :    Optional[Any]            = activation_function

      _snake_case  :    int            = DecoderConfig(vocab_size=snake_case_    ,     bos_token_id=snake_case_  )
      if "decoder" in common_kwargs:
        del common_kwargs["decoder"]

      _snake_case  :    List[str]            = scale_embedding  # scale factor will be sqrt(d_model) if True

      # 3 Types of Dropout
      _snake_case  :    Any            = attention_dropout
      _snake_case  :    Optional[int]            = activation_dropout
      _snake_case  :    Optional[Any]            = dropout

      _snake_case  :    List[Any]            = use_cache
      super().__init__(
          pad_token_id=snake_case_    ,     bos_token_id=snake_case_    ,     eos_token_id=snake_case_    ,     decoder_start_token_id=snake_case_    ,     is_encoder_decoder=snake_case_    ,     tie_word_embeddings=snake_case_    ,     forced_eos_token_id=snake_case_    ,     max_length=snake_case_    ,     num_beams=snake_case_    ,     length_penalty=snake_case_    ,     early_stopping=snake_case_    ,     **snake_case_    ,     )


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = copy.deepcopy(self.__dict__  )
      _snake_case  :    str            = self.decoder.to_dict()
      _snake_case  :    int            = self.__class__.model_type
      return output



"""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.

