





def     A__					(						SCREAMING_SNAKE_CASE_      :			str	)     ->     str:






   """simple docstring"""







   _UpperCAmelCase											=      word.upper()
   _UpperCAmelCase											=      0
   _UpperCAmelCase											=      {}
   _UpperCAmelCase											=      []

   for letter in word:
      if letter not in letter_nums:
         _UpperCAmelCase											=      str(SCREAMING_SNAKE_CASE_	)
         next_num += 1
      word_pattern.append(letter_nums[letter]	)
   return ".".join(SCREAMING_SNAKE_CASE_	)


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

   UpperCAmelCase_											=  time.time()
   with open("dictionary.txt") as in_file:
      UpperCAmelCase_											=  in_file.read().splitlines()

   UpperCAmelCase_											=  {}
   for word in word_list:
      UpperCAmelCase_											=  get_word_pattern(word)
      if pattern in all_patterns:
         all_patterns[pattern].append(word)
      else:
         UpperCAmelCase_											=  [word]

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

   UpperCAmelCase_											=  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.






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


UpperCAmelCase_											=  logging.get_logger(__name__)

UpperCAmelCase_											=  {
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class  __UpperCamelCase						(       A__						):
     __A  :     Tuple            =      """splinter"""





     def __init__(    self							,       _UpperCamelCase=30522							,       _UpperCamelCase=768							,       _UpperCamelCase=12							,       _UpperCamelCase=12							,       _UpperCamelCase=3072							,       _UpperCamelCase="gelu"							,       _UpperCamelCase=0.1							,       _UpperCamelCase=0.1							,       _UpperCamelCase=512							,       _UpperCamelCase=2							,       _UpperCamelCase=0.02							,       _UpperCamelCase=1e-12							,       _UpperCamelCase=True							,       _UpperCamelCase=0							,       _UpperCamelCase=104							,       **_UpperCamelCase							,       ):
        super().__init__(pad_token_id=_UpperCamelCase							,       **_UpperCamelCase				)

        _UpperCAmelCase											=      vocab_size
        _UpperCAmelCase											=      max_position_embeddings
        _UpperCAmelCase											=      hidden_size
        _UpperCAmelCase											=      num_hidden_layers
        _UpperCAmelCase											=      num_attention_heads
        _UpperCAmelCase											=      intermediate_size
        _UpperCAmelCase											=      hidden_act
        _UpperCAmelCase											=      hidden_dropout_prob
        _UpperCAmelCase											=      attention_probs_dropout_prob
        _UpperCAmelCase											=      initializer_range
        _UpperCAmelCase											=      type_vocab_size
        _UpperCAmelCase											=      layer_norm_eps
        _UpperCAmelCase											=      use_cache
        _UpperCAmelCase											=      question_token_id