


"""simple docstring"""






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


UpperCamelCase__							:Any       =  logging.get_logger(__name__)

UpperCamelCase__							:List[Any]       =  {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       "nystromformer"
		def __init__(   self				,							SCREAMING_SNAKE_CASE__=3_00_00				,							SCREAMING_SNAKE_CASE__=7_68				,							SCREAMING_SNAKE_CASE__=12				,							SCREAMING_SNAKE_CASE__=12				,							SCREAMING_SNAKE_CASE__=30_72				,							SCREAMING_SNAKE_CASE__="gelu_new"				,							SCREAMING_SNAKE_CASE__=0.1				,							SCREAMING_SNAKE_CASE__=0.1				,							SCREAMING_SNAKE_CASE__=5_10				,							SCREAMING_SNAKE_CASE__=2				,							SCREAMING_SNAKE_CASE__=64				,							SCREAMING_SNAKE_CASE__=64				,							SCREAMING_SNAKE_CASE__=65				,							SCREAMING_SNAKE_CASE__=False				,							SCREAMING_SNAKE_CASE__=0.0_2				,							SCREAMING_SNAKE_CASE__=1E-5				,							SCREAMING_SNAKE_CASE__=1				,							SCREAMING_SNAKE_CASE__=0				,							SCREAMING_SNAKE_CASE__=2				,							**SCREAMING_SNAKE_CASE__				,							)			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  vocab_size
					_UpperCamelCase				:Optional[int]				    =  max_position_embeddings
					_UpperCamelCase				:Any				    =  hidden_size
					_UpperCamelCase				:Optional[Any]				    =  num_hidden_layers
					_UpperCamelCase				:Union[str, Any]				    =  num_attention_heads
					_UpperCamelCase				:Dict				    =  intermediate_size
					_UpperCamelCase				:Dict				    =  hidden_act
					_UpperCamelCase				:str				    =  hidden_dropout_prob
					_UpperCamelCase				:Optional[int]				    =  attention_probs_dropout_prob
					_UpperCamelCase				:Optional[int]				    =  initializer_range
					_UpperCamelCase				:Dict				    =  type_vocab_size
					_UpperCamelCase				:Union[str, Any]				    =  segment_means_seq_len
					_UpperCamelCase				:Dict				    =  num_landmarks
					_UpperCamelCase				:str				    =  conv_kernel_size
					_UpperCamelCase				:Dict				    =  inv_coeff_init_option
					_UpperCamelCase				:Any				    =  layer_norm_eps
					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__				,							bos_token_id=SCREAMING_SNAKE_CASE__				,							eos_token_id=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )





"""simple docstring"""






def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:Tuple				    =  word.upper()
			_UpperCamelCase				:Dict				    =  0
			_UpperCamelCase				:Union[str, Any]				    =  {}
			_UpperCamelCase				:Optional[int]				    =  []

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


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

					UpperCamelCase__							:Dict       =  time.time()
					with open("""dictionary.txt""") as in_file:
										UpperCamelCase__							:str       =  in_file.read().splitlines()

					UpperCamelCase__							:dict       =  {}
					for word in word_list:
										UpperCamelCase__							:List[Any]       =  get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															UpperCamelCase__							:Tuple       =  [word]

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

					UpperCamelCase__							:Dict       =  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.

