


"""simple docstring"""






import os
from string import ascii_letters

lowercase  		=			ascii_letters + """ \t\n"""



def 			A__		(					)							->	dict[str, None]:

	'''simple docstring'''


	snake_case__  :						Tuple		=      os.path.split(os.path.realpath(lowerCamelCase__	)	)
	snake_case__  :						dict[str, None]		=      {}
	with open(path[0] + "/dictionary.txt"	) as dictionary_file:
		for word in dictionary_file.read().split("\n"	):
			snake_case__  :						List[str]		=      None
	return english_words


lowercase  		=			load_dictionary()



def 			A__		(					_UpperCAmelCase    :      str	)							->	float:

	'''simple docstring'''


	snake_case__  :						int		=      message.upper()
	snake_case__  :						Union[str, Any]		=      remove_non_letters(lowerCamelCase__	)
	snake_case__  :						Optional[Any]		=      message.split()
	snake_case__  :						Dict		=      len([word for word in possible_words if word in ENGLISH_WORDS]	)
	return float(lowerCamelCase__	) / len(lowerCamelCase__	)



def 			A__		(					_UpperCAmelCase    :      str	)							->	str:

	'''simple docstring'''


	return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE	)



def 			A__		(					_UpperCAmelCase    :      str				,    _UpperCAmelCase    :      int = 20				,    _UpperCAmelCase    :      int = 85	)							->	bool:

	'''simple docstring'''


	snake_case__  :						List[str]		=      get_english_count(lowerCamelCase__	) * 1_00 >= word_percentage
	snake_case__  :						Tuple		=      len(remove_non_letters(lowerCamelCase__	)	)
	snake_case__  :						str		=      (float(lowerCamelCase__	) / len(lowerCamelCase__	)) * 1_00
	snake_case__  :						Dict		=      message_letters_percentage >= letter_percentage
	return words_match and letters_match


if __name__ == "__main__":
					import doctest

					doctest.testmod()











'''simple docstring'''




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


snake_case__ 							=    logging.get_logger(__name__)

snake_case__ 							=    {
    """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      UpperCamelCase_  (a__  ):







						"""simple docstring"""


						_lowerCAmelCase         =       'nystromformer'







						def __init__(  self   :							Optional[Any]				,			_lowerCamelCase   :							str=30000				,			_lowerCamelCase   :							Optional[Any]=768				,			_lowerCamelCase   :							Optional[Any]=12				,			_lowerCamelCase   :							str=12				,			_lowerCamelCase   :							List[str]=3072				,			_lowerCamelCase   :							Any="gelu_new"				,			_lowerCamelCase   :							List[Any]=0.1				,			_lowerCamelCase   :							List[str]=0.1				,			_lowerCamelCase   :							List[str]=510				,			_lowerCamelCase   :							Optional[Any]=2				,			_lowerCamelCase   :							List[Any]=64				,			_lowerCamelCase   :							str=64				,			_lowerCamelCase   :							str=65				,			_lowerCamelCase   :							List[Any]=False				,			_lowerCamelCase   :							List[Any]=0.02				,			_lowerCamelCase   :							List[Any]=1E-5				,			_lowerCamelCase   :							Dict=1				,			_lowerCamelCase   :							Tuple=0				,			_lowerCamelCase   :							List[str]=2				,			**_lowerCamelCase   :							List[str]				,			):





											"""simple docstring"""







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



