




'''simple docstring'''







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


_lowerCamelCase				:		Tuple				      =    logging.get_logger(__name__)

_lowerCamelCase				:		Optional[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 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					"""nystromformer"""






					def __init__(       self,    __UpperCamelCase=30_000,    __UpperCamelCase=768,    __UpperCamelCase=12,    __UpperCamelCase=12,    __UpperCamelCase=3_072,    __UpperCamelCase="gelu_new",    __UpperCamelCase=0.1,    __UpperCamelCase=0.1,    __UpperCamelCase=510,    __UpperCamelCase=2,    __UpperCamelCase=64,    __UpperCamelCase=64,    __UpperCamelCase=65,    __UpperCamelCase=False,    __UpperCamelCase=0.02,    __UpperCamelCase=1E-5,    __UpperCamelCase=1,    __UpperCamelCase=0,    __UpperCamelCase=2,    **__UpperCamelCase,    ):
							_snake_case      			=			vocab_size
							_snake_case      			=			max_position_embeddings
							_snake_case      			=			hidden_size
							_snake_case      			=			num_hidden_layers
							_snake_case      			=			num_attention_heads
							_snake_case      			=			intermediate_size
							_snake_case      			=			hidden_act
							_snake_case      			=			hidden_dropout_prob
							_snake_case      			=			attention_probs_dropout_prob
							_snake_case      			=			initializer_range
							_snake_case      			=			type_vocab_size
							_snake_case      			=			segment_means_seq_len
							_snake_case      			=			num_landmarks
							_snake_case      			=			conv_kernel_size
							_snake_case      			=			inv_coeff_init_option
							_snake_case      			=			layer_norm_eps
							super().__init__(pad_token_id=__UpperCamelCase,    bos_token_id=__UpperCamelCase,    eos_token_id=__UpperCamelCase,    **__UpperCamelCase					)






"""simple docstring"""
import os
from string import ascii_letters

SCREAMING_SNAKE_CASE__       =	ascii_letters + " \t\n"





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





						"""simple docstring"""






						snake_case             =      os.path.split(os.path.realpath(_UpperCamelCase       )       )
						snake_case             =      {}
						with open(path[0] + '/dictionary.txt'       ) as dictionary_file:
												for word in dictionary_file.read().split('\n'       ):
																		snake_case             =      None
						return english_words


SCREAMING_SNAKE_CASE__       =	load_dictionary()





def  lowerCAmelCase__     (      _UpperCamelCase							:					str       )       ->					float:





						"""simple docstring"""






						snake_case             =      message.upper()
						snake_case             =      remove_non_letters(_UpperCamelCase       )
						snake_case             =      message.split()
						snake_case             =      len([word for word in possible_words if word in ENGLISH_WORDS]       )
						return float(_UpperCamelCase       ) / len(_UpperCamelCase       )





def  lowerCAmelCase__     (      _UpperCamelCase							:					str       )       ->					str:





						"""simple docstring"""






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





def  lowerCAmelCase__     (      _UpperCamelCase							:					str			,      _UpperCamelCase							:					int = 2_0			,      _UpperCamelCase							:					int = 8_5       )       ->					bool:





						"""simple docstring"""






						snake_case             =      get_english_count(_UpperCamelCase       ) * 1_0_0 >= word_percentage
						snake_case             =      len(remove_non_letters(_UpperCamelCase       )       )
						snake_case             =      (float(_UpperCamelCase       ) / len(_UpperCamelCase       )) * 1_0_0
						snake_case             =      message_letters_percentage >= letter_percentage
						return words_match and letters_match


if __name__ == "__main__":
					import doctest

					doctest.testmod()


