
"""simple docstring"""







def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase		):
	__lowerCAmelCase			:						Optional[Any]									=      len(_UpperCamelCase		)
	__lowerCAmelCase			:						int									=      []
	for i in range(len(_UpperCamelCase		) - pat_len + 1		):
		__lowerCAmelCase			:						Optional[Any]									=      True
		for j in range(_UpperCamelCase		):
			if s[i + j] != pattern[j]:
				__lowerCAmelCase			:						str									=      False
				break
		if match_found:
			position.append(_UpperCamelCase		)
	return position


if __name__ == "__main__":
							assert naive_pattern_search("""ABCDEFG""", """DE""") == [3]
							print(naive_pattern_search("""ABAAABCDBBABCDDEBCABC""", """ABC"""))




import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case						:Any      		=     logging.getLogger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						'''masked_bert'''



       def __init__(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     int=30_522     ,		__SCREAMING_SNAKE_CASE							:     str=768     ,		__SCREAMING_SNAKE_CASE							:     List[str]=12     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=3_072     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     int=0.1     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=512     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.02     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1E-12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0     ,		__SCREAMING_SNAKE_CASE							:     List[str]="topK"     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="constant"     ,		__SCREAMING_SNAKE_CASE							:     int=0.0     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]     ,		):


           '''simple docstring'''

           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)

           __a						       =				vocab_size
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				hidden_act
           __a						       =				intermediate_size
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				type_vocab_size
           __a						       =				initializer_range
           __a						       =				layer_norm_eps
           __a						       =				pruning_method
           __a						       =				mask_init
           __a						       =				mask_scale





