

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str )   ->       bool:
				_UpperCAmelCase       :		Any 		=    len(lowerCAmelCase ) + 1
				_UpperCAmelCase       :		Dict 		=    len(lowerCAmelCase ) + 1

				# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
				# length i of input_string matches with prefix string of length j of
				# given pattern.
				# "dp" stands for dynamic programming.
				_UpperCAmelCase       :		List[Any] 		=    [[0 for i in range(lowerCAmelCase )] for j in range(lowerCAmelCase )]

				# since string of zero length match pattern of zero length
				_UpperCAmelCase       :		str 		=    1

				# since pattern of zero length will never match with string of non-zero length
				for i in range(1  ,      lowerCAmelCase ):
								_UpperCAmelCase       :		Dict 		=    0

				# since string of zero length will match with pattern where there
				# is at least one * alternatively
				for j in range(1  ,      lowerCAmelCase ):
								_UpperCAmelCase       :		List[Any] 		=    dp[0][j - 2] if pattern[j - 1] == "*" else 0

				# now using bottom-up approach to find for all remaining lengths
				for i in range(1  ,      lowerCAmelCase ):
								for j in range(1  ,      lowerCAmelCase ):
												if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																_UpperCAmelCase       :		Any 		=    dp[i - 1][j - 1]

												elif pattern[j - 1] == "*":
																if dp[i][j - 2] == 1:
																				_UpperCAmelCase       :		Dict 		=    1
																elif pattern[j - 2] in (input_string[i - 1], "."):
																				_UpperCAmelCase       :		List[Any] 		=    dp[i - 1][j]
																else:
																				_UpperCAmelCase       :		List[str] 		=    0
												else:
																_UpperCAmelCase       :		List[str] 		=    0

				return bool(dp[-1][-1] )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					# inputing the strings
					# input_string = input("input a string :")
					# pattern = input("input a pattern :")

					SCREAMING_SNAKE_CASE_        =      'aab'
					SCREAMING_SNAKE_CASE_        =      'c*a*b'

					# using function to check whether given string matches the given pattern
					if match_pattern(input_string, pattern):
										print(F'''{input_string} matches the given pattern {pattern}''')
					else:
										print(F'''{input_string} does not match with the given pattern {pattern}''')





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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"megatron-bert"


						def __init__(       self						,					A_=29056						,					A_=1024						,					A_=24						,					A_=16						,					A_=4096						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=2						,					A_=0.02						,					A_=1e-12						,					A_=0						,					A_="absolute"						,					A_=True						,					**A_						,					):







										'''simple docstring'''


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

										_UpperCAmelCase       :		Optional[Any] 		=    vocab_size
										_UpperCAmelCase       :		List[str] 		=    hidden_size
										_UpperCAmelCase       :		List[str] 		=    num_hidden_layers
										_UpperCAmelCase       :		Optional[Any] 		=    num_attention_heads
										_UpperCAmelCase       :		Any 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    intermediate_size
										_UpperCAmelCase       :		int 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Optional[Any] 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Any 		=    max_position_embeddings
										_UpperCAmelCase       :		str 		=    type_vocab_size
										_UpperCAmelCase       :		Optional[int] 		=    initializer_range
										_UpperCAmelCase       :		Dict 		=    layer_norm_eps
										_UpperCAmelCase       :		Tuple 		=    position_embedding_type
										_UpperCAmelCase       :		Tuple 		=    use_cache


