

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


A_ :				Optional[Any]						 =    logging.get_logger(__name__)

A_ :				List[Any]						 =    {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     List[str]													= '''biogpt'''






					def __init__(				self					, A__=4_2384					, A__=1024					, A__=24					, A__=16					, A__=4096					, A__="gelu"					, A__=0.1					, A__=0.1					, A__=1024					, A__=0.0_2					, A__=1e-12					, A__=True					, A__=True					, A__=0.0					, A__=0.0					, A__=1					, A__=0					, A__=2					, **A__					, ):
						A__							:					Optional[int]       =	vocab_size
						A__							:					List[str]       =	max_position_embeddings
						A__							:					Optional[int]       =	hidden_size
						A__							:					Tuple       =	num_hidden_layers
						A__							:					Optional[Any]       =	num_attention_heads
						A__							:					Optional[int]       =	intermediate_size
						A__							:					Dict       =	hidden_act
						A__							:					List[Any]       =	hidden_dropout_prob
						A__							:					Union[str, Any]       =	attention_probs_dropout_prob
						A__							:					Union[str, Any]       =	initializer_range
						A__							:					Optional[int]       =	layer_norm_eps
						A__							:					Tuple       =	scale_embedding
						A__							:					Tuple       =	use_cache
						A__							:					Dict       =	layerdrop
						A__							:					Optional[Any]       =	activation_dropout
						super().__init__(pad_token_id=A__					, bos_token_id=A__					, eos_token_id=A__					, **A__    )






class 					_a		:


					'''simple docstring'''






					def __init__(				self    ):
						A__							:					str       =	""""""
						A__							:					Any       =	""""""
						A__							:					List[Any]       =	[]






					def 						__A    (				self					, A__					, A__    ):
						if m == -1:
							return n + 1
						elif n == -1:
							return m + 1
						elif self.dp[m][n] > -1:
							return self.dp[m][n]
						else:
							if self.worda[m] == self.worda[n]:
								A__							:					Optional[Any]       =	self.__min_dist_top_down_dp(m - 1					, n - 1    )
							else:
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(A__					, n - 1    )
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(m - 1					, A__    )
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(m - 1					, n - 1    )
								A__							:					List[Any]       =	1 + min(A__					, A__					, A__    )

							return self.dp[m][n]






					def 						__A    (				self					, A__					, A__    ):
						A__							:					Tuple       =	worda
						A__							:					Dict       =	worda
						A__							:					Optional[Any]       =	[[-1 for _ in range(len(A__    )    )] for _ in range(len(A__    )    )]

						return self.__min_dist_top_down_dp(len(A__    ) - 1					, len(A__    ) - 1    )






					def 						__A    (				self					, A__					, A__    ):
						A__							:					Optional[Any]       =	worda
						A__							:					Dict       =	worda
						A__							:					Union[str, Any]       =	len(A__    )
						A__							:					List[str]       =	len(A__    )
						A__							:					int       =	[[0 for _ in range(n + 1    )] for _ in range(m + 1    )]

						for i in range(m + 1    ):
							for j in range(n + 1    ):
								if i == 0:  # first string is empty
									A__							:					Tuple       =	j
								elif j == 0:  # second string is empty
									A__							:					Dict       =	i
								elif worda[i - 1] == worda[j - 1]:  # last characters are equal
									A__							:					str       =	self.dp[i - 1][j - 1]
								else:
									A__							:					Union[str, Any]       =	self.dp[i][j - 1]
									A__							:					str       =	self.dp[i - 1][j]
									A__							:					Union[str, Any]       =	self.dp[i - 1][j - 1]
									A__							:					Tuple       =	1 + min(A__					, A__					, A__    )
						return self.dp[m][n]


if __name__ == "__main__":
	A_ :				Union[str, Any]						 =    EditDistance()

	print('****************** Testing Edit Distance DP Algorithm ******************')
	print()

	A_ :				int						 =    input('Enter the first string: ').strip()
	A_ :				List[str]						 =    input('Enter the second string: ').strip()

	print()
	print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
	print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
	print()
	print('*************** End of Testing Edit Distance DP Algorithm ***************')



