

import warnings

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


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

A_ :				Tuple						 =    {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     Tuple													= '''mvp'''
					UpperCAmelCase__:     Dict													= ['''past_key_values''']
					UpperCAmelCase__:     Tuple													= {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






					def __init__(				self					, A__=5_0267					, A__=1024					, A__=12					, A__=4096					, A__=16					, A__=12					, A__=4096					, A__=16					, A__=0.0					, A__=0.0					, A__="gelu"					, A__=1024					, A__=0.1					, A__=0.0					, A__=0.0					, A__=0.0_2					, A__=0.0					, A__=False					, A__=True					, A__=1					, A__=0					, A__=2					, A__=True					, A__=2					, A__=2					, A__=False					, A__=100					, A__=800					, **A__					, ):
						A__							:					int       =	vocab_size
						A__							:					Optional[Any]       =	max_position_embeddings
						A__							:					Union[str, Any]       =	d_model
						A__							:					int       =	encoder_ffn_dim
						A__							:					List[Any]       =	encoder_layers
						A__							:					Tuple       =	encoder_attention_heads
						A__							:					Tuple       =	decoder_ffn_dim
						A__							:					Optional[Any]       =	decoder_layers
						A__							:					List[Any]       =	decoder_attention_heads
						A__							:					Union[str, Any]       =	dropout
						A__							:					List[Any]       =	attention_dropout
						A__							:					Optional[Any]       =	activation_dropout
						A__							:					List[str]       =	activation_function
						A__							:					List[str]       =	init_std
						A__							:					Optional[int]       =	encoder_layerdrop
						A__							:					str       =	decoder_layerdrop
						A__							:					Dict       =	classifier_dropout
						A__							:					Tuple       =	use_cache
						A__							:					List[str]       =	encoder_layers
						A__							:					str       =	scale_embedding  # scale factor will be sqrt(d_model) if True
						A__							:					Dict       =	use_prompt
						A__							:					Optional[int]       =	prompt_length
						A__							:					int       =	prompt_mid_dim

						super().__init__(
						    pad_token_id=A__					, bos_token_id=A__					, eos_token_id=A__					, is_encoder_decoder=A__					, decoder_start_token_id=A__					, forced_eos_token_id=A__					, **A__					, )

						if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""					, A__    ):
							A__							:					str       =	self.bos_token_id
							warnings.warn(
							    F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
							    """The config can simply be saved and uploaded again to be fixed."""    )






def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				str      )  ->   bool:
	A__							:					Union[str, Any]       =	len(lowercase_      )
	A__							:					List[Any]       =	len(lowercase_      )
	A__							:					List[Any]       =	[[False for _ in range(m + 1      )] for _ in range(n + 1      )]
	A__							:					str       =	True
	for i in range(lowercase_      ):
		for j in range(m + 1      ):
			if dp[i][j]:
				if j < m and a[i].upper() == b[j]:
					A__							:					int       =	True
				if a[i].islower():
					A__							:					Dict       =	True
	return dp[n][m]


if __name__ == "__main__":
	import doctest

	doctest.testmod()



