'''simple docstring'''




def 				lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      str				)    ->   str:

							if not (isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				) and isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				)):
														raise ValueError("""longest_common_substring() takes two strings for inputs"""				)

							lowercase_      :    Optional[int]       		= len(UpperCAmelCase__				)
							lowercase_      :    Union[str, Any]       		= len(UpperCAmelCase__				)

							lowercase_      :    str       		= [[0] * (texta_length + 1) for _ in range(texta_length + 1				)]
							lowercase_      :    Union[str, Any]       		= 0
							lowercase_      :    Dict       		= 0

							for i in range(1   ,   texta_length + 1				):
														for j in range(1   ,   texta_length + 1				):
																					if texta[i - 1] == texta[j - 1]:
																												lowercase_      :    int       		= 1 + dp[i - 1][j - 1]
																												if dp[i][j] > ans_length:
																																			lowercase_      :    List[Any]       		= i
																																			lowercase_      :    str       		= dp[i][j]

							return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






'''simple docstring'''




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


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

_lowercase		:				Union[str, Any]	       =		{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = '''speech_to_text'''
			UpperCamelCase__       = ['''past_key_values''']
			UpperCamelCase__       = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







			def __init__(			self       :						str       ,    lowercase_       :						Optional[int]=10000       ,    lowercase_       :						int=12       ,    lowercase_       :						Any=2048       ,    lowercase_       :						Any=4       ,    lowercase_       :						Dict=6       ,    lowercase_       :						Any=2048       ,    lowercase_       :						List[str]=4       ,    lowercase_       :						str=0.0       ,    lowercase_       :						str=0.0       ,    lowercase_       :						Union[str, Any]=True       ,    lowercase_       :						List[Any]=True       ,    lowercase_       :						int="relu"       ,    lowercase_       :						str=256       ,    lowercase_       :						int=0.1       ,    lowercase_       :						int=0.0       ,    lowercase_       :						str=0.0       ,    lowercase_       :						Optional[int]=0.02       ,    lowercase_       :						str=2       ,    lowercase_       :						Union[str, Any]=True       ,    lowercase_       :						Any=1       ,    lowercase_       :						Dict=0       ,    lowercase_       :						List[str]=2       ,    lowercase_       :						List[Any]=6000       ,    lowercase_       :						Tuple=1024       ,    lowercase_       :						str=2       ,    lowercase_       :						Any=(5, 5)       ,    lowercase_       :						Union[str, Any]=1024       ,    lowercase_       :						Dict=80       ,    lowercase_       :						List[Any]=1       ,    **lowercase_       :						int       ,    ):
										lowercase_      :    List[Any]       		= vocab_size
										lowercase_      :    str       		= d_model
										lowercase_      :    List[Any]       		= encoder_ffn_dim
										lowercase_      :    str       		= encoder_layers
										lowercase_      :    Dict       		= encoder_attention_heads
										lowercase_      :    str       		= decoder_ffn_dim
										lowercase_      :    int       		= decoder_layers
										lowercase_      :    Any       		= decoder_attention_heads
										lowercase_      :    Any       		= dropout
										lowercase_      :    Dict       		= attention_dropout
										lowercase_      :    Optional[int]       		= activation_dropout
										lowercase_      :    Any       		= activation_function
										lowercase_      :    Union[str, Any]       		= init_std
										lowercase_      :    str       		= encoder_layerdrop
										lowercase_      :    Optional[int]       		= decoder_layerdrop
										lowercase_      :    Dict       		= use_cache
										lowercase_      :    Union[str, Any]       		= encoder_layers
										lowercase_      :    Tuple       		= scale_embedding  # scale factor will be sqrt(d_model) if True
										lowercase_      :    Dict       		= max_source_positions
										lowercase_      :    Optional[int]       		= max_target_positions
										lowercase_      :    Tuple       		= num_conv_layers
										lowercase_      :    Tuple       		= list(lowercase_					)
										lowercase_      :    Union[str, Any]       		= conv_channels
										lowercase_      :    str       		= input_feat_per_channel
										lowercase_      :    str       		= input_channels

										if len(self.conv_kernel_sizes					) != self.num_conv_layers:
																	raise ValueError(
																	    """Configuration for convolutional module is incorrect. """
																	    """It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` """
																	    f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes					)}`, '''
																	    f'''`config.num_conv_layers = {self.num_conv_layers}`.'''					)

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





