


import math

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


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

UpperCAmelCase					:  Optional[int]			=						{
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}







class     __lowercase    (				__UpperCAmelCase   ):



		"""simple docstring"""

		UpperCamelCase			: List[str]           =						"data2vec-audio"







		def __init__(      self						,	A=32						,	A=7_68						,	A=12						,	A=12						,	A=30_72						,	A="gelu"						,	A=0.1						,	A=0.1						,	A=0.1						,	A=0.0						,	A=0.1						,	A=0.1						,	A=0.02						,	A=1e-5						,	A="gelu"						,	A=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)						,	A=(5, 2, 2, 2, 2, 2, 2)						,	A=(10, 3, 3, 3, 3, 2, 2)						,	A=False						,	A=16						,	A=19						,	A=5						,	A=0.05						,	A=10						,	A=2						,	A=0.0						,	A=10						,	A=0						,	A="sum"						,	A=False						,	A=False						,	A=2_56						,	A=(5_12, 5_12, 5_12, 5_12, 15_00)						,	A=(5, 3, 3, 1, 1)						,	A=(1, 2, 3, 1, 1)						,	A=5_12						,	A=0						,	A=1						,	A=2						,	A=False						,	A=3						,	A=2						,	A=3						,	A=None						,	**A						,	)   ->      str:







					'''simple docstring'''







					super().__init__(**lowerCamelCase__						,	pad_token_id=lowerCamelCase__						,	bos_token_id=lowerCamelCase__						,	eos_token_id=lowerCamelCase__     )
					lowerCamelCase  						=   hidden_size
					lowerCamelCase  						=   feat_extract_activation
					lowerCamelCase  						=   list(lowerCamelCase__     )
					lowerCamelCase  						=   list(lowerCamelCase__     )
					lowerCamelCase  						=   list(lowerCamelCase__     )
					lowerCamelCase  						=   conv_bias
					lowerCamelCase  						=   num_conv_pos_embeddings
					lowerCamelCase  						=   num_conv_pos_embedding_groups
					lowerCamelCase  						=   conv_pos_kernel_size
					lowerCamelCase  						=   len(self.conv_dim     )
					lowerCamelCase  						=   num_hidden_layers
					lowerCamelCase  						=   intermediate_size
					lowerCamelCase  						=   hidden_act
					lowerCamelCase  						=   num_attention_heads
					lowerCamelCase  						=   hidden_dropout
					lowerCamelCase  						=   attention_dropout
					lowerCamelCase  						=   activation_dropout
					lowerCamelCase  						=   feat_proj_dropout
					lowerCamelCase  						=   final_dropout
					lowerCamelCase  						=   layerdrop
					lowerCamelCase  						=   layer_norm_eps
					lowerCamelCase  						=   initializer_range
					lowerCamelCase  						=   vocab_size
					lowerCamelCase  						=   use_weighted_layer_sum

					if (
					    (len(self.conv_stride     ) != self.num_feat_extract_layers)
					    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
					    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
					):
								raise ValueError(
								    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
								    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
								    F' {len(self.conv_dim     )}`, `len(config.conv_stride) = {len(self.conv_stride     )}`,'
								    F' `len(config.conv_kernel) = {len(self.conv_kernel     )}`.'     )

					# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
					lowerCamelCase  						=   mask_time_prob
					lowerCamelCase  						=   mask_time_length
					lowerCamelCase  						=   mask_time_min_masks
					lowerCamelCase  						=   mask_feature_prob
					lowerCamelCase  						=   mask_feature_length
					lowerCamelCase  						=   mask_feature_min_masks

					# ctc loss
					lowerCamelCase  						=   ctc_loss_reduction
					lowerCamelCase  						=   ctc_zero_infinity

					# adapter
					lowerCamelCase  						=   add_adapter
					lowerCamelCase  						=   adapter_kernel_size
					lowerCamelCase  						=   adapter_stride
					lowerCamelCase  						=   num_adapter_layers
					lowerCamelCase  						=   output_hidden_size or hidden_size

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					lowerCamelCase  						=   classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					lowerCamelCase  						=   list(lowerCamelCase__     )
					lowerCamelCase  						=   list(lowerCamelCase__     )
					lowerCamelCase  						=   list(lowerCamelCase__     )
					lowerCamelCase  						=   xvector_output_dim







		@property
		def  __A					(      self     )   ->      List[Any]:







					'''simple docstring'''







					return math.prod(self.conv_stride     )


"""simple docstring"""






import functools



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							len(lowerCamelCase       )
				UpperCAmelCase__             =							len(lowerCamelCase       )

				@functools.cache
				def min_distance(lowerCamelCase					,			lowerCamelCase       ) -> int:
								# if first word index is overflow - delete all from the second word
								if indexa >= len_worda:
												return len_worda - indexa
								# if second word index is overflow - delete all from the first word
								if indexa >= len_worda:
												return len_worda - indexa
								UpperCAmelCase__             =							int(worda[indexa] != worda[indexa]       )  # current letters not identical
								return min(
								    1 + min_distance(indexa + 1					,			lowerCamelCase       )					,			1 + min_distance(lowerCamelCase					,			indexa + 1       )					,			diff + min_distance(indexa + 1					,			indexa + 1       )					,			)

				return min_distance(0					,			0       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



