






import functools
import operator

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


a_				=     logging.get_logger(__name__)

a_				=     {
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}


class   lowercase__  (      __SCREAMING_SNAKE_CASE						):
	a_       ="""wavlm"""

	def __init__(				self		,     __UpperCAmelCase=32		,     __UpperCAmelCase=768		,     __UpperCAmelCase=12		,     __UpperCAmelCase=12		,     __UpperCAmelCase=3072		,     __UpperCAmelCase="gelu"		,     __UpperCAmelCase=0.1		,     __UpperCAmelCase=0.1		,     __UpperCAmelCase=0.1		,     __UpperCAmelCase=0.0		,     __UpperCAmelCase=0.1		,     __UpperCAmelCase=0.1		,     __UpperCAmelCase=0.02		,     __UpperCAmelCase=1E-5		,     __UpperCAmelCase="group"		,     __UpperCAmelCase="gelu"		,     __UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)		,     __UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,     __UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)		,     __UpperCAmelCase=False		,     __UpperCAmelCase=128		,     __UpperCAmelCase=16		,     __UpperCAmelCase=320		,     __UpperCAmelCase=800		,     __UpperCAmelCase=False		,     __UpperCAmelCase=True		,     __UpperCAmelCase=0.05		,     __UpperCAmelCase=10		,     __UpperCAmelCase=2		,     __UpperCAmelCase=0.0		,     __UpperCAmelCase=10		,     __UpperCAmelCase=320		,     __UpperCAmelCase=2		,     __UpperCAmelCase=0.1		,     __UpperCAmelCase=100		,     __UpperCAmelCase=256		,     __UpperCAmelCase=256		,     __UpperCAmelCase=0.1		,     __UpperCAmelCase="mean"		,     __UpperCAmelCase=False		,     __UpperCAmelCase=False		,     __UpperCAmelCase=256		,     __UpperCAmelCase=(512, 512, 512, 512, 1500)		,     __UpperCAmelCase=(5, 3, 3, 1, 1)		,     __UpperCAmelCase=(1, 2, 3, 1, 1)		,     __UpperCAmelCase=512		,     __UpperCAmelCase=80		,     __UpperCAmelCase=0		,     __UpperCAmelCase=1		,     __UpperCAmelCase=2		,     __UpperCAmelCase=False		,     __UpperCAmelCase=3		,     __UpperCAmelCase=2		,     __UpperCAmelCase=3		,     __UpperCAmelCase=None		,     **__UpperCAmelCase		,     )->       int:







							'''simple docstring'''





							super().__init__(**lowerCAmelCase__		,     pad_token_id=lowerCAmelCase__		,     bos_token_id=lowerCAmelCase__		,     eos_token_id=lowerCAmelCase__							)
							lowerCAmelCase__			   =    hidden_size
							lowerCAmelCase__			   =    feat_extract_norm
							lowerCAmelCase__			   =    feat_extract_activation
							lowerCAmelCase__			   =    list(lowerCAmelCase__							)
							lowerCAmelCase__			   =    list(lowerCAmelCase__							)
							lowerCAmelCase__			   =    list(lowerCAmelCase__							)
							lowerCAmelCase__			   =    conv_bias
							lowerCAmelCase__			   =    num_buckets
							lowerCAmelCase__			   =    max_bucket_distance
							lowerCAmelCase__			   =    num_conv_pos_embeddings
							lowerCAmelCase__			   =    num_conv_pos_embedding_groups
							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__			   =    num_ctc_classes
							lowerCAmelCase__			   =    vocab_size
							lowerCAmelCase__			   =    do_stable_layer_norm
							lowerCAmelCase__			   =    use_weighted_layer_sum
							lowerCAmelCase__			   =    classifier_proj_size

							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__			   =    apply_spec_augment
							lowerCAmelCase__			   =    mask_time_prob
							lowerCAmelCase__			   =    mask_time_length
							lowerCAmelCase__			   =    mask_time_min_masks
							lowerCAmelCase__			   =    mask_feature_prob
							lowerCAmelCase__			   =    mask_feature_length

							# parameters for pretraining with codevector quantized representations
							lowerCAmelCase__			   =    num_codevectors_per_group
							lowerCAmelCase__			   =    num_codevector_groups
							lowerCAmelCase__			   =    contrastive_logits_temperature
							lowerCAmelCase__			   =    num_negatives
							lowerCAmelCase__			   =    codevector_dim
							lowerCAmelCase__			   =    proj_codevector_dim
							lowerCAmelCase__			   =    diversity_loss_weight

							# 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 		UpperCAmelCase	(				self							)->       Tuple:







							'''simple docstring'''





							return functools.reduce(operator.mul		,     self.conv_stride		,     1							)










def 						a__			(				A__,      A__				):

			def get_matched_characters(A__,      A__				) -> str:
						SCREAMING_SNAKE_CASE_		:     Dict					=					[]
						SCREAMING_SNAKE_CASE_		:     Any					=					min(len(_stra				),      len(_stra				)				) // 2
						for i, l in enumerate(_stra				):
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					int(max(0,      i - limit				)				)
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					int(min(i + limit + 1,      len(_stra				)				)				)
									if l in _stra[left:right]:
												matched.append(A__				)
												SCREAMING_SNAKE_CASE_		:     List[str]					=					F'''{_stra[0:_stra.index(A__				)]} {_stra[_stra.index(A__				) + 1:]}'''

						return "".join(A__				)

			# matching characters
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					len(A__				)

			# transposition
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
			    len([(ca, ca) for ca, ca in zip(A__,      A__				) if ca != ca]				) // 2
			)

			if not match_count:
						SCREAMING_SNAKE_CASE_		:     Dict					=					0.0
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(
						    1
						    / 3
						    * (
						        match_count / len(A__				)
						        + match_count / len(A__				)
						        + (match_count - transpositions) / match_count
						    )
						)

			# common prefix up to 4 characters
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					0
			for ca, ca in zip(stra[:4],      stra[:4]				):
						if ca == ca:
									prefix_len += 1
						else:
									break

			return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(jaro_winkler('hello', 'world'))





