





import functools
import operator

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


__lowerCAmelCase :Any     =    logging.get_logger(__name__)

__lowerCAmelCase :List[str]     =    {
    '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        _a(    __A    ):
		lowerCamelCase__     :Optional[int]   				=		'wavlm'







		def __init__(    self ,						__snake_case=3_2 ,						__snake_case=7_6_8 ,						__snake_case=1_2 ,						__snake_case=1_2 ,						__snake_case=3_0_7_2 ,						__snake_case="gelu" ,						__snake_case=0.1 ,						__snake_case=0.1 ,						__snake_case=0.1 ,						__snake_case=0.0 ,						__snake_case=0.1 ,						__snake_case=0.1 ,						__snake_case=0.02 ,						__snake_case=1E-5 ,						__snake_case="group" ,						__snake_case="gelu" ,						__snake_case=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) ,						__snake_case=(5, 2, 2, 2, 2, 2, 2) ,						__snake_case=(1_0, 3, 3, 3, 3, 2, 2) ,						__snake_case=False ,						__snake_case=1_2_8 ,						__snake_case=1_6 ,						__snake_case=3_2_0 ,						__snake_case=8_0_0 ,						__snake_case=False ,						__snake_case=True ,						__snake_case=0.05 ,						__snake_case=1_0 ,						__snake_case=2 ,						__snake_case=0.0 ,						__snake_case=1_0 ,						__snake_case=3_2_0 ,						__snake_case=2 ,						__snake_case=0.1 ,						__snake_case=1_0_0 ,						__snake_case=2_5_6 ,						__snake_case=2_5_6 ,						__snake_case=0.1 ,						__snake_case="mean" ,						__snake_case=False ,						__snake_case=False ,						__snake_case=2_5_6 ,						__snake_case=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) ,						__snake_case=(5, 3, 3, 1, 1) ,						__snake_case=(1, 2, 3, 1, 1) ,						__snake_case=5_1_2 ,						__snake_case=8_0 ,						__snake_case=0 ,						__snake_case=1 ,						__snake_case=2 ,						__snake_case=False ,						__snake_case=3 ,						__snake_case=2 ,						__snake_case=3 ,						__snake_case=None ,						**__snake_case ,						)      ->      str:


							'''simple docstring'''

							super().__init__(**__snake_case ,						pad_token_id=__snake_case ,						bos_token_id=__snake_case ,						eos_token_id=__snake_case				)
							_snake_case       :					str				       =   hidden_size
							_snake_case       :					Dict				       =   feat_extract_norm
							_snake_case       :					Tuple				       =   feat_extract_activation
							_snake_case       :					Optional[Any]				       =   list(__snake_case				)
							_snake_case       :					List[Any]				       =   list(__snake_case				)
							_snake_case       :					Union[str, Any]				       =   list(__snake_case				)
							_snake_case       :					str				       =   conv_bias
							_snake_case       :					Union[str, Any]				       =   num_buckets
							_snake_case       :					Optional[Any]				       =   max_bucket_distance
							_snake_case       :					Any				       =   num_conv_pos_embeddings
							_snake_case       :					List[str]				       =   num_conv_pos_embedding_groups
							_snake_case       :					Any				       =   len(self.conv_dim				)
							_snake_case       :					Dict				       =   num_hidden_layers
							_snake_case       :					Dict				       =   intermediate_size
							_snake_case       :					str				       =   hidden_act
							_snake_case       :					Any				       =   num_attention_heads
							_snake_case       :					int				       =   hidden_dropout
							_snake_case       :					Dict				       =   attention_dropout
							_snake_case       :					Any				       =   activation_dropout
							_snake_case       :					List[str]				       =   feat_proj_dropout
							_snake_case       :					str				       =   final_dropout
							_snake_case       :					Dict				       =   layerdrop
							_snake_case       :					Optional[int]				       =   layer_norm_eps
							_snake_case       :					str				       =   initializer_range
							_snake_case       :					Union[str, Any]				       =   num_ctc_classes
							_snake_case       :					List[Any]				       =   vocab_size
							_snake_case       :					List[Any]				       =   do_stable_layer_norm
							_snake_case       :					Tuple				       =   use_weighted_layer_sum
							_snake_case       :					str				       =   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
							_snake_case       :					List[Any]				       =   apply_spec_augment
							_snake_case       :					Union[str, Any]				       =   mask_time_prob
							_snake_case       :					List[str]				       =   mask_time_length
							_snake_case       :					int				       =   mask_time_min_masks
							_snake_case       :					List[str]				       =   mask_feature_prob
							_snake_case       :					Union[str, Any]				       =   mask_feature_length

							# parameters for pretraining with codevector quantized representations
							_snake_case       :					Dict				       =   num_codevectors_per_group
							_snake_case       :					Dict				       =   num_codevector_groups
							_snake_case       :					int				       =   contrastive_logits_temperature
							_snake_case       :					Optional[Any]				       =   num_negatives
							_snake_case       :					str				       =   codevector_dim
							_snake_case       :					int				       =   proj_codevector_dim
							_snake_case       :					Any				       =   diversity_loss_weight

							# ctc loss
							_snake_case       :					List[Any]				       =   ctc_loss_reduction
							_snake_case       :					str				       =   ctc_zero_infinity

							# adapter
							_snake_case       :					Tuple				       =   add_adapter
							_snake_case       :					Optional[int]				       =   adapter_kernel_size
							_snake_case       :					Dict				       =   adapter_stride
							_snake_case       :					Union[str, Any]				       =   num_adapter_layers
							_snake_case       :					List[str]				       =   output_hidden_size or hidden_size

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							_snake_case       :					str				       =   classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							_snake_case       :					int				       =   list(__snake_case				)
							_snake_case       :					str				       =   list(__snake_case				)
							_snake_case       :					Any				       =   list(__snake_case				)
							_snake_case       :					Tuple				       =   xvector_output_dim







		@property
		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

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






from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__lowerCAmelCase :Union[str, Any]     =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__lowerCAmelCase :Optional[Any]     =    typing.Union[np.floataa, int, float]  # noqa: UP007







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return np.sqrt(np.sum((np.asarray(UpperCAmelCase				) - np.asarray(UpperCAmelCase				)) ** 2				)				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return sum((va - va) ** 2 for va, va in zip(UpperCAmelCase  ,  UpperCAmelCase				)				) ** (1 / 2)


if __name__ == "__main__":







							def    A				(							):
												from timeit import timeit

												print("Without Numpy"				)
												print(
												    timeit(
												        "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)
												print("With Numpy"				)
												print(
												    timeit(
												        "euclidean_distance([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)

							benchmark()