




import functools
import operator

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


UpperCAmelCase__				     = logging.get_logger(__name__)

UpperCAmelCase__				     = {
    "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 (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       int       	=		'wavlm'






						def __init__( self :     Union[str, Any]				,							lowerCamelCase__ :     str=32				,							lowerCamelCase__ :     int=768				,							lowerCamelCase__ :     List[Any]=12				,							lowerCamelCase__ :     Dict=12				,							lowerCamelCase__ :     List[Any]=3_072				,							lowerCamelCase__ :     Optional[Any]="gelu"				,							lowerCamelCase__ :     Optional[Any]=0.1				,							lowerCamelCase__ :     Any=0.1				,							lowerCamelCase__ :     Optional[Any]=0.1				,							lowerCamelCase__ :     List[Any]=0.0				,							lowerCamelCase__ :     Union[str, Any]=0.1				,							lowerCamelCase__ :     Tuple=0.1				,							lowerCamelCase__ :     List[Any]=0.0_2				,							lowerCamelCase__ :     Any=1e-5				,							lowerCamelCase__ :     Dict="group"				,							lowerCamelCase__ :     Optional[int]="gelu"				,							lowerCamelCase__ :     Dict=(512, 512, 512, 512, 512, 512, 512)				,							lowerCamelCase__ :     List[Any]=(5, 2, 2, 2, 2, 2, 2)				,							lowerCamelCase__ :     Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)				,							lowerCamelCase__ :     Any=False				,							lowerCamelCase__ :     List[str]=128				,							lowerCamelCase__ :     Optional[Any]=16				,							lowerCamelCase__ :     Union[str, Any]=320				,							lowerCamelCase__ :     Optional[int]=800				,							lowerCamelCase__ :     int=False				,							lowerCamelCase__ :     Tuple=True				,							lowerCamelCase__ :     List[Any]=0.0_5				,							lowerCamelCase__ :     Optional[Any]=10				,							lowerCamelCase__ :     int=2				,							lowerCamelCase__ :     str=0.0				,							lowerCamelCase__ :     Optional[Any]=10				,							lowerCamelCase__ :     Optional[Any]=320				,							lowerCamelCase__ :     Any=2				,							lowerCamelCase__ :     Tuple=0.1				,							lowerCamelCase__ :     Dict=100				,							lowerCamelCase__ :     Any=256				,							lowerCamelCase__ :     Dict=256				,							lowerCamelCase__ :     Optional[Any]=0.1				,							lowerCamelCase__ :     Optional[int]="mean"				,							lowerCamelCase__ :     List[Any]=False				,							lowerCamelCase__ :     Tuple=False				,							lowerCamelCase__ :     Tuple=256				,							lowerCamelCase__ :     Union[str, Any]=(512, 512, 512, 512, 1_500)				,							lowerCamelCase__ :     List[str]=(5, 3, 3, 1, 1)				,							lowerCamelCase__ :     str=(1, 2, 3, 1, 1)				,							lowerCamelCase__ :     Any=512				,							lowerCamelCase__ :     List[Any]=80				,							lowerCamelCase__ :     Any=0				,							lowerCamelCase__ :     str=1				,							lowerCamelCase__ :     List[Any]=2				,							lowerCamelCase__ :     Optional[int]=False				,							lowerCamelCase__ :     Optional[Any]=3				,							lowerCamelCase__ :     Dict=2				,							lowerCamelCase__ :     Optional[Any]=3				,							lowerCamelCase__ :     Tuple=None				,							**lowerCamelCase__ :     Dict				,							)    ->						Tuple:
													"""simple docstring"""


													super().__init__(**lowerCamelCase__				,							pad_token_id=lowerCamelCase__				,							bos_token_id=lowerCamelCase__				,							eos_token_id=lowerCamelCase__				)
													__lowercase      =    hidden_size
													__lowercase      =    feat_extract_norm
													__lowercase      =    feat_extract_activation
													__lowercase      =    list(lowerCamelCase__				)
													__lowercase      =    list(lowerCamelCase__				)
													__lowercase      =    list(lowerCamelCase__				)
													__lowercase      =    conv_bias
													__lowercase      =    num_buckets
													__lowercase      =    max_bucket_distance
													__lowercase      =    num_conv_pos_embeddings
													__lowercase      =    num_conv_pos_embedding_groups
													__lowercase      =    len(self.conv_dim				)
													__lowercase      =    num_hidden_layers
													__lowercase      =    intermediate_size
													__lowercase      =    hidden_act
													__lowercase      =    num_attention_heads
													__lowercase      =    hidden_dropout
													__lowercase      =    attention_dropout
													__lowercase      =    activation_dropout
													__lowercase      =    feat_proj_dropout
													__lowercase      =    final_dropout
													__lowercase      =    layerdrop
													__lowercase      =    layer_norm_eps
													__lowercase      =    initializer_range
													__lowercase      =    num_ctc_classes
													__lowercase      =    vocab_size
													__lowercase      =    do_stable_layer_norm
													__lowercase      =    use_weighted_layer_sum
													__lowercase      =    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
													__lowercase      =    apply_spec_augment
													__lowercase      =    mask_time_prob
													__lowercase      =    mask_time_length
													__lowercase      =    mask_time_min_masks
													__lowercase      =    mask_feature_prob
													__lowercase      =    mask_feature_length

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

													# ctc loss
													__lowercase      =    ctc_loss_reduction
													__lowercase      =    ctc_zero_infinity

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

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

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






						@property
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						int:
													"""simple docstring"""


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












from __future__ import annotations







def 		_A(       UpperCamelCase__							:			list[int]				,       UpperCamelCase__							:			list[int]				,       UpperCamelCase__							:			int				)    -> tuple[float, list[float]]:
							'''simple docstring'''


							__lowercase      =    list(range(len(UpperCamelCase__				)				)				)
							__lowercase      =    [v / w for v, w in zip(UpperCamelCase__				,       UpperCamelCase__				)]
							index.sort(key=lambda UpperCamelCase__				: ratio[i]				,       reverse=UpperCamelCase__				)

							__lowercase      =    0
							__lowercase      =    [0] * len(UpperCamelCase__				)
							for i in index:
														if weight[i] <= capacity:
																					__lowercase      =    1
																					max_value += value[i]
																					capacity -= weight[i]
														else:
																					__lowercase      =    capacity / weight[i]
																					max_value += value[i] * capacity / weight[i]
																					break

							return max_value, fractions


if __name__ == "__main__":
			import doctest

			doctest.testmod()






