





'''simple docstring'''

import functools
import operator

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


__lowerCamelCase : Tuple											=			logging.get_logger(__name__)

__lowerCamelCase : Tuple											=			{
    "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       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Optional[Any] 			=		'''wavlm'''




		def __init__(self      :	List[str]							,			A__      :	Any=3_2							,			A__      :	List[Any]=7_6_8							,			A__      :	List[Any]=1_2							,			A__      :	Tuple=1_2							,			A__      :	List[Any]=3_0_7_2							,			A__      :	List[str]="gelu"							,			A__      :	Dict=0.1							,			A__      :	List[str]=0.1							,			A__      :	Dict=0.1							,			A__      :	Dict=0.0							,			A__      :	str=0.1							,			A__      :	int=0.1							,			A__      :	List[str]=0.0_2							,			A__      :	Dict=1e-5							,			A__      :	Union[str, Any]="group"							,			A__      :	List[str]="gelu"							,			A__      :	Dict=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)							,			A__      :	Dict=(5, 2, 2, 2, 2, 2, 2)							,			A__      :	List[Any]=(1_0, 3, 3, 3, 3, 2, 2)							,			A__      :	Dict=False							,			A__      :	Any=1_2_8							,			A__      :	Any=1_6							,			A__      :	Optional[Any]=3_2_0							,			A__      :	int=8_0_0							,			A__      :	Dict=False							,			A__      :	List[Any]=True							,			A__      :	Any=0.0_5							,			A__      :	Union[str, Any]=1_0							,			A__      :	str=2							,			A__      :	Dict=0.0							,			A__      :	Dict=1_0							,			A__      :	Optional[int]=3_2_0							,			A__      :	List[str]=2							,			A__      :	Dict=0.1							,			A__      :	Optional[int]=1_0_0							,			A__      :	List[str]=2_5_6							,			A__      :	Optional[Any]=2_5_6							,			A__      :	List[str]=0.1							,			A__      :	List[Any]="mean"							,			A__      :	Union[str, Any]=False							,			A__      :	Optional[Any]=False							,			A__      :	Optional[int]=2_5_6							,			A__      :	str=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)							,			A__      :	List[str]=(5, 3, 3, 1, 1)							,			A__      :	Dict=(1, 2, 3, 1, 1)							,			A__      :	Any=5_1_2							,			A__      :	Union[str, Any]=8_0							,			A__      :	Any=0							,			A__      :	Tuple=1							,			A__      :	str=2							,			A__      :	Any=False							,			A__      :	Optional[int]=3							,			A__      :	int=2							,			A__      :	str=3							,			A__      :	List[str]=None							,			**A__      :	int							,			)					-> List[Any]:
						super().__init__(**A__							,			pad_token_id=A__							,			bos_token_id=A__							,			eos_token_id=A__				)
						lowercase         = hidden_size
						lowercase         = feat_extract_norm
						lowercase         = feat_extract_activation
						lowercase         = list(A__				)
						lowercase         = list(A__				)
						lowercase         = list(A__				)
						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(A__				)
						lowercase         = list(A__				)
						lowercase         = list(A__				)
						lowercase         = xvector_output_dim





		@property
		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Optional[int]:
						return functools.reduce(operator.mul							,			self.conv_stride							,			1				)











'''simple docstring'''

# Algorithm for the pigeonhole sorting









def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = min(lowerCAmelCase_							)  # min() finds the minimum value
				lowercase         = max(lowerCAmelCase_							)  # max() finds the maximum value

				lowercase         = max_val - min_val + 1  # size is difference of max and min values plus one

				# list of pigeonholes of size equal to the variable size
				lowercase         = [0] * size

				# Populate the pigeonholes.
				for x in a:
								assert isinstance(lowerCAmelCase_		,							lowerCAmelCase_							), "integers only please"
								holes[x - min_val] += 1

				# Putting the elements back into the array in an order.
				lowercase         = 0
				for count in range(lowerCAmelCase_							):
								while holes[count] > 0:
												holes[count] -= 1
												lowercase         = count + min_val
												i += 1







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = [8, 3, 2, 7, 4, 6, 8]
				pigeonhole_sort(lowerCAmelCase_							)
				print("Sorted order is:"		,							" ".join(lowerCAmelCase_							)							)


if __name__ == "__main__":
			main()




