






import functools
import operator

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


A__	:			int			   = logging.get_logger(__name__)

A__	:			List[Any]			   = {
    '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					(       A__	):

				"""simple docstring"""




				lowercase__											=       """wavlm"""




				def __init__(     self						:    Any,  lowerCamelCase						:    Dict=32,  lowerCamelCase						:    Union[str, Any]=768,  lowerCamelCase						:    Dict=12,  lowerCamelCase						:    List[Any]=12,  lowerCamelCase						:    Union[str, Any]=3_072,  lowerCamelCase						:    List[str]="gelu",  lowerCamelCase						:    Union[str, Any]=0.1,  lowerCamelCase						:    List[Any]=0.1,  lowerCamelCase						:    List[str]=0.1,  lowerCamelCase						:    List[Any]=0.0,  lowerCamelCase						:    int=0.1,  lowerCamelCase						:    List[Any]=0.1,  lowerCamelCase						:    Any=0.02,  lowerCamelCase						:    Union[str, Any]=1E-5,  lowerCamelCase						:    List[Any]="group",  lowerCamelCase						:    Any="gelu",  lowerCamelCase						:    Any=(512, 512, 512, 512, 512, 512, 512),  lowerCamelCase						:    Optional[Any]=(5, 2, 2, 2, 2, 2, 2),  lowerCamelCase						:    List[str]=(10, 3, 3, 3, 3, 2, 2),  lowerCamelCase						:    Optional[int]=False,  lowerCamelCase						:    Any=128,  lowerCamelCase						:    Optional[int]=16,  lowerCamelCase						:    int=320,  lowerCamelCase						:    Dict=800,  lowerCamelCase						:    int=False,  lowerCamelCase						:    List[Any]=True,  lowerCamelCase						:    str=0.05,  lowerCamelCase						:    Optional[int]=10,  lowerCamelCase						:    Tuple=2,  lowerCamelCase						:    List[str]=0.0,  lowerCamelCase						:    str=10,  lowerCamelCase						:    Optional[int]=320,  lowerCamelCase						:    int=2,  lowerCamelCase						:    Any=0.1,  lowerCamelCase						:    List[Any]=100,  lowerCamelCase						:    Optional[int]=256,  lowerCamelCase						:    int=256,  lowerCamelCase						:    Optional[int]=0.1,  lowerCamelCase						:    str="mean",  lowerCamelCase						:    Dict=False,  lowerCamelCase						:    Any=False,  lowerCamelCase						:    List[str]=256,  lowerCamelCase						:    str=(512, 512, 512, 512, 1_500),  lowerCamelCase						:    str=(5, 3, 3, 1, 1),  lowerCamelCase						:    Optional[int]=(1, 2, 3, 1, 1),  lowerCamelCase						:    Optional[Any]=512,  lowerCamelCase						:    List[Any]=80,  lowerCamelCase						:    List[str]=0,  lowerCamelCase						:    Any=1,  lowerCamelCase						:    Dict=2,  lowerCamelCase						:    Any=False,  lowerCamelCase						:    Optional[Any]=3,  lowerCamelCase						:    Tuple=2,  lowerCamelCase						:    int=3,  lowerCamelCase						:    str=None,  **lowerCamelCase						:    Union[str, Any],  ):

								'''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 				lowercase__   (     self						:    str  ):

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












def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[1]

				lowercase__ ,						lowercase__ ,						lowercase__    =			0, 0, 0
				lowercase__    =			ugly_nums[ia] * 2
				lowercase__    =			ugly_nums[ia] * 3
				lowercase__    =			ugly_nums[ia] * 5

				for _ in range(1		,   lowerCamelCase_				):
								lowercase__    =			min(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				)
								ugly_nums.append(lowerCamelCase_				)
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 2
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 3
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 5
				return ugly_nums[-1]


if __name__ == "__main__":
							from doctest import testmod

							testmod(verbose=True)
							print(F"{ugly_numbers(2_00) = }")




