
import functools
import operator

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


lowercase  : Optional[Any]						   =							logging.get_logger(__name__)

lowercase  : Optional[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 __snake_case		(				lowerCamelCase__					):
    _a		:						int=    "wavlm"
    def __init__(  self		,snake_case=32		,snake_case=768		,snake_case=12		,snake_case=12		,snake_case=3072		,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=(512, 512, 512, 512, 512, 512, 512)		,snake_case=(5, 2, 2, 2, 2, 2, 2)		,snake_case=(10, 3, 3, 3, 3, 2, 2)		,snake_case=False		,snake_case=128		,snake_case=16		,snake_case=320		,snake_case=800		,snake_case=False		,snake_case=True		,snake_case=0.05		,snake_case=10		,snake_case=2		,snake_case=0.0		,snake_case=10		,snake_case=320		,snake_case=2		,snake_case=0.1		,snake_case=100		,snake_case=256		,snake_case=256		,snake_case=0.1		,snake_case="mean"		,snake_case=False		,snake_case=False		,snake_case=256		,snake_case=(512, 512, 512, 512, 1500)		,snake_case=(5, 3, 3, 1, 1)		,snake_case=(1, 2, 3, 1, 1)		,snake_case=512		,snake_case=80		,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		,):



           '''simple docstring'''







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

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

           # ctc loss
           lowercase							:					Optional[int]	     =				ctc_loss_reduction
           lowercase							:					int	     =				ctc_zero_infinity

           # adapter
           lowercase							:					int	     =				add_adapter
           lowercase							:					Optional[int]	     =				adapter_kernel_size
           lowercase							:					Tuple	     =				adapter_stride
           lowercase							:					Optional[Any]	     =				num_adapter_layers
           lowercase							:					int	     =				output_hidden_size or hidden_size

           # SequenceClassification-specific parameter. Feel free to ignore for other classes.
           lowercase							:					Optional[int]	     =				classifier_proj_size

           # XVector-specific parameters. Feel free to ignore for other classes.
           lowercase							:					str	     =				list(__lowerCamelCase				)
           lowercase							:					List[Any]	     =				list(__lowerCamelCase				)
           lowercase							:					List[str]	     =				list(__lowerCamelCase				)
           lowercase							:					Optional[Any]	     =				xvector_output_dim
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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

print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
