
'''simple docstring'''



import functools
import operator

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


_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """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_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Union[str, Any]										=  '''wavlm'''




						def __init__(   self       ,  _lowercase=32       ,  _lowercase=768       ,  _lowercase=12       ,  _lowercase=12       ,  _lowercase=3_072       ,  _lowercase="gelu"       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=0.0       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=0.02       ,  _lowercase=1e-5       ,  _lowercase="group"       ,  _lowercase="gelu"       ,  _lowercase=(512, 512, 512, 512, 512, 512, 512)       ,  _lowercase=(5, 2, 2, 2, 2, 2, 2)       ,  _lowercase=(10, 3, 3, 3, 3, 2, 2)       ,  _lowercase=False       ,  _lowercase=128       ,  _lowercase=16       ,  _lowercase=320       ,  _lowercase=800       ,  _lowercase=False       ,  _lowercase=True       ,  _lowercase=0.05       ,  _lowercase=10       ,  _lowercase=2       ,  _lowercase=0.0       ,  _lowercase=10       ,  _lowercase=320       ,  _lowercase=2       ,  _lowercase=0.1       ,  _lowercase=100       ,  _lowercase=256       ,  _lowercase=256       ,  _lowercase=0.1       ,  _lowercase="mean"       ,  _lowercase=False       ,  _lowercase=False       ,  _lowercase=256       ,  _lowercase=(512, 512, 512, 512, 1_500)       ,  _lowercase=(5, 3, 3, 1, 1)       ,  _lowercase=(1, 2, 3, 1, 1)       ,  _lowercase=512       ,  _lowercase=80       ,  _lowercase=0       ,  _lowercase=1       ,  _lowercase=2       ,  _lowercase=False       ,  _lowercase=3       ,  _lowercase=2       ,  _lowercase=3       ,  _lowercase=None       ,  **_lowercase       ,  ):

												"""simple docstring"""



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

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

												# ctc loss
												_lowerCAmelCase    		=					ctc_loss_reduction
												_lowerCAmelCase    		=					ctc_zero_infinity

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

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

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




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



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


import warnings

from .generation import TFGenerationMixin







class 							lowerCAmelCase__   ( __lowercase						):
						# warning at import time
						warnings.warn(
						    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
						    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,						__lowercase		,						)



