


from ... import PretrainedConfig


UpperCAmelCase      =   {
    """sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
 __snake_case												=  "nezha"



 def __init__(     self		,				_UpperCAmelCase=2_11_28		,				_UpperCAmelCase=7_68		,				_UpperCAmelCase=12		,				_UpperCAmelCase=12		,				_UpperCAmelCase=30_72		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=5_12		,				_UpperCAmelCase=64		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-12		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=True		,				**_UpperCAmelCase		,				):
       super().__init__(pad_token_id=_UpperCAmelCase		,				bos_token_id=_UpperCAmelCase		,				eos_token_id=_UpperCAmelCase		,				**_UpperCAmelCase )

       snake_case_	=				vocab_size
       snake_case_	=				hidden_size
       snake_case_	=				num_hidden_layers
       snake_case_	=				num_attention_heads
       snake_case_	=				hidden_act
       snake_case_	=				intermediate_size
       snake_case_	=				hidden_dropout_prob
       snake_case_	=				attention_probs_dropout_prob
       snake_case_	=				max_position_embeddings
       snake_case_	=				max_relative_position
       snake_case_	=				type_vocab_size
       snake_case_	=				initializer_range
       snake_case_	=				layer_norm_eps
       snake_case_	=				classifier_dropout
       snake_case_	=				use_cache



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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  "wavlm"



 def __init__(     self		,				_UpperCAmelCase=32		,				_UpperCAmelCase=7_68		,				_UpperCAmelCase=12		,				_UpperCAmelCase=12		,				_UpperCAmelCase=30_72		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-5		,				_UpperCAmelCase="group"		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)		,				_UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,				_UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)		,				_UpperCAmelCase=False		,				_UpperCAmelCase=1_28		,				_UpperCAmelCase=16		,				_UpperCAmelCase=3_20		,				_UpperCAmelCase=8_00		,				_UpperCAmelCase=False		,				_UpperCAmelCase=True		,				_UpperCAmelCase=0.05		,				_UpperCAmelCase=10		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=10		,				_UpperCAmelCase=3_20		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=1_00		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase="mean"		,				_UpperCAmelCase=False		,				_UpperCAmelCase=False		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 15_00)		,				_UpperCAmelCase=(5, 3, 3, 1, 1)		,				_UpperCAmelCase=(1, 2, 3, 1, 1)		,				_UpperCAmelCase=5_12		,				_UpperCAmelCase=80		,				_UpperCAmelCase=0		,				_UpperCAmelCase=1		,				_UpperCAmelCase=2		,				_UpperCAmelCase=False		,				_UpperCAmelCase=3		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       super().__init__(**_UpperCAmelCase		,				pad_token_id=_UpperCAmelCase		,				bos_token_id=_UpperCAmelCase		,				eos_token_id=_UpperCAmelCase )
       snake_case_	=				hidden_size
       snake_case_	=				feat_extract_norm
       snake_case_	=				feat_extract_activation
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				conv_bias
       snake_case_	=				num_buckets
       snake_case_	=				max_bucket_distance
       snake_case_	=				num_conv_pos_embeddings
       snake_case_	=				num_conv_pos_embedding_groups
       snake_case_	=				len(self.conv_dim )
       snake_case_	=				num_hidden_layers
       snake_case_	=				intermediate_size
       snake_case_	=				hidden_act
       snake_case_	=				num_attention_heads
       snake_case_	=				hidden_dropout
       snake_case_	=				attention_dropout
       snake_case_	=				activation_dropout
       snake_case_	=				feat_proj_dropout
       snake_case_	=				final_dropout
       snake_case_	=				layerdrop
       snake_case_	=				layer_norm_eps
       snake_case_	=				initializer_range
       snake_case_	=				num_ctc_classes
       snake_case_	=				vocab_size
       snake_case_	=				do_stable_layer_norm
       snake_case_	=				use_weighted_layer_sum
       snake_case_	=				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
       snake_case_	=				apply_spec_augment
       snake_case_	=				mask_time_prob
       snake_case_	=				mask_time_length
       snake_case_	=				mask_time_min_masks
       snake_case_	=				mask_feature_prob
       snake_case_	=				mask_feature_length

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

       # ctc loss
       snake_case_	=				ctc_loss_reduction
       snake_case_	=				ctc_zero_infinity

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

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

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





 @property
 def 						UpperCamelCase__					(     self ):
       return functools.reduce(operator.mul		,				self.conv_stride		,				1 )