

import math

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


__a    :Dict      =			logging.get_logger(__name__)

__a    :Optional[Any]      =			{
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			int      		=       'data2vec-audio'





      def __init__(   self							:				Dict						,			UpperCAmelCase							:				List[Any]=32						,			UpperCAmelCase							:				Optional[int]=768						,			UpperCAmelCase							:				int=12						,			UpperCAmelCase							:				int=12						,			UpperCAmelCase							:				Optional[Any]=3072						,			UpperCAmelCase							:				Dict="gelu"						,			UpperCAmelCase							:				List[str]=0.1						,			UpperCAmelCase							:				Any=0.1						,			UpperCAmelCase							:				Union[str, Any]=0.1						,			UpperCAmelCase							:				List[str]=0.0						,			UpperCAmelCase							:				Optional[Any]=0.1						,			UpperCAmelCase							:				Dict=0.1						,			UpperCAmelCase							:				Any=0.02						,			UpperCAmelCase							:				int=1E-5						,			UpperCAmelCase							:				Optional[Any]="gelu"						,			UpperCAmelCase							:				Union[str, Any]=(512, 512, 512, 512, 512, 512, 512)						,			UpperCAmelCase							:				Optional[Any]=(5, 2, 2, 2, 2, 2, 2)						,			UpperCAmelCase							:				Optional[int]=(10, 3, 3, 3, 3, 2, 2)						,			UpperCAmelCase							:				int=False						,			UpperCAmelCase							:				Optional[int]=16						,			UpperCAmelCase							:				List[Any]=19						,			UpperCAmelCase							:				Tuple=5						,			UpperCAmelCase							:				List[str]=0.05						,			UpperCAmelCase							:				int=10						,			UpperCAmelCase							:				List[Any]=2						,			UpperCAmelCase							:				Optional[Any]=0.0						,			UpperCAmelCase							:				int=10						,			UpperCAmelCase							:				List[str]=0						,			UpperCAmelCase							:				List[Any]="sum"						,			UpperCAmelCase							:				int=False						,			UpperCAmelCase							:				List[str]=False						,			UpperCAmelCase							:				Tuple=256						,			UpperCAmelCase							:				Union[str, Any]=(512, 512, 512, 512, 1500)						,			UpperCAmelCase							:				Dict=(5, 3, 3, 1, 1)						,			UpperCAmelCase							:				Union[str, Any]=(1, 2, 3, 1, 1)						,			UpperCAmelCase							:				Optional[int]=512						,			UpperCAmelCase							:				int=0						,			UpperCAmelCase							:				Optional[int]=1						,			UpperCAmelCase							:				Dict=2						,			UpperCAmelCase							:				int=False						,			UpperCAmelCase							:				Optional[Any]=3						,			UpperCAmelCase							:				Optional[Any]=2						,			UpperCAmelCase							:				List[str]=3						,			UpperCAmelCase							:				str=None						,			**UpperCAmelCase							:				Union[str, Any]						,			):
            super().__init__(**UpperCAmelCase						,			pad_token_id=UpperCAmelCase						,			bos_token_id=UpperCAmelCase						,			eos_token_id=UpperCAmelCase	)
            A_						  =				hidden_size
            A_						  =				feat_extract_activation
            A_						  =				list(UpperCAmelCase	)
            A_						  =				list(UpperCAmelCase	)
            A_						  =				list(UpperCAmelCase	)
            A_						  =				conv_bias
            A_						  =				num_conv_pos_embeddings
            A_						  =				num_conv_pos_embedding_groups
            A_						  =				conv_pos_kernel_size
            A_						  =				len(self.conv_dim	)
            A_						  =				num_hidden_layers
            A_						  =				intermediate_size
            A_						  =				hidden_act
            A_						  =				num_attention_heads
            A_						  =				hidden_dropout
            A_						  =				attention_dropout
            A_						  =				activation_dropout
            A_						  =				feat_proj_dropout
            A_						  =				final_dropout
            A_						  =				layerdrop
            A_						  =				layer_norm_eps
            A_						  =				initializer_range
            A_						  =				vocab_size
            A_						  =				use_weighted_layer_sum

            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
            A_						  =				mask_time_prob
            A_						  =				mask_time_length
            A_						  =				mask_time_min_masks
            A_						  =				mask_feature_prob
            A_						  =				mask_feature_length
            A_						  =				mask_feature_min_masks

            # ctc loss
            A_						  =				ctc_loss_reduction
            A_						  =				ctc_zero_infinity

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

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

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







      @property
      def 		__A	(   self							:				Optional[Any]	):
            return math.prod(self.conv_stride	)




'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):







							def __init__(				self   :			Optional[int]      ,*_UpperCAmelCase   :			List[Any]      ,**_UpperCAmelCase   :			Union[str, Any]						):
									warnings.warn(
									    'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use GLPNImageProcessor instead.'      ,_UpperCAmelCase      ,)
									super().__init__(*_UpperCAmelCase      ,**_UpperCAmelCase						)




