





import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


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







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def __init__(  self			:   int							,    *A			:   Optional[int]							,    **A			:   int		):
         warnings.warn(
             """The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
             """ Please use PerceiverImageProcessor instead."""							,    A							,    )
         super().__init__(*A							,    **A		)













import functools
import operator

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


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

__UpperCAmelCase	:       Union[str, Any]    	=							{
    "asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					"""sew-d"""




       def __init__(  self			:   Dict							,    A			:   Any=32							,    A			:   Dict=768							,    A			:   Optional[Any]=12							,    A			:   Union[str, Any]=12							,    A			:   Union[str, Any]=3_072							,    A			:   Optional[Any]=2							,    A			:   Union[str, Any]=512							,    A			:   List[Any]=256							,    A			:   Dict=True							,    A			:   Union[str, Any]=True							,    A			:   Optional[int]=("p2c", "c2p")							,    A			:   str="layer_norm"							,    A			:   Dict="gelu_python"							,    A			:   Tuple=0.1							,    A			:   Any=0.1							,    A			:   Tuple=0.1							,    A			:   Optional[int]=0.0							,    A			:   Any=0.1							,    A			:   Any=0.02							,    A			:   Dict=1E-7							,    A			:   str=1E-5							,    A			:   int="group"							,    A			:   int="gelu"							,    A			:   str=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)							,    A			:   Union[str, Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)							,    A			:   List[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)							,    A			:   Optional[int]=False							,    A			:   int=128							,    A			:   int=16							,    A			:   Optional[Any]=True							,    A			:   List[Any]=0.05							,    A			:   Any=10							,    A			:   Dict=2							,    A			:   List[Any]=0.0							,    A			:   Union[str, Any]=10							,    A			:   int=0							,    A			:   List[Any]="mean"							,    A			:   Union[str, Any]=False							,    A			:   Any=False							,    A			:   Optional[int]=256							,    A			:   List[Any]=0							,    A			:   Any=1							,    A			:   List[Any]=2							,    **A			:   List[Any]							,    ):
         super().__init__(**A							,    pad_token_id=A							,    bos_token_id=A							,    eos_token_id=A		)
         __snake_case:						Optional[int]			  =						hidden_size
         __snake_case:						str			  =						feat_extract_norm
         __snake_case:						int			  =						feat_extract_activation
         __snake_case:						str			  =						list(A		)
         __snake_case:						Any			  =						list(A		)
         __snake_case:						str			  =						list(A		)
         __snake_case:						Union[str, Any]			  =						conv_bias
         __snake_case:						int			  =						num_conv_pos_embeddings
         __snake_case:						str			  =						num_conv_pos_embedding_groups
         __snake_case:						List[Any]			  =						len(self.conv_dim		)
         __snake_case:						List[str]			  =						num_hidden_layers
         __snake_case:						Union[str, Any]			  =						intermediate_size
         __snake_case:						Dict			  =						squeeze_factor
         __snake_case:						List[Any]			  =						max_position_embeddings
         __snake_case:						List[Any]			  =						position_buckets
         __snake_case:						List[str]			  =						share_att_key
         __snake_case:						int			  =						relative_attention
         __snake_case:						Union[str, Any]			  =						norm_rel_ebd
         __snake_case:						List[str]			  =						list(A		)
         __snake_case:						Tuple			  =						hidden_act
         __snake_case:						List[Any]			  =						num_attention_heads
         __snake_case:						str			  =						hidden_dropout
         __snake_case:						int			  =						attention_dropout
         __snake_case:						Dict			  =						activation_dropout
         __snake_case:						Any			  =						feat_proj_dropout
         __snake_case:						int			  =						final_dropout
         __snake_case:						List[Any]			  =						layer_norm_eps
         __snake_case:						List[str]			  =						feature_layer_norm_eps
         __snake_case:						List[Any]			  =						initializer_range
         __snake_case:						List[Any]			  =						vocab_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)`,"""
               f'''but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'''
               f'''= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __snake_case:						List[Any]			  =						apply_spec_augment
         __snake_case:						List[Any]			  =						mask_time_prob
         __snake_case:						str			  =						mask_time_length
         __snake_case:						List[str]			  =						mask_time_min_masks
         __snake_case:						str			  =						mask_feature_prob
         __snake_case:						Optional[int]			  =						mask_feature_length
         __snake_case:						Dict			  =						mask_feature_min_masks

         # ctc loss
         __snake_case:						Any			  =						ctc_loss_reduction
         __snake_case:						str			  =						ctc_zero_infinity

         # sequence classification
         __snake_case:						Optional[Any]			  =						use_weighted_layer_sum
         __snake_case:						List[Any]			  =						classifier_proj_size






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






