"""simple docstring"""


import functools
import operator

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


__UpperCamelCase									=			logging.get_logger(__name__)

__UpperCamelCase									=			{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "sew"







       def __init__( self,							lowerCAmelCase__=32,							lowerCAmelCase__=768,							lowerCAmelCase__=12,							lowerCAmelCase__=12,							lowerCAmelCase__=3072,							lowerCAmelCase__=2,							lowerCAmelCase__="gelu",							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.0,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.02,							lowerCAmelCase__=1e-5,							lowerCAmelCase__="group",							lowerCAmelCase__="gelu",							lowerCAmelCase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512),							lowerCAmelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1),							lowerCAmelCase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1),							lowerCAmelCase__=False,							lowerCAmelCase__=128,							lowerCAmelCase__=16,							lowerCAmelCase__=True,							lowerCAmelCase__=0.05,							lowerCAmelCase__=10,							lowerCAmelCase__=2,							lowerCAmelCase__=0.0,							lowerCAmelCase__=10,							lowerCAmelCase__=0,							lowerCAmelCase__="mean",							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=256,							lowerCAmelCase__=0,							lowerCAmelCase__=1,							lowerCAmelCase__=2,							**lowerCAmelCase__,							)      ->      str:
            super().__init__(**lowerCAmelCase__,							pad_token_id=lowerCAmelCase__,							bos_token_id=lowerCAmelCase__,							eos_token_id=lowerCAmelCase__)
            snake_case_					       =					hidden_size
            snake_case_					       =					feat_extract_norm
            snake_case_					       =					feat_extract_activation
            snake_case_					       =					list(lowerCAmelCase__)
            snake_case_					       =					list(lowerCAmelCase__)
            snake_case_					       =					list(lowerCAmelCase__)
            snake_case_					       =					conv_bias
            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_					       =					squeeze_factor
            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_					       =					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_					       =					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
            snake_case_					       =					mask_feature_min_masks

            # ctc loss
            snake_case_					       =					ctc_loss_reduction
            snake_case_					       =					ctc_zero_infinity

            # sequence classification
            snake_case_					       =					use_weighted_layer_sum
            snake_case_					       =					classifier_proj_size







       @property
       def 		a_						( self)      ->      Optional[Any]:
            return functools.reduce(operator.mul,							self.conv_stride,							1)









"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_snake_case          =   logging.get_logger(__name__)






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Any							,						*UpperCAmelCase__		:						Optional[Any]							,						**UpperCAmelCase__		:						Tuple							)					->       None:
   warnings.warn(
       """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
       """ use CLIPImageProcessor instead."""							,						UpperCAmelCase__							,						)
   super().__init__(*UpperCAmelCase__							,						**UpperCAmelCase__							)

