






'''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     A    (      _A						):






 """simple docstring"""





 __a			:						Union[str, Any]   =						'''sew'''
 def __init__(							self			,     __lowerCAmelCase=32			,     __lowerCAmelCase=7_68			,     __lowerCAmelCase=12			,     __lowerCAmelCase=12			,     __lowerCAmelCase=30_72			,     __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, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)			,     __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=1_28			,     __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=2_56			,     __lowerCAmelCase=0			,     __lowerCAmelCase=1			,     __lowerCAmelCase=2			,     **__lowerCAmelCase			,     ):
       super().__init__(**UpperCamelCase__			,     pad_token_id=UpperCamelCase__			,     bos_token_id=UpperCamelCase__			,     eos_token_id=UpperCamelCase__ )
       UpperCamelCase_				:   int					    =							hidden_size
       UpperCamelCase_				:   Optional[int]					    =							feat_extract_norm
       UpperCamelCase_				:   Union[str, Any]					    =							feat_extract_activation
       UpperCamelCase_				:   Tuple					    =							list(UpperCamelCase__ )
       UpperCamelCase_				:   List[str]					    =							list(UpperCamelCase__ )
       UpperCamelCase_				:   int					    =							list(UpperCamelCase__ )
       UpperCamelCase_				:   List[Any]					    =							conv_bias
       UpperCamelCase_				:   str					    =							num_conv_pos_embeddings
       UpperCamelCase_				:   List[Any]					    =							num_conv_pos_embedding_groups
       UpperCamelCase_				:   int					    =							len(self.conv_dim )
       UpperCamelCase_				:   Any					    =							num_hidden_layers
       UpperCamelCase_				:   Tuple					    =							intermediate_size
       UpperCamelCase_				:   List[str]					    =							squeeze_factor
       UpperCamelCase_				:   List[str]					    =							hidden_act
       UpperCamelCase_				:   Dict					    =							num_attention_heads
       UpperCamelCase_				:   Union[str, Any]					    =							hidden_dropout
       UpperCamelCase_				:   str					    =							attention_dropout
       UpperCamelCase_				:   List[str]					    =							activation_dropout
       UpperCamelCase_				:   Tuple					    =							feat_proj_dropout
       UpperCamelCase_				:   str					    =							final_dropout
       UpperCamelCase_				:   List[Any]					    =							layerdrop
       UpperCamelCase_				:   List[str]					    =							layer_norm_eps
       UpperCamelCase_				:   Optional[int]					    =							initializer_range
       UpperCamelCase_				:   Tuple					    =							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
       UpperCamelCase_				:   Dict					    =							apply_spec_augment
       UpperCamelCase_				:   List[str]					    =							mask_time_prob
       UpperCamelCase_				:   Optional[Any]					    =							mask_time_length
       UpperCamelCase_				:   List[Any]					    =							mask_time_min_masks
       UpperCamelCase_				:   Any					    =							mask_feature_prob
       UpperCamelCase_				:   Union[str, Any]					    =							mask_feature_length
       UpperCamelCase_				:   List[Any]					    =							mask_feature_min_masks

       # ctc loss
       UpperCamelCase_				:   List[str]					    =							ctc_loss_reduction
       UpperCamelCase_				:   Union[str, Any]					    =							ctc_zero_infinity

       # sequence classification
       UpperCamelCase_				:   Tuple					    =							use_weighted_layer_sum
       UpperCamelCase_				:   List[str]					    =							classifier_proj_size





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














'''simple docstring'''

from datetime import datetime

import matplotlib.pyplot as plt
import torch



def 							snake_case					(     a_							: List[Any]	)       ->				Any:





      """simple docstring"""

      for param in module.parameters():
            UpperCamelCase_				:   Dict					    =							False



def 							snake_case					(     )       ->				List[str]:





      """simple docstring"""

      UpperCamelCase_				:   List[str]					    =							"""cuda""" if torch.cuda.is_available() else """cpu"""
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            UpperCamelCase_				:   int					    =							"""mps"""
      if device == "mps":
            print(
                """WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
                """ errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
                """ with generations."""	)
      return device



def 							snake_case					(     a_							: str	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							plt.imshow(a_	)
      fig.axes.get_xaxis().set_visible(a_	)
      fig.axes.get_yaxis().set_visible(a_	)
      plt.show()



def 							snake_case					(     )       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							datetime.now()
      UpperCamelCase_				:   int					    =							current_time.strftime("""%H:%M:%S"""	)
      return timestamp






