"""simple docstring"""





import functools
import operator

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


_UpperCamelCase  :		Union[str, Any]           =       logging.get_logger(__name__)

_UpperCamelCase  :		Optional[int]           =       {
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class 	UpperCAmelCase_      (       A_):
      lowerCamelCase__						:       Optional[int]					    =    "wav2vec2"


      def __init__(  self						,     a=3_2						,     a=7_6_8						,     a=1_2						,     a=1_2						,     a=3_0_7_2						,     a="gelu"						,     a=0.1						,     a=0.1						,     a=0.1						,     a=0.0						,     a=0.0						,     a=0.1						,     a=0.1						,     a=0.02						,     a=1e-5						,     a="group"						,     a="gelu"						,     a=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)						,     a=(5, 2, 2, 2, 2, 2, 2)						,     a=(1_0, 3, 3, 3, 3, 2, 2)						,     a=False						,     a=1_2_8						,     a=1_6						,     a=False						,     a=True						,     a=0.05						,     a=1_0						,     a=2						,     a=0.0						,     a=1_0						,     a=0						,     a=3_2_0						,     a=2						,     a=0.1						,     a=1_0_0						,     a=2_5_6						,     a=2_5_6						,     a=0.1						,     a="sum"						,     a=False						,     a=False						,     a=2_5_6						,     a=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)						,     a=(5, 3, 3, 1, 1)						,     a=(1, 2, 3, 1, 1)						,     a=5_1_2						,     a=0						,     a=1						,     a=2						,     a=False						,     a=3						,     a=2						,     a=3						,     a=None						,     a=None						,     **a						,     )			->   Tuple:
             super().__init__(**a						,     pad_token_id=a						,     bos_token_id=a						,     eos_token_id=a				)
             lowercase__     :						Dict         =      hidden_size
             lowercase__     :						List[str]         =      feat_extract_norm
             lowercase__     :						Optional[Any]         =      feat_extract_activation
             lowercase__     :						Optional[int]         =      list(a				)
             lowercase__     :						List[Any]         =      list(a				)
             lowercase__     :						List[Any]         =      list(a				)
             lowercase__     :						Optional[int]         =      conv_bias
             lowercase__     :						Union[str, Any]         =      num_conv_pos_embeddings
             lowercase__     :						Dict         =      num_conv_pos_embedding_groups
             lowercase__     :						Tuple         =      len(self.conv_dim				)
             lowercase__     :						Any         =      num_hidden_layers
             lowercase__     :						Union[str, Any]         =      intermediate_size
             lowercase__     :						Optional[int]         =      hidden_act
             lowercase__     :						Tuple         =      num_attention_heads
             lowercase__     :						List[str]         =      hidden_dropout
             lowercase__     :						Tuple         =      attention_dropout
             lowercase__     :						Optional[int]         =      activation_dropout
             lowercase__     :						Optional[int]         =      feat_proj_dropout
             lowercase__     :						str         =      final_dropout
             lowercase__     :						List[str]         =      layerdrop
             lowercase__     :						Tuple         =      layer_norm_eps
             lowercase__     :						List[str]         =      initializer_range
             lowercase__     :						List[Any]         =      vocab_size
             lowercase__     :						str         =      do_stable_layer_norm
             lowercase__     :						str         =      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
             lowercase__     :						List[str]         =      apply_spec_augment
             lowercase__     :						List[str]         =      mask_time_prob
             lowercase__     :						List[Any]         =      mask_time_length
             lowercase__     :						Optional[int]         =      mask_time_min_masks
             lowercase__     :						Optional[Any]         =      mask_feature_prob
             lowercase__     :						Optional[int]         =      mask_feature_length
             lowercase__     :						Any         =      mask_feature_min_masks

             # parameters for pretraining with codevector quantized representations
             lowercase__     :						Optional[int]         =      num_codevectors_per_group
             lowercase__     :						Union[str, Any]         =      num_codevector_groups
             lowercase__     :						List[str]         =      contrastive_logits_temperature
             lowercase__     :						List[Any]         =      feat_quantizer_dropout
             lowercase__     :						List[Any]         =      num_negatives
             lowercase__     :						Optional[Any]         =      codevector_dim
             lowercase__     :						Union[str, Any]         =      proj_codevector_dim
             lowercase__     :						Optional[int]         =      diversity_loss_weight

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

             # adapter
             lowercase__     :						str         =      add_adapter
             lowercase__     :						List[Any]         =      adapter_kernel_size
             lowercase__     :						List[Any]         =      adapter_stride
             lowercase__     :						List[str]         =      num_adapter_layers
             lowercase__     :						int         =      output_hidden_size or hidden_size
             lowercase__     :						Union[str, Any]         =      adapter_attn_dim

             # SequenceClassification-specific parameter. Feel free to ignore for other classes.
             lowercase__     :						int         =      classifier_proj_size

             # XVector-specific parameters. Feel free to ignore for other classes.
             lowercase__     :						Optional[Any]         =      list(a				)
             lowercase__     :						int         =      list(a				)
             lowercase__     :						Dict         =      list(a				)
             lowercase__     :						Tuple         =      xvector_output_dim


      @property
      def 						_UpperCAmelCase			(  self				)			->   Union[str, Any]:
             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      (   __snake_case							:      List[str]   ):




   """simple docstring"""







   for param in module.parameters():
      _lowerCamelCase	:					Optional[Any]   =		False

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		"""cuda""" if torch.cuda.is_available() else """cpu"""
   if torch.backends.mps.is_available() and torch.backends.mps.is_built():
      _lowerCamelCase	:					Any   =		"""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      (   __snake_case							:      Union[str, Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		plt.imshow(__snake_case   )
   fig.axes.get_xaxis().set_visible(__snake_case   )
   fig.axes.get_yaxis().set_visible(__snake_case   )
   plt.show()





def 				_snake_case      (   ):




   """simple docstring"""







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

