




"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Union[str, Any]     =      '''data2vec-text'''

 def __init__(					self			:			List[str]       ,				_UpperCAmelCase			:			List[str]=30_522       ,				_UpperCAmelCase			:			str=768       ,				_UpperCAmelCase			:			List[Any]=12       ,				_UpperCAmelCase			:			Optional[Any]=12       ,				_UpperCAmelCase			:			Union[str, Any]=3_072       ,				_UpperCAmelCase			:			Optional[int]="gelu"       ,				_UpperCAmelCase			:			Any=0.1       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			str=512       ,				_UpperCAmelCase			:			Dict=2       ,				_UpperCAmelCase			:			Dict=0.02       ,				_UpperCAmelCase			:			str=1E-1_2       ,				_UpperCAmelCase			:			List[str]=1       ,				_UpperCAmelCase			:			Optional[Any]=0       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			Optional[Any]="absolute"       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			str=None       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       super().__init__(pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       _A     =  vocab_size
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  hidden_act
       _A     =  intermediate_size
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  max_position_embeddings
       _A     =  type_vocab_size
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  position_embedding_type
       _A     =  use_cache
       _A     =  classifier_dropout





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 @property
 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       if self.task == "multiple-choice":
             _A     =  {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             _A     =  {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
           ]						)







"""simple docstring"""

from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      for param in module.parameters():
            _A     =  False






def 		_snake_case	(   )       ->   Tuple:






      '''simple docstring'''


      _A     =  'cuda' if torch.cuda.is_available() else 'cpu'
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            _A     =  '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				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  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	(   )       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  datetime.now()
      _A     =  current_time.strftime('%H:%M:%S'				)
      return timestamp

