from datetime import datetime

import matplotlib.pyplot as plt
import torch



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]			)							->      Any:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      )							->      Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   """cuda""" if torch.cuda.is_available() else """cpu"""
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    SCREAMING_SNAKE_CASE__    							=   """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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str			)							->      Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   plt.imshow(lowercase__			)
  fig.axes.get_xaxis().set_visible(lowercase__			)
  fig.axes.get_yaxis().set_visible(lowercase__			)
  plt.show()






def 			__SCREAMING_SNAKE_CASE						(      )							->      List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   datetime.now()
  SCREAMING_SNAKE_CASE__    							=   current_time.strftime("""%H:%M:%S"""			)
  return timestamp

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


__lowerCamelCase    :				List[Any]							     =	logging.get_logger(__name__)

__lowerCamelCase    :				str							     =	{
    '''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"timesformer"




   def __init__(					self    :       Any ,			_lowercase    :       Tuple=2_24 ,			_lowercase    :       Tuple=16 ,			_lowercase    :       List[Any]=3 ,			_lowercase    :       str=8 ,			_lowercase    :       Optional[Any]=7_68 ,			_lowercase    :       List[str]=12 ,			_lowercase    :       List[str]=12 ,			_lowercase    :       Dict=30_72 ,			_lowercase    :       List[str]="gelu" ,			_lowercase    :       List[str]=0.0 ,			_lowercase    :       List[str]=0.0 ,			_lowercase    :       Tuple=0.02 ,			_lowercase    :       List[Any]=1E-6 ,			_lowercase    :       List[str]=True ,			_lowercase    :       Dict="divided_space_time" ,			_lowercase    :       Union[str, Any]=0 ,			**_lowercase    :       Union[str, Any] ,			):



     """simple docstring"""






     super().__init__(**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   image_size
     SCREAMING_SNAKE_CASE__    							=   patch_size
     SCREAMING_SNAKE_CASE__    							=   num_channels
     SCREAMING_SNAKE_CASE__    							=   num_frames

     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   num_hidden_layers
     SCREAMING_SNAKE_CASE__    							=   num_attention_heads
     SCREAMING_SNAKE_CASE__    							=   intermediate_size
     SCREAMING_SNAKE_CASE__    							=   hidden_act
     SCREAMING_SNAKE_CASE__    							=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   initializer_range
     SCREAMING_SNAKE_CASE__    							=   layer_norm_eps
     SCREAMING_SNAKE_CASE__    							=   qkv_bias

     SCREAMING_SNAKE_CASE__    							=   attention_type
     SCREAMING_SNAKE_CASE__    							=   drop_path_rate
