from datetime import datetime

import matplotlib.pyplot as plt
import torch




def    lowerCAmelCase_  (		lowercase:				Union[str, Any]		)     ->					Optional[Any]:







 '''simple docstring'''
 for param in module.parameters():
  _UpperCamelCase:  Dict    						=			False




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







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			'''cuda''' if torch.cuda.is_available() else '''cpu'''
 if torch.backends.mps.is_available() and torch.backends.mps.is_built():
  _UpperCamelCase:  Union[str, 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    lowerCAmelCase_  (		lowercase:				Any		)     ->					Tuple:







 '''simple docstring'''
 _UpperCamelCase:  str    						=			plt.imshow(lowercase		)
 fig.axes.get_xaxis().set_visible(lowercase		)
 fig.axes.get_yaxis().set_visible(lowercase		)
 plt.show()





def    lowerCAmelCase_  (		)     ->					Dict:







 '''simple docstring'''
 _UpperCamelCase:  Union[str, Any]    						=			datetime.now()
 _UpperCamelCase:  str    						=			current_time.strftime('''%H:%M:%S'''		)
 return timestamp
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_           =				{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				[
      '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MegaForCausalLM''',
      '''MegaForMaskedLM''',
      '''MegaForMultipleChoice''',
      '''MegaForQuestionAnswering''',
      '''MegaForSequenceClassification''',
      '''MegaForTokenClassification''',
      '''MegaModel''',
      '''MegaPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mega import (
        MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegaForCausalLM,
        MegaForMaskedLM,
        MegaForMultipleChoice,
        MegaForQuestionAnswering,
        MegaForSequenceClassification,
        MegaForTokenClassification,
        MegaModel,
        MegaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_           =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)