'''simple docstring'''
from typing import TYPE_CHECKING

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


lowercase__  :      List[str]     =					{
    '''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNetConfig''', '''ResNetOnnxConfig''']
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Any     =					[
          '''RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ResNetForImageClassification''',
          '''ResNetModel''',
          '''ResNetPreTrainedModel''',
          '''ResNetBackbone''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Any     =					[
          '''TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFResNetForImageClassification''',
          '''TFResNetModel''',
          '''TFResNetPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Optional[Any]     =					[
          '''FlaxResNetForImageClassification''',
          '''FlaxResNetModel''',
          '''FlaxResNetPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_resnet import (
                RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ResNetBackbone,
                ResNetForImageClassification,
                ResNetModel,
                ResNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_resnet import (
                TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFResNetForImageClassification,
                TFResNetModel,
                TFResNetPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
      import sys

      lowercase__  :      int     =					_LazyModule(__name__, globals()["__file__"], _import_structure)






'''simple docstring'''
from datetime import datetime

import matplotlib.pyplot as plt
import torch



def  __lowerCamelCase       (      _UpperCamelCase  :  Union[str, Any]					):


      '''simple docstring'''


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



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  '''cuda''' if torch.cuda.is_available() else '''cpu'''
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            UpperCAmelCase_            =  '''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       (      _UpperCamelCase  :  Union[str, Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  plt.imshow(_UpperCamelCase					)
      fig.axes.get_xaxis().set_visible(_UpperCamelCase					)
      fig.axes.get_yaxis().set_visible(_UpperCamelCase					)
      plt.show()





def  __lowerCamelCase       (      ):


      '''simple docstring'''


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





