code: 
  
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  __lowerCAmelCase							:			List[Any]				={"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Optional[int]				=[
        "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "RegNetForImageClassification",
        "RegNetModel",
        "RegNetPreTrainedModel",
    ]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			str				=[
        "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFRegNetForImageClassification",
        "TFRegNetModel",
        "TFRegNetPreTrainedModel",
    ]
  
  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Dict				=[
        "FlaxRegNetForImageClassification",
        "FlaxRegNetModel",
        "FlaxRegNetPreTrainedModel",
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_regnet import (
          REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          RegNetForImageClassification,
          RegNetModel,
          RegNetPreTrainedModel,
      )
  
    try:
      if not is_tf_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_tf_regnet import (
          TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRegNetForImageClassification,
          TFRegNetModel,
          TFRegNetPreTrainedModel,
      )
  
    try:
      if not is_flax_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_flax_regnet import (
          FlaxRegNetForImageClassification,
          FlaxRegNetModel,
          FlaxRegNetPreTrainedModel,
      )
  
  
  else:
    import sys
  
    __lowerCAmelCase							:			Dict				=_LazyModule(__name__, globals()["__file__"], _import_structure)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Union[str, Any]=2_56 ):
      A__       =    min(img.size )
  
      if s < target_image_size:
          raise ValueError(F"min dim for image {s} < {target_image_size}" )
  
      A__       =    target_image_size / s
      A__       =    (round(r * img.size[1] ), round(r * img.size[0] ))
      A__       =    TF.resize(_lowerCamelCase		, _lowerCamelCase		, interpolation=PIL.Image.LANCZOS )
      A__       =    TF.center_crop(_lowerCamelCase		, output_size=2 * [target_image_size] )
      A__       =    torch.unsqueeze(T.ToTensor()(_lowerCamelCase )		, 0 )
      return img
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
      A__       =    2.0 * x - 1.0
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Tuple=True		, _lowerCamelCase      :      int="RGB" ):
      A__       =    x.detach().cpu()
      if process:
          A__       =    post_process_tensor(_lowerCamelCase )
      A__       =    x.numpy()
      if process:
          A__       =    (2_55 * x).astype(np.uinta )
      A__       =    Image.fromarray(_lowerCamelCase )
      if not x.mode == mode:
          A__       =    x.convert(_lowerCamelCase )
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Any ):
      A__       =    torch.clamp(_lowerCamelCase		, -1.0		, 1.0 )
      A__       =    (x + 1.0) / 2.0
      A__       =    x.permute(1		, 2		, 0 )
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
      A__       =    post_process_tensor(x.squeeze() )
      return x.permute(2		, 0		, 1 ).unsqueeze(0 )
  
  
  
  