code: 
  
  
  
  
  
  '''simple docstring'''
  
  import argparse
  
  import torch
  
  from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
  from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[str, Any]      , __lowerCamelCase				:					List[Any]      , __lowerCamelCase				:					List[str]					)		->Union[str, Any]:
      # Construct configuration depending on size
      _SCREAMING_SNAKE_CASE             =    {"""small""": (512, 8, 24), """medium""": (1024, 8, 36), """large""": (1536, 16, 48)}
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    MODELS[model_size]  # set model hyperparameters
      _SCREAMING_SNAKE_CASE             =    ImageGPTConfig(n_embd=__lowerCamelCase      , n_layer=__lowerCamelCase      , n_head=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    ImageGPTForCausalLM(__lowerCamelCase					)
  
      # Load weights from numpy
      load_tf_weights_in_imagegpt(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
  
      # Save pytorch-model
      _SCREAMING_SNAKE_CASE             =    pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
      _SCREAMING_SNAKE_CASE             =    pytorch_dump_folder_path + """/""" + CONFIG_NAME
      print(F'Save PyTorch model to {pytorch_weights_dump_path}'					)
      torch.save(model.state_dict()      , __lowerCamelCase					)
      print(F'Save configuration file to {pytorch_config_dump_path}'					)
      with open(__lowerCamelCase      , """w"""      , encoding="""utf-8"""					) as f:
          f.write(config.to_json_string()					)
  
  
  if __name__ == "__main__":
         lowercase_			       =				argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             """--imagegpt_checkpoint_path""",
             default=None,
             type=str,
             required=True,
             help="""Path to the TensorFlow checkpoint path.""",
         )
         parser.add_argument(
             """--model_size""",
             default=None,
             type=str,
             required=True,
             help="""Size of the model (can be either 'small', 'medium' or 'large').""",
         )
         parser.add_argument(
             """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
         )
         lowercase_			       =				parser.parse_args()
         convert_imagegpt_checkpoint_to_pytorch(
             args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
         )
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					float      , __lowerCamelCase				:					float      , __lowerCamelCase				:					float					)		->list[int]:
      if hue < 0 or hue > 360:
          raise Exception("""hue should be between 0 and 360"""					)
  
      if saturation < 0 or saturation > 1:
          raise Exception("""saturation should be between 0 and 1"""					)
  
      if value < 0 or value > 1:
          raise Exception("""value should be between 0 and 1"""					)
  
      _SCREAMING_SNAKE_CASE             =    value * saturation
      _SCREAMING_SNAKE_CASE             =    hue / 60
      _SCREAMING_SNAKE_CASE             =    chroma * (1 - abs(hue_section % 2 - 1					))
      _SCREAMING_SNAKE_CASE             =    value - chroma
  
      if hue_section >= 0 and hue_section <= 1:
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
      elif hue_section > 1 and hue_section <= 2:
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
      elif hue_section > 2 and hue_section <= 3:
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
      elif hue_section > 3 and hue_section <= 4:
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
      elif hue_section > 4 and hue_section <= 5:
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
      else:
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
  
      return [red, green, blue]
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int      , __lowerCamelCase				:					int      , __lowerCamelCase				:					int					)		->list[float]:
      if red < 0 or red > 255:
          raise Exception("""red should be between 0 and 255"""					)
  
      if green < 0 or green > 255:
          raise Exception("""green should be between 0 and 255"""					)
  
      if blue < 0 or blue > 255:
          raise Exception("""blue should be between 0 and 255"""					)
  
      _SCREAMING_SNAKE_CASE             =    red / 255
      _SCREAMING_SNAKE_CASE             =    green / 255
      _SCREAMING_SNAKE_CASE             =    blue / 255
      _SCREAMING_SNAKE_CASE             =    max(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    value - min(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    0 if value == 0 else chroma / value
  
      if chroma == 0:
          _SCREAMING_SNAKE_CASE             =    0.0
      elif value == float_red:
          _SCREAMING_SNAKE_CASE             =    60 * (0 + (float_green - float_blue) / chroma)
      elif value == float_green:
          _SCREAMING_SNAKE_CASE             =    60 * (2 + (float_blue - float_red) / chroma)
      else:
          _SCREAMING_SNAKE_CASE             =    60 * (4 + (float_red - float_green) / chroma)
  
      _SCREAMING_SNAKE_CASE             =    (hue + 360) % 360
  
      return [hue, saturation, value]
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					list[float]      , __lowerCamelCase				:					list[float]					)		->bool:
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[0] - hsv_a[0]					) < 0.2
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[1] - hsv_a[1]					) < 0.002
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[2] - hsv_a[2]					) < 0.002
  
      return check_hue and check_saturation and check_value
  
  
  
  