



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 ,						_lowerCAmelCase ,						_lowerCAmelCase):
					# Construct configuration depending on size
					UpperCamelCase_			= {"small": (5_12, 8, 24), "medium": (10_24, 8, 36), "large": (15_36, 16, 48)}
					UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= MODELS[model_size]  # set model hyperparameters
					UpperCamelCase_			= ImageGPTConfig(n_embd=_lowerCAmelCase ,						n_layer=_lowerCAmelCase ,						n_head=_lowerCAmelCase)
					UpperCamelCase_			= ImageGPTForCausalLM(_lowerCAmelCase)

					# Load weights from numpy
					load_tf_weights_in_imagegpt(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)

					# Save pytorch-model
					UpperCamelCase_			= pytorch_dump_folder_path + "/" + WEIGHTS_NAME
					UpperCamelCase_			= 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__":
	UpperCAmelCase :				Optional[int]							=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."""
	)
	UpperCAmelCase :				List[Any]							=parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)

"""simple docstring"""







def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	if len(__UpperCAmelCase      ) > 1:
		_lowercase			:     Dict      					=				len(__UpperCAmelCase      ) // 2  # Finds the middle of the array
		_lowercase			:     List[str]      					=				arr[
		    :middle_length
		]  # Creates an array of the elements in the first half.
		_lowercase			:     Optional[int]      					=				arr[
		    middle_length:
		]  # Creates an array of the elements in the second half.
		_lowercase			:     Optional[Any]      					=				len(__UpperCAmelCase      )
		_lowercase			:     Optional[int]      					=				len(__UpperCAmelCase      )
		merge(__UpperCAmelCase      )  # Starts sorting the left.
		merge(__UpperCAmelCase      )  # Starts sorting the right
		_lowercase			:     Dict      					=				0  # Left Counter
		_lowercase			:     List[str]      					=				0  # Right Counter
		_lowercase			:     Optional[Any]      					=				0  # Position Counter
		while (
		    left_index < left_size and right_index < right_size
		):  # Runs until the lowers size of the left and right are sorted.
			if left_array[left_index] < right_array[right_index]:
				_lowercase			:     Optional[int]      					=				left_array[left_index]
				left_index += 1
			else:
				_lowercase			:     int      					=				right_array[right_index]
				right_index += 1
			index += 1
		while (
		    left_index < left_size
		):  # Adds the left over elements in the left half of the array
			_lowercase			:     Any      					=				left_array[left_index]
			left_index += 1
			index += 1
		while (
		    right_index < right_size
		):  # Adds the left over elements in the right half of the array
			_lowercase			:     str      					=				right_array[right_index]
			right_index += 1
			index += 1
	return arr


if __name__ == "__main__":
						import doctest

						doctest.testmod()



