



'''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 							__UpperCAmelCase				(				UpperCamelCase__				:Tuple   ,				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:Union[str, Any]			)		->						Any:
				# Construct configuration depending on size
				snake_case__      :   Tuple      =    {'''small''': (512, 8, 24), '''medium''': (1024, 8, 36), '''large''': (1536, 16, 48)}
				snake_case__  ,      snake_case__  ,      snake_case__      :   int      =    MODELS[model_size]  # set model hyperparameters
				snake_case__      :   Any      =    ImageGPTConfig(n_embd=UpperCamelCase__   ,				n_layer=UpperCamelCase__   ,				n_head=UpperCamelCase__			)
				snake_case__      :   str      =    ImageGPTForCausalLM(UpperCamelCase__			)

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

				# Save pytorch-model
				snake_case__      :   Tuple      =    pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				snake_case__      :   str      =    pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''			)
				torch.save(model.state_dict()   ,				UpperCamelCase__			)
				print(F'''Save configuration file to {pytorch_config_dump_path}'''			)
				with open(UpperCamelCase__   ,				'''w'''   ,				encoding='''utf-8'''			) as f:
								f.write(config.to_json_string()			)


if __name__ == "__main__":
				_lowercase       :						Optional[Any]						=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       :						Dict						=parser.parse_args()
				convert_imagegpt_checkpoint_to_pytorch(
				    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
				)









'''simple docstring'''



def 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						Any:
				snake_case__      :   Dict      =    len(UpperCamelCase__			)
				for _ in range(
				    0   ,				int(((no_of_elements - 1) / 2) + 1			)			):  # we don't need to traverse to end of list as
								for j in range(0   ,				no_of_elements - 1			):
												if (
												    lst[j + 1] < lst[j]
												):  # applying bubble sort algorithm from left to right (or forwards)
																snake_case__      :   int      =    lst[j + 1]
																snake_case__      :   Tuple      =    lst[j]
																snake_case__      :   Tuple      =    temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																snake_case__      :   Optional[Any]      =    lst[no_of_elements - 1 - j]
																snake_case__      :   Tuple      =    lst[no_of_elements - 2 - j]
																snake_case__      :   Any      =    temp
				return lst


if __name__ == "__main__":
				print("enter the list to be sorted")
				_lowercase       :						Any						=[int(x) for x in input().split()]  # inputing elements of the list in one line
				_lowercase       :						Tuple						=double_sort(lst)
				print("the sorted list is")
				print(sorted_lst)




