

"""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 		A    (       snake_case__						,			snake_case__						,			snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= {"""small""": (5_12, 8, 24), """medium""": (10_24, 8, 36), """large""": (15_36, 16, 48)}
			SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= MODELS[model_size]  # set model hyperparameters
			SCREAMING_SNAKE_CASE__									= ImageGPTConfig(n_embd=snake_case__						,			n_layer=snake_case__						,			n_head=snake_case__      )
			SCREAMING_SNAKE_CASE__									= ImageGPTForCausalLM(snake_case__      )

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

			# 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()						,			snake_case__      )
			print(f"""Save configuration file to {pytorch_config_dump_path}"""      )
			with open(snake_case__						,			"""w"""						,			encoding="""utf-8"""      ) as f:
						f.write(config.to_json_string()      )


if __name__ == "__main__":
						A_							:     Dict     =	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."
						)
						A_							:     str     =	parser.parse_args()
						convert_imagegpt_checkpoint_to_pytorch(
						    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
						)




"""simple docstring"""




def 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__      ):




			'''simple docstring'''


			if start == end:
						return start if lst[start] > item else start + 1
			if start > end:
						return start

			SCREAMING_SNAKE_CASE__									= (start + end) // 2
			if lst[mid] < item:
						return binary_search(snake_case__						,			snake_case__						,			mid + 1						,			snake_case__      )
			elif lst[mid] > item:
						return binary_search(snake_case__						,			snake_case__						,			snake_case__						,			mid - 1      )
			else:
						return mid

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= len(snake_case__      )

			for index in range(1						,			snake_case__      ):
						SCREAMING_SNAKE_CASE__									= lst[index]
						SCREAMING_SNAKE_CASE__									= binary_search(snake_case__						,			snake_case__						,			0						,			index - 1      )
						SCREAMING_SNAKE_CASE__									= lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

			return lst

def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


			if not left:
						return right

			if not right:
						return left

			if left[0] < right[0]:
						return [left[0], *merge(left[1:]						,			snake_case__      )]

			return [right[0], *merge(snake_case__						,			right[1:]      )]

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= len(snake_case__      )
			SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= [], []
			SCREAMING_SNAKE_CASE__									= [lst[0]]
			SCREAMING_SNAKE_CASE__									= []
			SCREAMING_SNAKE_CASE__									= 1
			while i < length:
						if lst[i] < lst[i - 1]:
									runs.append(snake_case__      )
									SCREAMING_SNAKE_CASE__									= [lst[i]]
						else:
									new_run.append(lst[i]      )
						i += 1
			runs.append(snake_case__      )

			for run in runs:
						sorted_runs.append(insertion_sort(snake_case__      )      )
			for run in sorted_runs:
						SCREAMING_SNAKE_CASE__									= merge(snake_case__						,			snake_case__      )

			return sorted_array

def 		A    (       ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= [5, 9, 10, 3, -4, 5, 1_78, 92, 46, -18, 0, 7]
			SCREAMING_SNAKE_CASE__									= tim_sort(snake_case__      )
			print(snake_case__      )


if __name__ == "__main__":
						main()


