



"""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_    (     _lowercase,   _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:int		=     {"""small""": (512, 8, 24), """medium""": (1024, 8, 36), """large""": (1536, 16, 48)}
						snake_case_,			snake_case_,			snake_case_					:Dict		=     MODELS[model_size]  # set model hyperparameters
						snake_case_					:List[str]		=     ImageGPTConfig(n_embd=_lowercase,   n_layer=_lowercase,   n_head=_lowercase      )
						snake_case_					:str		=     ImageGPTForCausalLM(_lowercase      )

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

						# Save pytorch-model
						snake_case_					:int		=     pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
						snake_case_					:Optional[int]		=     pytorch_dump_folder_path + """/""" + CONFIG_NAME
						print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""      )
						torch.save(model.state_dict(),   _lowercase      )
						print(f"""Save configuration file to {pytorch_config_dump_path}"""      )
						with open(_lowercase,   """w""",   encoding="""utf-8"""      ) as f:
												f.write(config.to_json_string()      )


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





"""simple docstring"""
def   A_    (     _lowercase,   _lowercase,   _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:Dict		=     int((lower_bound + upper_bound) // 2      )
						if array[r] == value:
												return r
						if lower_bound >= upper_bound:
												return -1
						if array[r] < value:
												return binary_search(_lowercase,   r + 1,   _lowercase,   _lowercase      )
						else:
												return binary_search(_lowercase,   _lowercase,   r - 1,   _lowercase      )
def   A_    (     _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:Any		=     0
						if matrix[index][0] == value:
												return [index, 0]
						while index < len(_lowercase      ) and matrix[index][0] < value:
												snake_case_					:List[str]		=     binary_search(matrix[index],   0,   len(matrix[index]      ) - 1,   _lowercase      )
												if r != -1:
																		return [index, r]
												index += 1
						return [-1, -1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()
