


'''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_					(				__SCREAMING_SNAKE_CASE :					List[Any]	,   __SCREAMING_SNAKE_CASE :					List[Any]	,   __SCREAMING_SNAKE_CASE :					Union[str, Any]       )     ->  List[Any]:
					# Construct configuration depending on size
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = {'''small''': (5_12, 8, 24), '''medium''': (10_24, 8, 36), '''large''': (15_36, 16, 48)}
					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							str			   = MODELS[model_size]  # set model hyperparameters
					__SCREAMING_SNAKE_CASE					:							str			   = ImageGPTConfig(n_embd=__SCREAMING_SNAKE_CASE	,   n_layer=__SCREAMING_SNAKE_CASE	,   n_head=__SCREAMING_SNAKE_CASE       )
					__SCREAMING_SNAKE_CASE					:							str			   = ImageGPTForCausalLM(__SCREAMING_SNAKE_CASE       )

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

					# Save pytorch-model
					__SCREAMING_SNAKE_CASE					:							str			   = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
					print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""       )
					torch.save(model.state_dict()	,   __SCREAMING_SNAKE_CASE       )
					print(f"""Save configuration file to {pytorch_config_dump_path}"""       )
					with open(__SCREAMING_SNAKE_CASE	,   '''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'''






import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_A      =     TypeVar("""T""", bound="""TaskTemplate""")






@dataclass(frozen=snake_case		)
class      SCREAMING_SNAKE_CASE_	( abc.ABC		):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			__a					:      str
			__a					:      ClassVar[Features]
			__a					:      ClassVar[Features]




			def 			_snake_case   (      self					,      lowercase       )			->						T:


								'''simple docstring'''






								return copy.deepcopy(self       )




			@property
			def 			_snake_case   (      self       )			->						Features:


								'''simple docstring'''






								return Features(**self.input_schema					,      **self.label_schema       )




			@property
			@abc.abstractmethod
			def 			_snake_case   (      self       )			->						Dict[str, str]:


								'''simple docstring'''






								raise NotImplementedError




			@classmethod
			def 			_snake_case   (      cls					,      lowercase       )			->						T:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = {f.name for f in dataclasses.fields(cls       )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}       )


