



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
	)





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase :				Union[str, Any]							=logging.get_logger(__name__)


class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """speech-encoder-decoder"""
				lowercase__     =    True

				def __init__(			self  ,			**snake_case__     ):

									'''simple docstring'''


									super().__init__(**snake_case__     )
									if "encoder" not in kwargs or "decoder" not in kwargs:
														raise ValueError(
														    F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
														    F""" `decoder` sub-configurations are passed, but only {kwargs}"""     )

									UpperCamelCase_			= kwargs.pop("encoder"     )
									UpperCamelCase_			= encoder_config.pop("model_type"     )
									UpperCamelCase_			= kwargs.pop("decoder"     )
									UpperCamelCase_			= decoder_config.pop("model_type"     )

									UpperCamelCase_			= AutoConfig.for_model(snake_case__  ,			**snake_case__     )
									UpperCamelCase_			= AutoConfig.for_model(snake_case__  ,			**snake_case__     )
									UpperCamelCase_			= True

				@classmethod
				def    _lowerCamelCase       (			cls  ,			snake_case__  ,			snake_case__  ,			**snake_case__     ):

									'''simple docstring'''


									logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"     )
									UpperCamelCase_			= True
									UpperCamelCase_			= True

									return cls(encoder=encoder_config.to_dict()  ,			decoder=decoder_config.to_dict()  ,			**snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= copy.deepcopy(self.__dict__     )
									UpperCamelCase_			= self.encoder.to_dict()
									UpperCamelCase_			= self.decoder.to_dict()
									UpperCamelCase_			= self.__class__.model_type
									return output
