





"""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  ):
						# Construct configuration depending on size
						_UpperCamelCase     :   Optional[Any]      =       {'''small''': (512, 8, 24), '''medium''': (1024, 8, 36), '''large''': (1536, 16, 48)}
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Optional[int]      =       MODELS[model_size]  # set model hyperparameters
						_UpperCamelCase     :   List[str]      =       ImageGPTConfig(n_embd=_lowerCAmelCase			,		n_layer=_lowerCAmelCase			,		n_head=_lowerCAmelCase  )
						_UpperCamelCase     :   int      =       ImageGPTForCausalLM(_lowerCAmelCase  )

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

						# Save pytorch-model
						_UpperCamelCase     :   Tuple      =       pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
						_UpperCamelCase     :   List[str]      =       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_	 =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_	 =parser.parse_args()
						convert_imagegpt_checkpoint_to_pytorch(
						    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
						)








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase :				List[Any]							={
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				int							=["""RetriBertTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				str							=[
	    """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RetriBertModel""",
	    """RetriBertPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
	from .tokenization_retribert import RetriBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_retribert_fast import RetriBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_retribert import (
		    RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RetriBertModel,
		    RetriBertPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase :				List[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
