
'''simple docstring'''







import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Any:
	# Construct model
	if openai_config_file == "":
		_snake_case   						=		OpenAIGPTConfig()
	else:
		_snake_case   						=		OpenAIGPTConfig.from_json_file(__A	)
	_snake_case   						=		OpenAIGPTModel(__A	)

	# Load weights from numpy
	load_tf_weights_in_openai_gpt(__A			,				__A			,				__A	)

	# Save pytorch-model
	_snake_case   						=		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
	_snake_case   						=		pytorch_dump_folder_path + '/' + CONFIG_NAME
	print(F'Save PyTorch model to {pytorch_weights_dump_path}'	)
	torch.save(model.state_dict()			,				__A	)
	print(F'Save configuration file to {pytorch_config_dump_path}'	)
	with open(__A			,				'w'			,				encoding='utf-8'	) as f:
		f.write(config.to_json_string()	)


if __name__ == "__main__":
					lowercase    :							str      =					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--openai_checkpoint_folder_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path to the TensorFlow checkpoint path.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)
					parser.add_argument(
					    "--openai_config_file",
					    default="",
					    type=str,
					    help=(
					        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
					        "This specifies the model architecture."
					    ),
					)
					lowercase    :							int      =					parser.parse_args()
					convert_openai_checkpoint_to_pytorch(
					    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
					)



'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

