


'''simple docstring'''






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def    _snake_case  (	A					,       A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      OmegaConf.load(A			)
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)['''model''']
							lowerCAmelCase__			  =      list(state_dict.keys()			)

							# extract state_dict for VQVAE
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      '''first_stage_model.'''
							for key in keys:
														if key.startswith(A			):
																					lowerCAmelCase__			  =      state_dict[key]

    # extract state_dict for UNetLDM
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      '''model.diffusion_model.'''
							for key in keys:
														if key.startswith(A			):
																					lowerCAmelCase__			  =      state_dict[key]

							lowerCAmelCase__			  =      config.model.params.first_stage_config.params
							lowerCAmelCase__			  =      config.model.params.unet_config.params

							lowerCAmelCase__			  =      VQModel(**A			).eval()
							vqvae.load_state_dict(A			)

							lowerCAmelCase__			  =      UNetLDMModel(**A			).eval()
							unet.load_state_dict(A			)

							lowerCAmelCase__			  =      DDIMScheduler(
							    timesteps=config.model.params.timesteps					,       beta_schedule='''scaled_linear'''					,       beta_start=config.model.params.linear_start					,       beta_end=config.model.params.linear_end					,       clip_sample=A					,       )

							lowerCAmelCase__			  =      LDMPipeline(A					,       A					,       A			)
							pipeline.save_pretrained(A			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				argparse.ArgumentParser()
		parser.add_argument('''--checkpoint_path''', type=str, required=True)
		parser.add_argument('''--config_path''', type=str, required=True)
		parser.add_argument('''--output_path''', type=str, required=True)
		__UpperCAmelCase       							=				parser.parse_args()

		convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)





'''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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		str:
			# Construct model
			if openai_config_file == "":
						A_									= OpenAIGPTConfig()
			else:
						A_									= OpenAIGPTConfig.from_json_file(SCREAMING_SNAKE_CASE_     )
			A_									= OpenAIGPTModel(SCREAMING_SNAKE_CASE_     )

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

			# Save pytorch-model
			A_									= pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			A_									= 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__":
							_SCREAMING_SNAKE_CASE			=						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."
							    ),
							)
							_SCREAMING_SNAKE_CASE			=						parser.parse_args()
							convert_openai_checkpoint_to_pytorch(
							    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
							)




