






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							OmegaConf.load(a_)
							snake_case_				=							torch.load(a_							,     map_location='cpu')['model']
							snake_case_				=							list(state_dict.keys())

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

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

							snake_case_				=							config.model.params.first_stage_config.params
							snake_case_				=							config.model.params.unet_config.params

							snake_case_				=							VQModel(**a_).eval()
							vqvae.load_state_dict(a_)

							snake_case_				=							UNetLDMModel(**a_).eval()
							unet.load_state_dict(a_)

							snake_case_				=							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_							,     )

							snake_case_				=							LDMPipeline(a_							,     a_							,     a_)
							pipeline.save_pretrained(a_)


if __name__ == "__main__":
	lowercase  							=					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)
	lowercase  							=					parser.parse_args()

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













import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


lowercase  							=					logging.getLogger(__name__)




@dataclass
class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= field(
	    default=0.0       ,					metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''whether to use adafactor'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default='''linear'''       ,					metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}       ,					)





