



import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel

def      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       Tuple						,       _lowercase    :       int)							->	List[Any]:







			"""simple docstring"""
			a__       :						int							=  OmegaConf.load(_lowercase)
			a__       :						Union[str, Any]							=  torch.load(_lowercase						,       map_location="""cpu""")["""model"""]
			a__       :						str							=  list(state_dict.keys())

			# extract state_dict for VQVAE
			a__       :						List[Any]							=  {}
			a__       :						List[Any]							=  """first_stage_model."""
			for key in keys:
						if key.startswith(_lowercase):
									a__       :						Union[str, Any]							=  state_dict[key]

    # extract state_dict for UNetLDM
			a__       :						Optional[Any]							=  {}
			a__       :						Union[str, Any]							=  """model.diffusion_model."""
			for key in keys:
						if key.startswith(_lowercase):
									a__       :						Union[str, Any]							=  state_dict[key]

			a__       :						List[Any]							=  config.model.params.first_stage_config.params
			a__       :						Optional[Any]							=  config.model.params.unet_config.params

			a__       :						Any							=  VQModel(**_lowercase).eval()
			vqvae.load_state_dict(_lowercase)

			a__       :						Any							=  UNetLDMModel(**_lowercase).eval()
			unet.load_state_dict(_lowercase)

			a__       :						Union[str, Any]							=  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=_lowercase						,       )

			a__       :						Tuple							=  LDMPipeline(_lowercase						,       _lowercase						,       _lowercase)
			pipeline.save_pretrained(_lowercase)


if __name__ == "__main__":
		_lowercase		:				str      		=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		:				Any      		=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		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[float]      =					field(
						    default=0.0 ,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "Whether to SortishSamler or not."}		)
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "whether to use adafactor"}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(default=A__ ,     metadata={"help": "Dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Attention dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default="linear" ,     metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}"} ,     )




