






"""simple docstring"""
from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class      UpperCamelCase_			(__A	):





	def __init__( self   :					str      ,      lowerCAmelCase_   :					NestedDataStructureLike[PathLike]      ,      lowerCAmelCase_   :					Optional[NamedSplit] = None      ,      lowerCAmelCase_   :					Optional[Features] = None      ,      lowerCAmelCase_   :					str = None      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      **lowerCAmelCase_   :					List[Any]      ,      )		->     Tuple:
				super().__init__(
				    lowerCAmelCase_      ,      split=lowerCAmelCase_      ,      features=lowerCAmelCase_      ,      cache_dir=lowerCAmelCase_      ,      keep_in_memory=lowerCAmelCase_      ,      streaming=lowerCAmelCase_      ,      num_proc=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )
				UpperCAmelCase_	:					Union[str, Any]     	=   path_or_paths if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else {self.split: path_or_paths}
				UpperCAmelCase_	:					Optional[Any]     	=   Text(
				    cache_dir=lowerCAmelCase_      ,      data_files=lowerCAmelCase_      ,      features=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[Any]:
				# Build iterable dataset
				if self.streaming:
							UpperCAmelCase_	:					Any     	=   self.builder.as_streaming_dataset(split=self.split		)
				# Build regular (map-style) dataset
				else:
							UpperCAmelCase_	:					Dict     	=   None
							UpperCAmelCase_	:					Tuple     	=   None
							UpperCAmelCase_	:					str     	=   None
							UpperCAmelCase_	:					Optional[Any]     	=   None

							self.builder.download_and_prepare(
							    download_config=lowerCAmelCase_      ,      download_mode=lowerCAmelCase_      ,      verification_mode=lowerCAmelCase_      ,      base_path=lowerCAmelCase_      ,      num_proc=self.num_proc      ,      )
							UpperCAmelCase_	:					Optional[int]     	=   self.builder.as_dataset(
							    split=self.split      ,      verification_mode=lowerCAmelCase_      ,      in_memory=self.keep_in_memory		)
				return dataset







'''simple docstring'''

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
 lowerCamelCase				:  int    		=      argparse.ArgumentParser()

 parser.add_argument(
     '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
 )
 # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
 parser.add_argument(
     '--original_config_file',
     default=None,
     type=str,
     help='The YAML config file corresponding to the original architecture.',
 )
 parser.add_argument(
     '--num_in_channels',
     default=None,
     type=int,
     help='The number of input channels. If `None` number of input channels will be automatically inferred.',
 )
 parser.add_argument(
     '--scheduler_type',
     default='pndm',
     type=str,
     help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
 )
 parser.add_argument(
     '--pipeline_type',
     default=None,
     type=str,
     help=(
         'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
         '. If `None` pipeline will be automatically inferred.'
     ),
 )
 parser.add_argument(
     '--image_size',
     default=None,
     type=int,
     help=(
         'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
         ' Base. Use 768 for Stable Diffusion v2.'
     ),
 )
 parser.add_argument(
     '--prediction_type',
     default=None,
     type=str,
     help=(
         'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
         ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
     ),
 )
 parser.add_argument(
     '--extract_ema',
     action='store_true',
     help=(
         'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
         ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
         ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
     ),
 )
 parser.add_argument(
     '--upcast_attention',
     action='store_true',
     help=(
         'Whether the attention computation should always be upcasted. This is necessary when running stable'
         ' diffusion 2.1.'
     ),
 )
 parser.add_argument(
     '--from_safetensors',
     action='store_true',
     help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
 )
 parser.add_argument(
     '--to_safetensors',
     action='store_true',
     help='Whether to store pipeline in safetensors format or not.',
 )
 parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
 parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
 parser.add_argument(
     '--stable_unclip',
     type=str,
     default=None,
     required=False,
     help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
 )
 parser.add_argument(
     '--stable_unclip_prior',
     type=str,
     default=None,
     required=False,
     help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
 )
 parser.add_argument(
     '--clip_stats_path',
     type=str,
     help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
     required=False,
 )
 parser.add_argument(
     '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
 )
 parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
 parser.add_argument(
     '--vae_path',
     type=str,
     default=None,
     required=False,
     help='Set to a path, hub id to an already converted vae to not convert it again.',
 )
 lowerCamelCase				:  str    		=      parser.parse_args()

 lowerCamelCase				:  Tuple    		=      download_from_original_stable_diffusion_ckpt(
     checkpoint_path=args.checkpoint_path,
     original_config_file=args.original_config_file,
     image_size=args.image_size,
     prediction_type=args.prediction_type,
     model_type=args.pipeline_type,
     extract_ema=args.extract_ema,
     scheduler_type=args.scheduler_type,
     num_in_channels=args.num_in_channels,
     upcast_attention=args.upcast_attention,
     from_safetensors=args.from_safetensors,
     device=args.device,
     stable_unclip=args.stable_unclip,
     stable_unclip_prior=args.stable_unclip_prior,
     clip_stats_path=args.clip_stats_path,
     controlnet=args.controlnet,
     vae_path=args.vae_path,
 )

 if args.half:
  pipe.to(torch_dtype=torch.floataa)

 if args.controlnet:
  # only save the controlnet model
  pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
 else:
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




