

"""simple docstring"""




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
    __UpperCAmelCase =				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.',
    )
    __UpperCAmelCase =				parser.parse_args()

    __UpperCAmelCase =				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)








"""simple docstring"""




from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__UpperCAmelCase =				logging.get_logger(__name__)


__UpperCAmelCase =				TypeVar('DatasetType', Dataset, IterableDataset)
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase = None							,    __UpperCamelCase = None							,    __UpperCamelCase = None							,    __UpperCamelCase = None							,    __UpperCamelCase = "first_exhausted"							,    ):
 '''simple docstring'''





 from .arrow_dataset import Dataset
 from .iterable_dataset import IterableDataset

 if not datasets:
  raise ValueError("""Unable to interleave an empty list of datasets."""      )
 for i, dataset in enumerate(__UpperCamelCase      ):
  if not isinstance(__UpperCamelCase							,    (Dataset, IterableDataset)      ):
   if isinstance(__UpperCamelCase							,    (DatasetDict, IterableDatasetDict)      ):
    if not dataset:
     raise ValueError(
         F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         """is an empty dataset dictionary."""      )
    raise ValueError(
        F"Dataset at position {i} has at least one split: {list(__UpperCamelCase      )}\n"
        F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCamelCase      )      )}']"      )
   raise ValueError(
       F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCamelCase      ).__name__}."      )
  if i == 0:
   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[str]					   =					(
       (Dataset, IterableDataset) if isinstance(__UpperCamelCase							,    __UpperCamelCase      ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCamelCase							,    __UpperCamelCase      ):
   raise ValueError(
       F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."      )
 if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
  raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."      )
 if dataset_type is Dataset:
  return _interleave_map_style_datasets(
      __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    info=__UpperCamelCase							,    split=__UpperCamelCase							,    stopping_strategy=__UpperCamelCase      )
 else:
  return _interleave_iterable_datasets(
      __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    info=__UpperCamelCase							,    split=__UpperCamelCase							,    stopping_strategy=__UpperCamelCase      )




def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase = None							,    __UpperCamelCase = None							,    __UpperCamelCase = 0							,    ):
 '''simple docstring'''





 if not dsets:
  raise ValueError("""Unable to concatenate an empty list of datasets."""      )
 for i, dataset in enumerate(__UpperCamelCase      ):
  if not isinstance(__UpperCamelCase							,    (Dataset, IterableDataset)      ):
   if isinstance(__UpperCamelCase							,    (DatasetDict, IterableDatasetDict)      ):
    if not dataset:
     raise ValueError(
         F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         """is an empty dataset dictionary."""      )
    raise ValueError(
        F"Dataset at position {i} has at least one split: {list(__UpperCamelCase      )}\n"
        F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCamelCase      )      )}']"      )
   raise ValueError(
       F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCamelCase      ).__name__}."      )
  if i == 0:
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					(
       (Dataset, IterableDataset) if isinstance(__UpperCamelCase							,    __UpperCamelCase      ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCamelCase							,    __UpperCamelCase      ):
   raise ValueError(
       F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."      )
 if dataset_type is Dataset:
  return _concatenate_map_style_datasets(__UpperCamelCase							,    info=__UpperCamelCase							,    split=__UpperCamelCase							,    axis=__UpperCamelCase      )
 else:
  return _concatenate_iterable_datasets(__UpperCamelCase							,    info=__UpperCamelCase							,    split=__UpperCamelCase							,    axis=__UpperCamelCase      )





