






"""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_     :				Union[str, Any]         =				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_     :				Any         =				parser.parse_args()

     UpperCAmelCase_     :				Optional[int]         =				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"""






import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def       _A    (__a							)				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{}
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer(example['''content''']					,       truncation=__a							)['''input_ids''']
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	len(example['''content''']							) / len(output['''input_ids''']							)
    return output


UpperCAmelCase_     :				Tuple         =				HfArgumentParser(PretokenizationArguments)
UpperCAmelCase_     :				Tuple         =				parser.parse_args()
if args.num_workers is None:
  UpperCAmelCase_     :				Tuple         =				multiprocessing.cpu_count()
UpperCAmelCase_     :				str         =				AutoTokenizer.from_pretrained(args.tokenizer_dir)

UpperCAmelCase_     :				Tuple         =				time.time()
UpperCAmelCase_     :				List[Any]         =				load_dataset(args.dataset_name, split="""train""")
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

UpperCAmelCase_     :				Dict         =				time.time()
UpperCAmelCase_     :				Dict         =				ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')

UpperCAmelCase_     :				str         =				time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')


