




import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()

					parser.add_argument(
					    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
					)
					parser.add_argument(
					    "--original_config_file",
					    type=str,
					    required=True,
					    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(
					    "--image_size",
					    default=5_12,
					    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(
					    "--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.)")







					def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str]      )     ->       Any:





									"""simple docstring"""


									if string == "True":
													return True
									elif string == "False":
													return False
									else:
													raise ValueError(F"""could not parse string as bool {string}"""      )

					parser.add_argument(
					    "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
					)

					parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

					__A        =    parser.parse_args()

					__A        =    download_controlnet_from_original_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    extract_ema=args.extract_ema,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    use_linear_projection=args.use_linear_projection,
					    cross_attention_dim=args.cross_attention_dim,
					)


					controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)








import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Any=1024      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase       ,						__lowerCamelCase		       =    [], []

				__lowerCamelCase		       =    list(zip(UpperCamelCase__ ,     UpperCamelCase__      )      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    sorted_examples[0]

				def is_too_big(UpperCamelCase__				:      List[str]      ):
								return tok(UpperCamelCase__ ,     return_tensors='pt'      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								__lowerCamelCase		       =    new_src + ' ' + src
								__lowerCamelCase		       =    new_tgt + ' ' + tgt
								if is_too_big(UpperCamelCase__      ) or is_too_big(UpperCamelCase__      ):  # cant fit, finalize example
												finished_src.append(UpperCamelCase__      )
												finished_tgt.append(UpperCamelCase__      )
												__lowerCamelCase       ,						__lowerCamelCase		       =    src, tgt
								else:  # can fit, keep adding
												__lowerCamelCase       ,						__lowerCamelCase		       =    cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(UpperCamelCase__      )
								finished_tgt.append(UpperCamelCase__      )
				return finished_src, finished_tgt







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path(UpperCamelCase__      )
				save_path.mkdir(exist_ok=UpperCamelCase__      )
				for split in ["train"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase       ,						__lowerCamelCase		       =    pack_examples(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""packed {split} split from {len(UpperCamelCase__      )} examples -> {len(UpperCamelCase__      )}."""      )
								Path(save_path / F"""{split}.source"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
								Path(save_path / F"""{split}.target"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
				for split in ["val", "test"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.source"""      )
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.target"""      )







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser()
				parser.add_argument('--tok_name' ,     type=UpperCamelCase__ ,     help='like facebook/bart-large-cnn,t5-base, etc.'      )
				parser.add_argument('--max_seq_len' ,     type=UpperCamelCase__ ,     default=128      )
				parser.add_argument('--data_dir' ,     type=UpperCamelCase__      )
				parser.add_argument('--save_path' ,     type=UpperCamelCase__      )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(UpperCamelCase__ ,     Path(args.data_dir      ) ,     args.max_seq_len ,     args.save_path      )


if __name__ == "__main__":
					packer_cli()


