



'''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__:   List[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__:   str								=			parser.parse_args()

						UpperCamelCase__:   Union[str, Any]								=			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 __future__ import annotations


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						str  )      ->   bool:
							UpperCAmelCase :       str 							=		get_failure_array(_lowerCAmelCase  )

							# 2) Step through text searching for pattern
							UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		0, 0  # index into text, pattern
							while i < len(_lowerCAmelCase  ):
														if pattern[j] == text[i]:
																					if j == (len(_lowerCAmelCase  ) - 1):
																												return True
																					j += 1

														# if this is a prefix in our pattern
														# just go back far enough to continue
														elif j > 0:
																					UpperCAmelCase :       Optional[Any] 							=		failure[j - 1]
																					continue
														i += 1
							return False


def  snake_case_     (					_lowerCAmelCase  :						str  )      ->   list[int]:
							UpperCAmelCase :       Optional[Any] 							=		[0]
							UpperCAmelCase :       str 							=		0
							UpperCAmelCase :       List[str] 							=		1
							while j < len(_lowerCAmelCase  ):
														if pattern[i] == pattern[j]:
																					i += 1
														elif i > 0:
																					UpperCAmelCase :       Union[str, Any] 							=		failure[i - 1]
																					continue
														j += 1
														failure.append(_lowerCAmelCase  )
							return failure


if __name__ == "__main__":
						# Test 1)
						UpperCamelCase__:   str								=			"abc1abc12"
						UpperCamelCase__:   str								=			"alskfjaldsabc1abc1abc12k23adsfabcabc"
						UpperCamelCase__:   Any								=			"alskfjaldsk23adsfabcabc"
						assert kmp(pattern, texta) and not kmp(pattern, texta)

						# Test 2)
						UpperCamelCase__:   Tuple								=			"ABABX"
						UpperCamelCase__:   Union[str, Any]								=			"ABABZABABYABABX"
						assert kmp(pattern, text)

						# Test 3)
						UpperCamelCase__:   Any								=			"AAAB"
						UpperCamelCase__:   str								=			"ABAAAAAB"
						assert kmp(pattern, text)

						# Test 4)
						UpperCamelCase__:   int								=			"abcdabcy"
						UpperCamelCase__:   Any								=			"abcxabcdabxabcdabcdabcy"
						assert kmp(pattern, text)

						# Test 5)
						UpperCamelCase__:   List[str]								=			"aabaabaaa"
						assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]





