'''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	:       str						       =	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	:       Tuple						       =	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)

'''simple docstring'''


import argparse
import os
import re

import packaging.version


__lowerCAmelCase	:       Optional[int]						       =	"examples/"
__lowerCAmelCase	:       Dict						       =	{
    "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","),
    "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__lowerCAmelCase	:       List[str]						       =	{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
__lowerCAmelCase	:       int						       =	"README.md"





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   Tuple       ):


	"""simple docstring"""
	with open(UpperCamelCase__    ,					'''r'''    ,					encoding='''utf-8'''    ,					newline='''\n'''       ) as f:
		__UpperCAmelCase							=			f.read()
	__UpperCAmelCase				,			__UpperCAmelCase							=			REPLACE_PATTERNS[pattern]
	__UpperCAmelCase							=			replace.replace('''VERSION'''    ,					UpperCamelCase__       )
	__UpperCAmelCase							=			re_pattern.sub(UpperCamelCase__    ,					UpperCamelCase__       )
	with open(UpperCamelCase__    ,					'''w'''    ,					encoding='''utf-8'''    ,					newline='''\n'''       ) as f:
		f.write(UpperCamelCase__       )





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[int]       ):


	"""simple docstring"""
	for folder, directories, fnames in os.walk(UpperCamelCase__       ):
		# Removing some of the folders with non-actively maintained examples from the walk
		if "research_projects" in directories:
			directories.remove('''research_projects'''       )
		if "legacy" in directories:
			directories.remove('''legacy'''       )
		for fname in fnames:
			if fname.endswith('''.py'''       ):
				update_version_in_file(os.path.join(UpperCamelCase__    ,					UpperCamelCase__       )    ,					UpperCamelCase__    ,					pattern='''examples'''       )





def       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Any=False       ):


	"""simple docstring"""
	for pattern, fname in REPLACE_FILES.items():
		update_version_in_file(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )
	if not patch:
		update_version_in_examples(UpperCamelCase__       )





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			'''🤗 Transformers currently provides the following architectures'''
	__UpperCAmelCase							=			'''1. Want to contribute a new model?'''
	with open(UpperCamelCase__    ,					'''r'''    ,					encoding='''utf-8'''    ,					newline='''\n'''       ) as f:
		__UpperCAmelCase							=			f.readlines()

	# Find the start of the list.
	__UpperCAmelCase							=			0
	while not lines[start_index].startswith(_start_prompt       ):
		start_index += 1
	start_index += 1

	__UpperCAmelCase							=			start_index
	# Update the lines in the model list.
	while not lines[index].startswith(_end_prompt       ):
		if lines[index].startswith('''1.'''       ):
			__UpperCAmelCase							=			lines[index].replace(
			    '''https://huggingface.co/docs/transformers/main/model_doc'''    ,					'''https://huggingface.co/docs/transformers/model_doc'''    ,					)
		index += 1

	with open(UpperCamelCase__    ,					'''w'''    ,					encoding='''utf-8'''    ,					newline='''\n'''       ) as f:
		f.writelines(UpperCamelCase__       )





def       lowerCAmelCase					(		):


	"""simple docstring"""
	with open(REPLACE_FILES['''init''']    ,					'''r'''       ) as f:
		__UpperCAmelCase							=			f.read()
	__UpperCAmelCase							=			REPLACE_PATTERNS['''init'''][0].search(UpperCamelCase__       ).groups()[0]
	return packaging.version.parse(UpperCamelCase__       )





def       lowerCAmelCase					(		UpperCamelCase__				:   Any=False       ):


	"""simple docstring"""
	__UpperCAmelCase							=			get_version()
	if patch and default_version.is_devrelease:
		raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''       )
	if default_version.is_devrelease:
		__UpperCAmelCase							=			default_version.base_version
	elif patch:
		__UpperCAmelCase							=			f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
	else:
		__UpperCAmelCase							=			f"""{default_version.major}.{default_version.minor + 1}.0"""

	# Now let's ask nicely if that's the right one.
	__UpperCAmelCase							=			input(f"""Which version are you releasing? [{default_version}]"""       )
	if len(UpperCamelCase__       ) == 0:
		__UpperCAmelCase							=			default_version

	print(f"""Updating version to {version}."""       )
	global_version_update(UpperCamelCase__    ,					patch=UpperCamelCase__       )
	if not patch:
		print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''       )
		clean_main_ref_in_model_list()





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			get_version()
	__UpperCAmelCase							=			f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
	__UpperCAmelCase							=			current_version.base_version

	# Check with the user we got that right.
	__UpperCAmelCase							=			input(f"""Which version are we developing now? [{dev_version}]"""       )
	if len(UpperCamelCase__       ) == 0:
		__UpperCAmelCase							=			dev_version

	print(f"""Updating version to {version}."""       )
	global_version_update(UpperCamelCase__       )
	print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''       )
	clean_main_ref_in_model_list()


if __name__ == "__main__":
					__lowerCAmelCase	:       Tuple						       =	argparse.ArgumentParser()
					parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
					parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
					__lowerCAmelCase	:       Tuple						       =	parser.parse_args()
					if not args.post_release:
										pre_release_work(patch=args.patch)
					elif args.patch:
										print("Nothing to do after a patch :-)")
					else:
										post_release_work()
