'''simple docstring'''


import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase	:       Optional[Any]						       =	logging.get_logger(__name__)  # pylint: disable=invalid-name





def       lowerCAmelCase					(		UpperCamelCase__				:   Union[List, PIL.Image.Image, torch.Tensor]       ):


	"""simple docstring"""
	warnings.warn(
	    '''The preprocess method is deprecated and will be removed in a future version. Please'''
	    ''' use VaeImageProcessor.preprocess instead'''    ,					UpperCamelCase__    ,					)
	if isinstance(UpperCamelCase__    ,					torch.Tensor       ):
		return image
	elif isinstance(UpperCamelCase__    ,					PIL.Image.Image       ):
		__UpperCAmelCase							=			[image]

	if isinstance(image[0]    ,					PIL.Image.Image       ):
		__UpperCAmelCase				,			__UpperCAmelCase							=			image[0].size
		__UpperCAmelCase				,			__UpperCAmelCase							=			(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

		__UpperCAmelCase							=			[np.array(i.resize((w, h)    ,					resample=PIL_INTERPOLATION['''lanczos''']       )       )[None, :] for i in image]
		__UpperCAmelCase							=			np.concatenate(UpperCamelCase__    ,					axis=0       )
		__UpperCAmelCase							=			np.array(UpperCamelCase__       ).astype(np.floataa       ) / 2_55.0
		__UpperCAmelCase							=			image.transpose(0    ,					3    ,					1    ,					2       )
		__UpperCAmelCase							=			2.0 * image - 1.0
		__UpperCAmelCase							=			torch.from_numpy(UpperCamelCase__       )
	elif isinstance(image[0]    ,					torch.Tensor       ):
		__UpperCAmelCase							=			torch.cat(UpperCamelCase__    ,					dim=0       )
	return image





def       lowerCAmelCase					(		UpperCamelCase__				:   Union[List, PIL.Image.Image, torch.Tensor]       ):


	"""simple docstring"""
	if isinstance(UpperCamelCase__    ,					torch.Tensor       ):
		return mask
	elif isinstance(UpperCamelCase__    ,					PIL.Image.Image       ):
		__UpperCAmelCase							=			[mask]

	if isinstance(mask[0]    ,					PIL.Image.Image       ):
		__UpperCAmelCase				,			__UpperCAmelCase							=			mask[0].size
		__UpperCAmelCase				,			__UpperCAmelCase							=			(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
		__UpperCAmelCase							=			[np.array(m.convert('''L'''       ).resize((w, h)    ,					resample=PIL_INTERPOLATION['''nearest''']       )       )[None, :] for m in mask]
		__UpperCAmelCase							=			np.concatenate(UpperCamelCase__    ,					axis=0       )
		__UpperCAmelCase							=			mask.astype(np.floataa       ) / 2_55.0
		__UpperCAmelCase							=			0
		__UpperCAmelCase							=			1
		__UpperCAmelCase							=			torch.from_numpy(UpperCamelCase__       )
	elif isinstance(mask[0]    ,					torch.Tensor       ):
		__UpperCAmelCase							=			torch.cat(UpperCamelCase__    ,					dim=0       )
	return mask






class 						A   (   UpperCAmelCase   ):
							a_        =      42
							a_        =      42





							def __init__(      self				:							Union[str, Any]     ,		__a				:							Optional[int]     ,		__a				:							Dict	)							->	int:
								super().__init__()
								self.register_modules(unet=__a     ,		scheduler=__a	)






							@torch.no_grad()
							def __call__(      self				:							Tuple     ,		__a				:							Union[torch.Tensor, PIL.Image.Image]     ,		__a				:							Union[torch.Tensor, PIL.Image.Image]     ,		__a				:							int = 2_5_0     ,		__a				:							float = 0.0     ,		__a				:							int = 1_0     ,		__a				:							int = 1_0     ,		__a				:							Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		__a				:							Optional[str] = "pil"     ,		__a				:							bool = True     ,		)							->	Union[ImagePipelineOutput, Tuple]:
								__UpperCAmelCase							=			image

								__UpperCAmelCase							=			_preprocess_image(__a	)
								__UpperCAmelCase							=			original_image.to(device=self.device     ,		dtype=self.unet.dtype	)
								__UpperCAmelCase							=			_preprocess_mask(__a	)
								__UpperCAmelCase							=			mask_image.to(device=self.device     ,		dtype=self.unet.dtype	)

								__UpperCAmelCase							=			original_image.shape[0]

								# sample gaussian noise to begin the loop
								if isinstance(__a     ,		__a	) and len(__a	) != batch_size:
									raise ValueError(
									    f"""You have passed a list of generators of length {len(__a	)}, but requested an effective batch"""
									    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""	)

								__UpperCAmelCase							=			original_image.shape
								__UpperCAmelCase							=			randn_tensor(__a     ,		generator=__a     ,		device=self.device     ,		dtype=self.unet.dtype	)

								# set step values
								self.scheduler.set_timesteps(__a     ,		__a     ,		__a     ,		self.device	)
								__UpperCAmelCase							=			eta

								__UpperCAmelCase							=			self.scheduler.timesteps[0] + 1
								__UpperCAmelCase							=			generator[0] if isinstance(__a     ,		__a	) else generator
								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
									if t < t_last:
										# predict the noise residual
										__UpperCAmelCase							=			self.unet(__a     ,		__a	).sample
										# compute previous image: x_t -> x_t-1
										__UpperCAmelCase							=			self.scheduler.step(__a     ,		__a     ,		__a     ,		__a     ,		__a     ,		__a	).prev_sample

									else:
										# compute the reverse: x_t-1 -> x_t
										__UpperCAmelCase							=			self.scheduler.undo_step(__a     ,		__a     ,		__a	)
									__UpperCAmelCase							=			t

								__UpperCAmelCase							=			(image / 2 + 0.5).clamp(0     ,		1	)
								__UpperCAmelCase							=			image.cpu().permute(0     ,		2     ,		3     ,		1	).numpy()
								if output_type == "pil":
									__UpperCAmelCase							=			self.numpy_to_pil(__a	)

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=__a	)

'''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()
