





"""simple docstring"""




import argparse
import os
import re


a						: List[Any]     	=			'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a						: List[str]     	=			re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
a						: str     	=			re.compile(R'''\s*\(\s*"(\S[^"]+)"''')






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Tuple    ,       _lowercase						:    bool = False					) ->Any:






	'''simple docstring'''






	with open(_lowercase    ,       "r"    ,       encoding="utf-8"					) as f:
		a      :		List[Any]	=				f.read()

	a      :		Dict	=				content.split("\n"					)
	a      :		List[str]	=				[]
	a      :		Any	=				0
	while line_idx < len(_lowercase					):
		if _re_intro_mapping.search(lines[line_idx]					) is not None:
			a      :		List[str]	=				len(re.search(R"^(\s*)\S"    ,       lines[line_idx]					).groups()[0]					) + 8
			# Start of a new mapping!
			while not lines[line_idx].startswith(" " * indent + "("					):
				new_lines.append(lines[line_idx]					)
				line_idx += 1

			a      :		int	=				[]
			while lines[line_idx].strip() != "]":
				# Blocks either fit in one line or not
				if lines[line_idx].strip() == "(":
					a      :		int	=				line_idx
					while not lines[line_idx].startswith(" " * indent + ")"					):
						line_idx += 1
					blocks.append("\n".join(lines[start_idx : line_idx + 1]					)					)
				else:
					blocks.append(lines[line_idx]					)
				line_idx += 1

			# Sort blocks by their identifiers
			a      :		str	=				sorted(_lowercase    ,       key=lambda _lowercase					: _re_identifier.search(_lowercase					).groups()[0]					)
			new_lines += blocks
		else:
			new_lines.append(lines[line_idx]					)
			line_idx += 1

	if overwrite:
		with open(_lowercase    ,       "w"    ,       encoding="utf-8"					) as f:
			f.write("\n".join(_lowercase					)					)
	elif "\n".join(_lowercase					) != content:
		return True






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    bool = False					) ->Any:






	'''simple docstring'''






	a      :		List[str]	=				[os.path.join(_lowercase    ,       _lowercase					) for f in os.listdir(_lowercase					) if f.endswith(".py"					)]
	a      :		Union[str, Any]	=				[sort_auto_mapping(_lowercase    ,       overwrite=_lowercase					) for fname in fnames]

	if not overwrite and any(_lowercase					):
		a      :		List[Any]	=				[f for f, d in zip(_lowercase    ,       _lowercase					) if d]
		raise ValueError(
		    F"""The following files have auto mappings that need sorting: {', '.join(_lowercase					)}. Run `make style` to fix"""
		    " this."					)


if __name__ == "__main__":
		a						: Any     	=			argparse.ArgumentParser()
		parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
		a						: str     	=			parser.parse_args()

		sort_all_auto_mappings(not args.check_only)









"""simple docstring"""




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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	a,       a      :		str	=				image.size
	a,       a      :		Tuple	=				(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
	a      :		Union[str, Any]	=				image.resize((w, h)    ,       resample=PIL_INTERPOLATION["lanczos"]					)
	a      :		int	=				np.array(_lowercase					).astype(np.floataa					) / 255.0
	a      :		List[str]	=				image[None].transpose(0    ,       3    ,       1    ,       2					)
	a      :		Dict	=				torch.from_numpy(_lowercase					)
	return 2.0 * image - 1.0







class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> str:
								super().__init__()
								self.register_modules(vqvae=lowerCAmelCase__      ,					unet=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 1      ,					lowerCAmelCase__ = 100      ,					lowerCAmelCase__ = 0.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[Tuple, ImagePipelineOutput]:
								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		int	=				1
								elif isinstance(lowerCAmelCase__      ,					torch.Tensor      ):
									a      :		str	=				image.shape[0]
								else:
									raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCAmelCase__      )}"""      )

								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		Tuple	=				preprocess(lowerCAmelCase__      )

								a,       a      :		Optional[Any]	=				image.shape[-2:]

								# in_channels should be 6: 3 for latents, 3 for low resolution image
								a      :		Tuple	=				(batch_size, self.unet.config.in_channels // 2, height, width)
								a      :		List[str]	=				next(self.unet.parameters()      ).dtype

								a      :		Any	=				randn_tensor(lowerCAmelCase__      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=lowerCAmelCase__      )

								a      :		Union[str, Any]	=				image.to(device=self.device      ,					dtype=lowerCAmelCase__      )

								# set timesteps and move to the correct device
								self.scheduler.set_timesteps(lowerCAmelCase__      ,					device=self.device      )
								a      :		int	=				self.scheduler.timesteps

								# scale the initial noise by the standard deviation required by the scheduler
								a      :		str	=				latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								a      :		str	=				"eta" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
								a      :		List[Any]	=				{}
								if accepts_eta:
									a      :		Any	=				eta

								for t in self.progress_bar(lowerCAmelCase__      ):
									# concat latents and low resolution image in the channel dimension.
									a      :		str	=				torch.cat([latents, image]      ,					dim=1      )
									a      :		int	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )
									# predict the noise residual
									a      :		Union[str, Any]	=				self.unet(lowerCAmelCase__      ,					lowerCAmelCase__      ).sample
									# compute the previous noisy sample x_t -> x_t-1
									a      :		Tuple	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      ).prev_sample

								# decode the image latents with the VQVAE
								a      :		List[Any]	=				self.vqvae.decode(lowerCAmelCase__      ).sample
								a      :		int	=				torch.clamp(lowerCAmelCase__      ,					-1.0      ,					1.0      )
								a      :		Optional[int]	=				image / 2 + 0.5
								a      :		Optional[int]	=				image.cpu().permute(0      ,					2      ,					3      ,					1      ).numpy()

								if output_type == "pil":
									a      :		Union[str, Any]	=				self.numpy_to_pil(lowerCAmelCase__      )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


