

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



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def __init__(				self					, A__					, A__					, A__					, ):
						super().__init__()
						self.register_modules(vqvae=A__					, unet=A__					, scheduler=A__    )






					@torch.no_grad()
					def __call__(				self					, A__ = None					, A__ = 1					, A__ = 100					, A__ = 0.0					, A__ = None					, A__ = "pil"					, A__ = True					, ):
						if isinstance(A__					, PIL.Image.Image    ):
							A__							:					Any       =	1
						elif isinstance(A__					, torch.Tensor    ):
							A__							:					Optional[int]       =	image.shape[0]
						else:
							raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(A__    )}"""    )

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

						A__     ,     A__							:					int       =	image.shape[-2:]

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

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

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

						# set timesteps and move to the correct device
						self.scheduler.set_timesteps(A__					, device=self.device    )
						A__							:					List[Any]       =	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__							:					Optional[int]       =	"""eta""" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
						A__							:					str       =	{}
						if accepts_eta:
							A__							:					Optional[int]       =	eta

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

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

						if output_type == "pil":
							A__							:					Tuple       =	self.numpy_to_pil(A__    )

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=A__    )






import os

try:
	from .build_directory_md import good_file_paths
except ImportError:
	from build_directory_md import good_file_paths  # type: ignore

A_ :				Dict						 =    list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

A_ :				Optional[Any]						 =    [file for file in filepaths if file != file.lower()]
if upper_files:
	print(f'''{len(upper_files)} files contain uppercase characters:''')
	print('\n'.join(upper_files) + '\n')

A_ :				Tuple						 =    [file for file in filepaths if ' ' in file]
if space_files:
	print(f'''{len(space_files)} files contain space characters:''')
	print('\n'.join(space_files) + '\n')

A_ :				Any						 =    [file for file in filepaths if '-' in file]
if hyphen_files:
	print(f'''{len(hyphen_files)} files contain hyphen characters:''')
	print('\n'.join(hyphen_files) + '\n')

A_ :				List[str]						 =    [file for file in filepaths if os.sep not in file]
if nodir_files:
	print(f'''{len(nodir_files)} files are not in a directory:''')
	print('\n'.join(nodir_files) + '\n')

A_ :				Any						 =    len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)



