



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      _A				(UpperCamelCase		:      Dict )  ->Optional[Any]:



			'''simple docstring'''







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





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





			@torch.no_grad()
			def __call__(self							,    __magic_name__ = None							,    __magic_name__ = 1							,    __magic_name__ = 100							,    __magic_name__ = 0.0							,    __magic_name__ = None							,    __magic_name__ = "pil"							,    __magic_name__ = True							,    ):




						if isinstance(_a							,    PIL.Image.Image				):
									lowerCamelCase__ :      Any								=		1
						elif isinstance(_a							,    torch.Tensor				):
									lowerCamelCase__ :      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				):
									lowerCamelCase__ :      str								=		preprocess(_a				)

						lowerCamelCase__ :      Any								=		image.shape[-2:]

						# in_channels should be 6: 3 for latents, 3 for low resolution image
						lowerCamelCase__ :      Dict								=		(batch_size, self.unet.config.in_channels // 2, height, width)
						lowerCamelCase__ :      Optional[int]								=		next(self.unet.parameters()				).dtype

						lowerCamelCase__ :      int								=		randn_tensor(_a							,    generator=_a							,    device=self.device							,    dtype=_a				)

						lowerCamelCase__ :      List[Any]								=		image.to(device=self.device							,    dtype=_a				)

						# set timesteps and move to the correct device
						self.scheduler.set_timesteps(_a							,    device=self.device				)
						lowerCamelCase__ :      Optional[int]								=		self.scheduler.timesteps

						# scale the initial noise by the standard deviation required by the scheduler
						lowerCamelCase__ :      Optional[Any]								=		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]
						lowerCamelCase__ :      Union[str, Any]								=		"""eta""" in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
						lowerCamelCase__ :      List[Any]								=		{}
						if accepts_eta:
									lowerCamelCase__ :      Union[str, Any]								=		eta

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

						# decode the image latents with the VQVAE
						lowerCamelCase__ :      Any								=		self.vqvae.decode(_a				).sample
						lowerCamelCase__ :      Tuple								=		torch.clamp(_a							,    -1.0							,    1.0				)
						lowerCamelCase__ :      int								=		image / 2 + 0.5
						lowerCamelCase__ :      Any								=		image.cpu().permute(0							,    2							,    3							,    1				).numpy()

						if output_type == "pil":
									lowerCamelCase__ :      List[Any]								=		self.numpy_to_pil(_a				)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=_a				)








import itertools
import string
from collections.abc import Generator, Iterable






def      _A				(UpperCamelCase		:      Iterable[str]      ,			UpperCamelCase		:      int )  ->Generator[tuple[str, ...], None, None]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		iter(UpperCamelCase )
			while True:
						lowerCamelCase__ :      Union[str, Any]								=		tuple(itertools.islice(UpperCamelCase      ,			UpperCamelCase ) )
						if not chunk:
									return
						yield chunk






def      _A				(UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		"""""".join([c.upper() for c in dirty if c in string.ascii_letters] )
			lowerCamelCase__ :      Any								=		""""""

			if len(UpperCamelCase ) < 2:
						return dirty

			for i in range(len(UpperCamelCase ) - 1 ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(UpperCamelCase ) & 1:
						clean += "X"

			return clean






def      _A				(UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCamelCase__ :      List[Any]								=		[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(UpperCamelCase )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(UpperCamelCase )

			return table






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      int								=		prepare_input(UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      Any								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      int								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Any								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



