



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


lowercase_						=       logging.get_logger(__name__)  # pylint: disable=invalid-name



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

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

						lowercase__   					=			[np.array(i.resize((w, h)  ,					resample=PIL_INTERPOLATION["lanczos"]					)					)[None, :] for i in image]
						lowercase__   					=			np.concatenate(SCREAMING_SNAKE_CASE_  ,					axis=0					)
						lowercase__   					=			np.array(SCREAMING_SNAKE_CASE_					).astype(np.floataa					) / 255.0
						lowercase__   					=			image.transpose(0  ,					3  ,					1  ,					2					)
						lowercase__   					=			2.0 * image - 1.0
						lowercase__   					=			torch.from_numpy(SCREAMING_SNAKE_CASE_					)
			elif isinstance(image[0]  ,					torch.Tensor					):
						lowercase__   					=			torch.cat(SCREAMING_SNAKE_CASE_  ,					dim=0					)
			return image



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if isinstance(SCREAMING_SNAKE_CASE_  ,					torch.Tensor					):
						return mask
			elif isinstance(SCREAMING_SNAKE_CASE_  ,					PIL.Image.Image					):
						lowercase__   					=			[mask]

			if isinstance(mask[0]  ,					PIL.Image.Image					):
						lowercase__					,    lowercase__   					=			mask[0].size
						lowercase__					,    lowercase__   					=			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						lowercase__   					=			[np.array(m.convert("L"					).resize((w, h)  ,					resample=PIL_INTERPOLATION["nearest"]					)					)[None, :] for m in mask]
						lowercase__   					=			np.concatenate(SCREAMING_SNAKE_CASE_  ,					axis=0					)
						lowercase__   					=			mask.astype(np.floataa					) / 255.0
						lowercase__   					=			0
						lowercase__   					=			1
						lowercase__   					=			torch.from_numpy(SCREAMING_SNAKE_CASE_					)
			elif isinstance(mask[0]  ,					torch.Tensor					):
						lowercase__   					=			torch.cat(SCREAMING_SNAKE_CASE_  ,					dim=0					)
			return mask




class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   UNetaDModel
				UpperCamelCase__			:   RePaintScheduler




				def __init__(  self   :  Tuple,   __lowercase   :  Any,   __lowercase   :  Tuple  ):
							super().__init__()
							self.register_modules(unet=__lowercase,   scheduler=__lowercase  )




				@torch.no_grad()
				def __call__(  self   :  Union[str, Any],   __lowercase   :  Union[torch.Tensor, PIL.Image.Image],   __lowercase   :  Union[torch.Tensor, PIL.Image.Image],   __lowercase   :  int = 250,   __lowercase   :  float = 0.0,   __lowercase   :  int = 10,   __lowercase   :  int = 10,   __lowercase   :  Optional[Union[torch.Generator, List[torch.Generator]]] = None,   __lowercase   :  Optional[str] = "pil",   __lowercase   :  bool = True,   ):
							lowercase__   					=			image

							lowercase__   					=			_preprocess_image(__lowercase  )
							lowercase__   					=			original_image.to(device=self.device,   dtype=self.unet.dtype  )
							lowercase__   					=			_preprocess_mask(__lowercase  )
							lowercase__   					=			mask_image.to(device=self.device,   dtype=self.unet.dtype  )

							lowercase__   					=			original_image.shape[0]

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

							lowercase__   					=			original_image.shape
							lowercase__   					=			randn_tensor(__lowercase,   generator=__lowercase,   device=self.device,   dtype=self.unet.dtype  )

							# set step values
							self.scheduler.set_timesteps(__lowercase,   __lowercase,   __lowercase,   self.device  )
							lowercase__   					=			eta

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

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

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

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=__lowercase  )









from __future__ import annotations

from typing import TypedDict
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   str
				UpperCamelCase__			:   int



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)

			return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE_					)					)]



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)
			if not s:
						raise ValueError("The parameter s must not be empty."					)

			lowercase__   					=			all_rotations(SCREAMING_SNAKE_CASE_					)
			rotations.sort()  # sort the list of rotations in alphabetically order
			# make a string composed of the last char of each rotation
			lowercase__   					=			{
			    "bwt_string": "".join([word[-1] for word in rotations]					),
			    "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE_					),
			}
			return response



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter bwt_string type must be str."					)
			if not bwt_string:
						raise ValueError("The parameter bwt_string must not be empty."					)
			try:
						lowercase__   					=			int(SCREAMING_SNAKE_CASE_					)
			except ValueError:
						raise TypeError(
						    "The parameter idx_original_string type must be int or passive"
						    " of cast to int."					)
			if idx_original_string < 0:
						raise ValueError("The parameter idx_original_string must not be lower than 0."					)
			if idx_original_string >= len(SCREAMING_SNAKE_CASE_					):
						raise ValueError(
						    "The parameter idx_original_string must be lower than" " len(bwt_string)."					)

			lowercase__   					=			[""] * len(SCREAMING_SNAKE_CASE_					)
			for _ in range(len(SCREAMING_SNAKE_CASE_					)					):
						for i in range(len(SCREAMING_SNAKE_CASE_					)					):
									lowercase__   					=			bwt_string[i] + ordered_rotations[i]
						ordered_rotations.sort()
			return ordered_rotations[idx_original_string]


if __name__ == "__main__":
							lowercase_						=       """Provide a string that I will generate its BWT transform: """
							lowercase_						=       input(entry_msg).strip()
							lowercase_						=       bwt_transform(s)
							print(
							    F'Burrows Wheeler transform for string \'{s}\' results '
							    F'in \'{result["bwt_string"]}\''
							)
							lowercase_						=       reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
							print(
							    F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
							    F'we get original string \'{original_string}\''
							)




