



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     :							List[str]  							=				logging.get_logger(__name__)  # pylint: disable=invalid-name

def    lowerCamelCase_(lowerCamelCase_    ) ->       Optional[int]:
						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    ) / 255.0
												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_(lowerCamelCase_    ) ->       Dict:





						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 % 32 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    ) / 255.0
												UpperCAmelCase         =     0
												UpperCAmelCase         =     1
												UpperCAmelCase         =     torch.from_numpy(UpperCamelCase__    )
						elif isinstance(mask[0]    ,   torch.Tensor    ):
												UpperCAmelCase         =     torch.cat(UpperCamelCase__    ,   dim=0    )
						return mask





class  __magic_name__   (							UpperCAmelCase_						):
		lowercase      :       int          =42
		lowercase      :       List[Any]          =42






		def __init__(						self      :     Optional[int]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Tuple      )				->					Union[str, Any]:
								'''simple docstring'''







								super().__init__()
								self.register_modules(unet=_lowercase				,		scheduler=_lowercase      )







		@torch.no_grad()
		def __call__(						self      :     Any				,		UpperCamelCase__      :     Union[torch.Tensor, PIL.Image.Image]				,		UpperCamelCase__      :     Union[torch.Tensor, PIL.Image.Image]				,		UpperCamelCase__      :     int = 2_50				,		UpperCamelCase__      :     float = 0.0				,		UpperCamelCase__      :     int = 10				,		UpperCamelCase__      :     int = 10				,		UpperCamelCase__      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,		UpperCamelCase__      :     Optional[str] = "pil"				,		UpperCamelCase__      :     bool = True				,		)				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     image

								UpperCAmelCase         =     _preprocess_image(_lowercase      )
								UpperCAmelCase         =     original_image.to(device=self.device				,		dtype=self.unet.dtype      )
								UpperCAmelCase         =     _preprocess_mask(_lowercase      )
								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(_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.'      )

								UpperCAmelCase         =     original_image.shape
								UpperCAmelCase         =     randn_tensor(_lowercase				,		generator=_lowercase				,		device=self.device				,		dtype=self.unet.dtype      )

								# set step values
								self.scheduler.set_timesteps(_lowercase				,		_lowercase				,		_lowercase				,		self.device      )
								UpperCAmelCase         =     eta

								UpperCAmelCase         =     self.scheduler.timesteps[0] + 1
								UpperCAmelCase         =     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
																				UpperCAmelCase         =     self.unet(_lowercase				,		_lowercase      ).sample
																				# compute previous image: x_t -> x_t-1
																				UpperCAmelCase         =     self.scheduler.step(_lowercase				,		_lowercase				,		_lowercase				,		_lowercase				,		_lowercase				,		_lowercase      ).prev_sample

														else:
																				# compute the reverse: x_t-1 -> x_t
																				UpperCAmelCase         =     self.scheduler.undo_step(_lowercase				,		_lowercase				,		_lowercase      )
														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(_lowercase      )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=_lowercase      )








def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       list[int]:
						UpperCAmelCase         =     int(lowerCamelCase_    )

						# Initialize Result
						UpperCAmelCase         =     []

						# Traverse through all denomination
						for denomination in reversed(lowerCamelCase_    ):
												# Find denominations
												while int(lowerCamelCase_    ) >= int(lowerCamelCase_    ):
																		total_value -= int(lowerCamelCase_    )
																		answer.append(lowerCamelCase_    )  # Append the "answers" array

						return answer


# Driver Code
if __name__ == "__main__":
		__lowerCamelCase     :							Optional[int]  							=				[]
		__lowerCamelCase     :							Dict  							=				"0"

		if (
		    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
		    == "y"
		):
				__lowerCamelCase     :							List[Any]  							=				int(input("Enter the number of denominations you want to add: ").strip())

				for i in range(0, n):
						denominations.append(int(input(F'''Denomination {i}: ''').strip()))
				__lowerCamelCase     :							str  							=				input("Enter the change you want to make in Indian Currency: ").strip()
		else:
				# All denominations of Indian Currency if user does not enter
				__lowerCamelCase     :							Optional[int]  							=				[1, 2, 5, 10, 20, 50, 100, 500, 2_000]
				__lowerCamelCase     :							Optional[int]  							=				input("Enter the change you want to make: ").strip()

		if int(value) == 0 or int(value) < 0:
				print("The total value cannot be zero or negative.")

		else:
				print(F'''Following is minimal change for {value}: ''')
				__lowerCamelCase     :							Union[str, Any]  							=				find_minimum_change(denominations, value)
				# Print result
				for i in range(len(answer)):
						print(answer[i], end=" ")



