


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


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






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

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

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






def 							__lowercase     (				__lowerCAmelCase							:       Union[List, PIL.Image.Image, torch.Tensor]     ):
							if isinstance(__lowerCAmelCase      ,					torch.Tensor     ):
														return mask
							elif isinstance(__lowerCAmelCase      ,					PIL.Image.Image     ):
														a__    =  [mask]

							if isinstance(mask[0]      ,					PIL.Image.Image     ):
														a__   ,      a__    =  mask[0].size
														a__   ,      a__    =  (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
														a__    =  [np.array(m.convert('L'     ).resize((w, h)      ,					resample=PIL_INTERPOLATION['nearest']     )     )[None, :] for m in mask]
														a__    =  np.concatenate(__lowerCAmelCase      ,					axis=0     )
														a__    =  mask.astype(np.floataa     ) / 255.0
														a__    =  0
														a__    =  1
														a__    =  torch.from_numpy(__lowerCAmelCase     )
							elif isinstance(mask[0]      ,					torch.Tensor     ):
														a__    =  torch.cat(__lowerCAmelCase      ,					dim=0     )
							return mask







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					UNetaDModel
					UpperCAmelCase__      :					RePaintScheduler
					def __init__(   self     :Tuple    ,__snake_case     :Dict    ,__snake_case     :Any   )      ->					Optional[Any]:
												super().__init__()
												self.register_modules(unet=__snake_case    ,scheduler=__snake_case   )



					@torch.no_grad()
					def __call__(   self     :List[str]    ,__snake_case     :Union[torch.Tensor, PIL.Image.Image]    ,__snake_case     :Union[torch.Tensor, PIL.Image.Image]    ,__snake_case     :int = 2_50    ,__snake_case     :float = 0.0    ,__snake_case     :int = 10    ,__snake_case     :int = 10    ,__snake_case     :Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,__snake_case     :Optional[str] = "pil"    ,__snake_case     :bool = True    ,)      ->					Union[ImagePipelineOutput, Tuple]:
												a__    =  image

												a__    =  _preprocess_image(__snake_case   )
												a__    =  original_image.to(device=self.device    ,dtype=self.unet.dtype   )
												a__    =  _preprocess_mask(__snake_case   )
												a__    =  mask_image.to(device=self.device    ,dtype=self.unet.dtype   )

												a__    =  original_image.shape[0]

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

												a__    =  original_image.shape
												a__    =  randn_tensor(__snake_case    ,generator=__snake_case    ,device=self.device    ,dtype=self.unet.dtype   )

												# set step values
												self.scheduler.set_timesteps(__snake_case    ,__snake_case    ,__snake_case    ,self.device   )
												a__    =  eta

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

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

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

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__snake_case   )








def 							__lowercase     (				__lowerCAmelCase							:       list      ,					__lowerCAmelCase							:       list      ,					__lowerCAmelCase							:       int     ):
							if len(__lowerCAmelCase     ) != len(__lowerCAmelCase     ):
														raise ValueError('The length of profit and weight must be same.'     )
							if max_weight <= 0:
														raise ValueError('max_weight must greater than zero.'     )
							if any(p < 0 for p in profit     ):
														raise ValueError('Profit can not be negative.'     )
							if any(w < 0 for w in weight     ):
														raise ValueError('Weight can not be negative.'     )

							# List created to store profit gained for the 1kg in case of each weight
							# respectively.  Calculate and append profit/weight for each element.
							a__    =  [p / w for p, w in zip(__lowerCAmelCase      ,					__lowerCAmelCase     )]

							# Creating a copy of the list and sorting profit/weight in ascending order
							a__    =  sorted(__lowerCAmelCase     )

							# declaring useful variables
							a__    =  len(__lowerCAmelCase     )
							a__    =  0
							a__    =  0
							a__    =  0

							# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
							while limit <= max_weight and i < length:
														# flag value for encountered greatest element in sorted_profit_by_weight
														a__    =  sorted_profit_by_weight[length - i - 1]
														a__    =  profit_by_weight.index(__lowerCAmelCase     )
														a__    =  -1

														# check if the weight encountered is less than the total weight
														# encountered before.
														if max_weight - limit >= weight[index]:
																					limit += weight[index]
																					# Adding profit gained for the given weight 1 ===
																					# weight[index]/weight[index]
																					gain += 1 * profit[index]
														else:
																					# Since the weight encountered is greater than limit, therefore take the
																					# required number of remaining kgs and calculate profit for it.
																					# weight remaining / weight[index]
																					gain += (max_weight - limit) / weight[index] * profit[index]
																					break
														i += 1
							return gain


if __name__ == "__main__":
						print(
						    '''Input profits, weights, and then max_weight (all positive ints) separated by '''
						    '''spaces.'''
						)

						snake_case							: Tuple      =   [int(x) for x in input('''Input profits separated by spaces: ''').split()]
						snake_case							: Optional[int]      =   [int(x) for x in input('''Input weights separated by spaces: ''').split()]
						snake_case							: List[str]      =   int(input('''Max weight allowed: '''))

						# Function Call
						calc_profit(profit, weight, max_weight)




