

"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   __lowercase      (     __lowerCamelCase       ):

  def __init__(	self		:      Optional[Any]       ,A		:      str       ,A		:      Tuple     ):






   '''simple docstring'''
   super().__init__()

   # make sure scheduler can always be converted to DDIM
   UpperCAmelCase__       :	Any        =       DDIMScheduler.from_config(scheduler.config     )

   self.register_modules(unet=A       ,scheduler=A     )




  @torch.no_grad()
  def __call__(	self		:      List[Any]       ,A		:      int = 1       ,A		:      Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,A		:      float = 0.0       ,A		:      int = 50       ,A		:      Optional[bool] = None       ,A		:      Optional[str] = "pil"       ,A		:      bool = True       ,):






   '''simple docstring'''

   # Sample gaussian noise to begin loop
   if isinstance(self.unet.config.sample_size       ,A     ):
    UpperCAmelCase__       :	Optional[int]        =       (
        batch_size,
        self.unet.config.in_channels,
        self.unet.config.sample_size,
        self.unet.config.sample_size,
    )
   else:
    UpperCAmelCase__       :	str        =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

   if isinstance(A       ,A     ) and len(A     ) != batch_size:
    raise ValueError(
        f"You have passed a list of generators of length {len(A     )}, but requested an effective batch"
        f" size of {batch_size}. Make sure the batch size matches the length of the generators."     )

   UpperCAmelCase__       :	Optional[Any]        =       randn_tensor(A       ,generator=A       ,device=self.device       ,dtype=self.unet.dtype     )

   # set step values
   self.scheduler.set_timesteps(A     )

   for t in self.progress_bar(self.scheduler.timesteps     ):
    # 1. predict noise model_output
    UpperCAmelCase__       :	Tuple        =       self.unet(A       ,A     ).sample

    # 2. predict previous mean of image x_t-1 and add variance depending on eta
    # eta corresponds to η in paper and should be between [0, 1]
    # do x_t -> x_t-1
    UpperCAmelCase__       :	Tuple        =       self.scheduler.step(
        A       ,A       ,A       ,eta=A       ,use_clipped_model_output=A       ,generator=A     ).prev_sample

   UpperCAmelCase__       :	Optional[Any]        =       (image / 2 + 0.5).clamp(0       ,1     )
   UpperCAmelCase__       :	Optional[int]        =       image.cpu().permute(0       ,2       ,3       ,1     ).numpy()
   if output_type == "pil":
    UpperCAmelCase__       :	List[str]        =       self.numpy_to_pil(A     )

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=A     )







'''simple docstring'''
def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	if len(A__						) != len(A__						):
		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(A__  ,      A__						)]

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

	# declaring useful variables
	a_      				=						len(A__						)
	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(A__						)
		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.'
		)

		lowercase__											=[int(x) for x in input('Input profits separated by spaces: ').split()]
		lowercase__											=[int(x) for x in input('Input weights separated by spaces: ').split()]
		lowercase__											=int(input('Max weight allowed: '))

		# Function Call
		calc_profit(profit, weight, max_weight)
