
def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	list		,   SCREAMING_SNAKE_CASE_	:	list		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     int:
						if len(SCREAMING_SNAKE_CASE_       ) != len(SCREAMING_SNAKE_CASE_       ):
												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.
						_lowercase					       =					[p / w for p, w in zip(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )]

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

						# declaring useful variables
						_lowercase					       =					len(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					0
						_lowercase					       =					0
						_lowercase					       =					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
												_lowercase					       =					sorted_profit_by_weight[length - i - 1]
												_lowercase					       =					profit_by_weight.index(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					-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.'''
		)

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

		# Function Call
		calc_profit(profit, weight, max_weight)

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

A		: Optional[Any]								=     '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	int=8       )  ->     Optional[int]:
						_lowercase					       =					height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						_lowercase					       =					width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor



class    a_ (			_a    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			):
									super().__init__()

									self.register_modules(
									    unet=__UpperCamelCase				,			scheduler=__UpperCamelCase				,			movq=__UpperCamelCase				,			)
									_lowercase					       =					2 ** (len(self.movq.config.block_out_channels  ) - 1)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									if latents is None:
															_lowercase					       =					randn_tensor(__UpperCamelCase				,			generator=__UpperCamelCase				,			device=__UpperCamelCase				,			dtype=__UpperCamelCase  )
									else:
															if latents.shape != shape:
																					raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
															_lowercase					       =					latents.to(__UpperCamelCase  )

									_lowercase					       =					latents * scheduler.init_noise_sigma
									return latents






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=0  ):
									if is_accelerate_available():
															from accelerate import cpu_offload
									else:
															raise ImportError("""Please install accelerate via `pip install accelerate`"""  )

									_lowercase					       =					torch.device(f"""cuda:{gpu_id}"""  )

									_lowercase					       =					[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(__UpperCamelCase				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=0  ):
									if is_accelerate_available() and is_accelerate_version(""">="""				,			"""0.17.0.dev0"""  ):
															from accelerate import cpu_offload_with_hook
									else:
															raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""  )

									_lowercase					       =					torch.device(f"""cuda:{gpu_id}"""  )

									if self.device.type != "cpu":
															self.to("""cpu"""				,			silence_dtype_warnings=__UpperCamelCase  )
															torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									_lowercase					       =					None
									for cpu_offloaded_model in [self.unet, self.movq]:
															_lowercase       ,						_lowercase					       =					cpu_offload_with_hook(__UpperCamelCase				,			__UpperCamelCase				,			prev_module_hook=__UpperCamelCase  )

									# We'll offload the last model manually.
									_lowercase					       =					hook






			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def    UpperCamelCase_				(      self  ):
									if not hasattr(self.unet				,			"""_hf_hook"""  ):
															return self.device
									for module in self.unet.modules():
															if (
															    hasattr(__UpperCamelCase				,			"""_hf_hook"""  )
															    and hasattr(module._hf_hook				,			"""execution_device"""  )
															    and module._hf_hook.execution_device is not None
															):
																					return torch.device(module._hf_hook.execution_device  )
									return self.device






			@torch.no_grad()
			@replace_example_docstring(__UpperCamelCase  )
			def __call__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase = 512				,			__UpperCamelCase = 512				,			__UpperCamelCase = 100				,			__UpperCamelCase = 4.0				,			__UpperCamelCase = 1				,			__UpperCamelCase = None				,			__UpperCamelCase = None				,			__UpperCamelCase = "pil"				,			__UpperCamelCase = True				,			):
									_lowercase					       =					self._execution_device

									_lowercase					       =					guidance_scale > 1.0

									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )
									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )
									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					torch.cat(__UpperCamelCase				,			dim=0  )

									_lowercase					       =					image_embeds.shape[0] * num_images_per_prompt

									if do_classifier_free_guidance:
															_lowercase					       =					image_embeds.repeat_interleave(__UpperCamelCase				,			dim=0  )
															_lowercase					       =					negative_image_embeds.repeat_interleave(__UpperCamelCase				,			dim=0  )
															_lowercase					       =					hint.repeat_interleave(__UpperCamelCase				,			dim=0  )

									_lowercase					       =					torch.cat([negative_image_embeds, image_embeds]				,			dim=0  ).to(dtype=self.unet.dtype				,			device=__UpperCamelCase  )
									_lowercase					       =					torch.cat([hint, hint]				,			dim=0  ).to(dtype=self.unet.dtype				,			device=__UpperCamelCase  )
									self.scheduler.set_timesteps(__UpperCamelCase				,			device=__UpperCamelCase  )
									_lowercase					       =					self.scheduler.timesteps

									_lowercase					       =					self.movq.config.latent_channels

									_lowercase       ,						_lowercase					       =					downscale_height_and_width(__UpperCamelCase				,			__UpperCamelCase				,			self.movq_scale_factor  )

									# create initial latent
									_lowercase					       =					self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)				,			image_embeds.dtype				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			self.scheduler				,			)

									for i, t in enumerate(self.progress_bar(__UpperCamelCase  )  ):
															# expand the latents if we are doing classifier free guidance
															_lowercase					       =					torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

															_lowercase					       =					{"""image_embeds""": image_embeds, """hint""": hint}
															_lowercase					       =					self.unet(
															    sample=__UpperCamelCase				,			timestep=__UpperCamelCase				,			encoder_hidden_states=__UpperCamelCase				,			added_cond_kwargs=__UpperCamelCase				,			return_dict=__UpperCamelCase				,			)[0]

															if do_classifier_free_guidance:
																					_lowercase       ,						_lowercase					       =					noise_pred.split(latents.shape[1]				,			dim=1  )
																					_lowercase       ,						_lowercase					       =					noise_pred.chunk(2  )
																					_lowercase       ,						_lowercase					       =					variance_pred.chunk(2  )
																					_lowercase					       =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																					_lowercase					       =					torch.cat([noise_pred, variance_pred_text]				,			dim=1  )

															if not (
															    hasattr(self.scheduler.config				,			"""variance_type"""  )
															    and self.scheduler.config.variance_type in ["learned", "learned_range"]
															):
																					_lowercase       ,						_lowercase					       =					noise_pred.split(latents.shape[1]				,			dim=1  )

															# compute the previous noisy sample x_t -> x_t-1
															_lowercase					       =					self.scheduler.step(
															    __UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			generator=__UpperCamelCase				,			)[0]
									# post-processing
									_lowercase					       =					self.movq.decode(__UpperCamelCase				,			force_not_quantize=__UpperCamelCase  )["""sample"""]

									if output_type not in ["pt", "np", "pil"]:
															raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""  )

									if output_type in ["np", "pil"]:
															_lowercase					       =					image * 0.5 + 0.5
															_lowercase					       =					image.clamp(0				,			1  )
															_lowercase					       =					image.cpu().permute(0				,			2				,			3				,			1  ).float().numpy()

									if output_type == "pil":
															_lowercase					       =					self.numpy_to_pil(__UpperCamelCase  )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=__UpperCamelCase  )