



import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class   _A   ( _lowercase  ,						_lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     List[Any]      							=      IFImgaImgSuperResolutionPipeline
						_snake_case			:     Union[str, Any]      							=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
						_snake_case			:     Any      							=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}	)
						_snake_case			:     Any      							=      PipelineTesterMixin.required_optional_params - {"""latents"""}



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							return self._get_superresolution_dummy_components()



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							List[Any]							,      lowerCamelCase	:							str=0	):






							'''simple docstring'''



							if str(lowerCamelCase	).startswith("mps"	):
								__lowercase		       =			torch.manual_seed(lowerCamelCase	)
							else:
								__lowercase		       =			torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)

							__lowercase		       =			floats_tensor((1, 3, 32, 32)							,      rng=random.Random(lowerCamelCase	)	).to(lowerCamelCase	)
							__lowercase		       =			floats_tensor((1, 3, 16, 16)							,      rng=random.Random(lowerCamelCase	)	).to(lowerCamelCase	)

							__lowercase		       =			{
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": image,
							    "original_image": original_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "numpy",
							}

							return inputs



						@unittest.skipIf(
						    torch_device != "cuda" or not is_xformers_available()							,      reason="XFormers attention is only available with CUDA and `xformers` installed"							,      )
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							self._test_save_load_optional_components()



						@unittest.skipIf(torch_device != "cuda"							,      reason="float16 requires CUDA"	)
						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							super().test_save_load_floataa(expected_max_diff=1e-1	)



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							self._test_save_load_local()



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2							,      )








def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	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."""
	)

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

	# Function Call
	calc_profit(profit, weight, max_weight)



