


'''simple docstring'''





import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					Optional[Any]					   =    VQModel
					UpperCAmelCase__      :					Any					   =    '''sample'''
					@property
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Any=(32, 32)   )      ->					Any:
												a__    =  4
												a__    =  3

												a__    =  floats_tensor((batch_size, num_channels) + sizes   ).to(__snake_case   )

												return {"sample": image}
					@property
					def 				lowerCamelCase__(   self     :List[str]   )      ->					str:
												return (3, 32, 32)
					@property
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					List[Any]:
												return (3, 32, 32)
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Dict:
												a__    =  {
												    'block_out_channels': [32, 64],
												    'in_channels': 3,
												    'out_channels': 3,
												    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
												    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
												    'latent_channels': 3,
												}
												a__    =  self.dummy_input
												return init_dict, inputs_dict
					def 				lowerCamelCase__(   self     :Dict   )      ->					List[Any]:
												pass
					def 				lowerCamelCase__(   self     :List[str]   )      ->					List[str]:
												pass
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Dict:
												a__   ,      a__    =  VQModel.from_pretrained('fusing/vqgan-dummy'    ,output_loading_info=__snake_case   )
												self.assertIsNotNone(__snake_case   )
												self.assertEqual(len(loading_info['missing_keys']   )    ,0   )

												model.to(__snake_case   )
												a__    =  model(**self.dummy_input   )

												assert image is not None, "Make sure output is not None"



					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					int:
												a__    =  VQModel.from_pretrained('fusing/vqgan-dummy'   )
												model.to(__snake_case   ).eval()

												torch.manual_seed(0   )
												if torch.cuda.is_available():
																			torch.cuda.manual_seed_all(0   )

												a__    =  torch.randn(1    ,model.config.in_channels    ,model.config.sample_size    ,model.config.sample_size   )
												a__    =  image.to(__snake_case   )
												with torch.no_grad():
																			a__    =  model(__snake_case   ).sample

												a__    =  output[0, -1, -3:, -3:].flatten().cpu()
												# fmt: off
												a__    =  torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]   )
												# fmt: on
												self.assertTrue(torch.allclose(__snake_case    ,__snake_case    ,atol=1E-3   )   )








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)




