


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					(IPNDMScheduler,)
					__UpperCAmelCase							  =					(("""num_inference_steps""", 50),)






					def A   (					self			,							**snake_case_    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = {'num_train_timesteps': 1_0_0_0}
								config.update(**UpperCamelCase_    )
								return config






					def A   (					self			,							snake_case_=0			,							**snake_case_    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = dict(self.forward_default_kwargs    )
								__lowercase			       = kwargs.pop('''num_inference_steps'''			,							UpperCamelCase_    )
								__lowercase			       = self.dummy_sample
								__lowercase			       = 0.1 * sample
								__lowercase			       = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

								for scheduler_class in self.scheduler_classes:
											__lowercase			       = self.get_scheduler_config(**UpperCamelCase_    )
											__lowercase			       = scheduler_class(**UpperCamelCase_    )
											scheduler.set_timesteps(UpperCamelCase_    )
											# copy over dummy past residuals
											__lowercase			       = dummy_past_residuals[:]

											if time_step is None:
														__lowercase			       = scheduler.timesteps[len(scheduler.timesteps    ) // 2]

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(UpperCamelCase_    )
														__lowercase			       = scheduler_class.from_pretrained(UpperCamelCase_    )
														new_scheduler.set_timesteps(UpperCamelCase_    )
														# copy over dummy past residuals
														__lowercase			       = dummy_past_residuals[:]

											__lowercase			       = scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample
											__lowercase			       = new_scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample

											assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"

											__lowercase			       = scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample
											__lowercase			       = new_scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample

											assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






					def A   (					self    ) ->      str:







								'''simple docstring'''

								pass






					def A   (					self			,							snake_case_=0			,							**snake_case_    ) ->      int:







								'''simple docstring'''

								__lowercase			       = dict(self.forward_default_kwargs    )
								__lowercase			       = kwargs.pop('''num_inference_steps'''			,							UpperCamelCase_    )
								__lowercase			       = self.dummy_sample
								__lowercase			       = 0.1 * sample
								__lowercase			       = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

								for scheduler_class in self.scheduler_classes:
											__lowercase			       = self.get_scheduler_config()
											__lowercase			       = scheduler_class(**UpperCamelCase_    )
											scheduler.set_timesteps(UpperCamelCase_    )

											# copy over dummy past residuals (must be after setting timesteps)
											__lowercase			       = dummy_past_residuals[:]

											if time_step is None:
														__lowercase			       = scheduler.timesteps[len(scheduler.timesteps    ) // 2]

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(UpperCamelCase_    )
														__lowercase			       = scheduler_class.from_pretrained(UpperCamelCase_    )
														# copy over dummy past residuals
														new_scheduler.set_timesteps(UpperCamelCase_    )

														# copy over dummy past residual (must be after setting timesteps)
														__lowercase			       = dummy_past_residuals[:]

											__lowercase			       = scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample
											__lowercase			       = new_scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample

											assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"

											__lowercase			       = scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample
											__lowercase			       = new_scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample

											assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






					def A   (					self			,							**snake_case_    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = self.scheduler_classes[0]
								__lowercase			       = self.get_scheduler_config(**UpperCamelCase_    )
								__lowercase			       = scheduler_class(**UpperCamelCase_    )

								__lowercase			       = 1_0
								__lowercase			       = self.dummy_model()
								__lowercase			       = self.dummy_sample_deter
								scheduler.set_timesteps(UpperCamelCase_    )

								for i, t in enumerate(scheduler.timesteps    ):
											__lowercase			       = model(UpperCamelCase_			,							UpperCamelCase_    )
											__lowercase			       = scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_    ).prev_sample

								for i, t in enumerate(scheduler.timesteps    ):
											__lowercase			       = model(UpperCamelCase_			,							UpperCamelCase_    )
											__lowercase			       = scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_    ).prev_sample

								return sample






					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								__lowercase			       = dict(self.forward_default_kwargs    )

								__lowercase			       = kwargs.pop('''num_inference_steps'''			,							UpperCamelCase_    )

								for scheduler_class in self.scheduler_classes:
											__lowercase			       = self.get_scheduler_config()
											__lowercase			       = scheduler_class(**UpperCamelCase_    )

											__lowercase			       = self.dummy_sample
											__lowercase			       = 0.1 * sample

											if num_inference_steps is not None and hasattr(UpperCamelCase_			,							'''set_timesteps'''    ):
														scheduler.set_timesteps(UpperCamelCase_    )
											elif num_inference_steps is not None and not hasattr(UpperCamelCase_			,							'''set_timesteps'''    ):
														__lowercase			       = num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											__lowercase			       = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
											__lowercase			       = dummy_past_residuals[:]

											__lowercase			       = scheduler.timesteps[5]
											__lowercase			       = scheduler.timesteps[6]

											__lowercase			       = scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample
											__lowercase			       = scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample

											self.assertEqual(output_a.shape			,							sample.shape    )
											self.assertEqual(output_a.shape			,							output_a.shape    )

											__lowercase			       = scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample
											__lowercase			       = scheduler.step(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							**UpperCamelCase_    ).prev_sample

											self.assertEqual(output_a.shape			,							sample.shape    )
											self.assertEqual(output_a.shape			,							output_a.shape    )






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								for timesteps in [1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=UpperCamelCase_			,							time_step=UpperCamelCase_    )






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								for t, num_inference_steps in zip([1, 5, 1_0]			,							[1_0, 5_0, 1_0_0]    ):
											self.check_over_forward(num_inference_steps=UpperCamelCase_			,							time_step=UpperCamelCase_    )






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.full_loop()
								__lowercase			       = torch.mean(torch.abs(UpperCamelCase_    )    )

								assert abs(result_mean.item() - 2_5_4_0_5_2_9    ) < 1_0




"""simple docstring"""



def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    if len(_snake_case ) != len(_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.
    UpperCAmelCase__	:      Any     							=					[p / w for p, w in zip(_snake_case    ,_snake_case )]

    # Creating a copy of the list and sorting profit/weight in ascending order
    UpperCAmelCase__	:      Union[str, Any]     							=					sorted(_snake_case )

    # declaring useful variables
    UpperCAmelCase__	:      Tuple     							=					len(_snake_case )
    UpperCAmelCase__	:      List[Any]     							=					0
    UpperCAmelCase__	:      Union[str, Any]     							=					0
    UpperCAmelCase__	:      Any     							=					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
        UpperCAmelCase__	:      Tuple     							=					sorted_profit_by_weight[length - i - 1]
        UpperCAmelCase__	:      int     							=					profit_by_weight.index(_snake_case )
        UpperCAmelCase__	:      str     							=					-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.'
    )

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

    # Function Call
    calc_profit(profit, weight, max_weight)

