

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		(  _SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE   ,     unittest.TestCase):
      SCREAMING_SNAKE_CASE    :      Optional[int]       =  IFImgaImgSuperResolutionPipeline
      SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
      SCREAMING_SNAKE_CASE    :      List[str]       =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''})
      SCREAMING_SNAKE_CASE    :      Any       =  PipelineTesterMixin.required_optional_params - {"latents"}

      def  UpperCAmelCase    (				self					):





            """simple docstring"""







            return self._get_superresolution_dummy_components()

      def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0					):





            """simple docstring"""







            if str(A_					).startswith('mps'					):
                  SCREAMING_SNAKE_CASE_				:  Optional[int]           =     torch.manual_seed(A_					)
            else:
                  SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.Generator(device=A_					).manual_seed(A_					)

            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     floats_tensor((1, 3, 32, 32) ,					rng=random.Random(A_					)					).to(A_					)
            SCREAMING_SNAKE_CASE_				:  Any           =     floats_tensor((1, 3, 16, 16) ,					rng=random.Random(A_					)					).to(A_					)

            SCREAMING_SNAKE_CASE_				:  int           =     {
                '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  UpperCAmelCase    (				self					):





            """simple docstring"""







            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)

      def  UpperCAmelCase    (				self					):





            """simple docstring"""







            self._test_save_load_optional_components()

      @unittest.skipIf(torch_device != 'cuda' ,					reason='float16 requires CUDA'					)
      def  UpperCAmelCase    (				self					):





            """simple docstring"""







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

      def  UpperCAmelCase    (				self					):





            """simple docstring"""







            self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)

      def  UpperCAmelCase    (				self					):





            """simple docstring"""







            self._test_save_load_local()



      def  UpperCAmelCase    (				self					):





            """simple docstring"""







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









def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      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.
      SCREAMING_SNAKE_CASE_				:  Tuple           =     [p / w for p, w in zip(a					,   a  )]

      # Creating a copy of the list and sorting profit/weight in ascending order
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     sorted(a  )

      # declaring useful variables
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     len(a  )
      SCREAMING_SNAKE_CASE_				:  str           =     0
      SCREAMING_SNAKE_CASE_				:  List[str]           =     0
      SCREAMING_SNAKE_CASE_				:  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
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     sorted_profit_by_weight[length - i - 1]
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     profit_by_weight.index(a  )
            SCREAMING_SNAKE_CASE_				:  Dict           =     -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.'
    )

    lowerCAmelCase     :   Tuple						     =  [int(x) for x in input('Input profits separated by spaces: ').split()]
    lowerCAmelCase     :   Union[str, Any]						     =  [int(x) for x in input('Input weights separated by spaces: ').split()]
    lowerCAmelCase     :   Dict						     =  int(input('Max weight allowed: '))

    # Function Call
    calc_profit(profit, weight, max_weight)






