

import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor
class  _A		(  unittest.TestCase):

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=7 ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=18 ,					_SCREAMING_SNAKE_CASE=30 ,					_SCREAMING_SNAKE_CASE=400 ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,					_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     parent
        SCREAMING_SNAKE_CASE_				:  int           =     batch_size
        SCREAMING_SNAKE_CASE_				:  Tuple           =     num_channels
        SCREAMING_SNAKE_CASE_				:  List[str]           =     image_size
        SCREAMING_SNAKE_CASE_				:  Dict           =     min_resolution
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     max_resolution
        SCREAMING_SNAKE_CASE_				:  str           =     do_resize
        SCREAMING_SNAKE_CASE_				:  Dict           =     size if size is not None else {'height': 18, 'width': 20}
        SCREAMING_SNAKE_CASE_				:  Dict           =     do_thumbnail
        SCREAMING_SNAKE_CASE_				:  str           =     do_align_axis
        SCREAMING_SNAKE_CASE_				:  Dict           =     do_pad
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     do_normalize
        SCREAMING_SNAKE_CASE_				:  str           =     image_mean
        SCREAMING_SNAKE_CASE_				:  Any           =     image_std



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_thumbnail": self.do_thumbnail,
            "do_align_long_axis": self.do_align_axis,
            "do_pad": self.do_pad,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }

@require_torch
@require_vision
class  _A		(  __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      List[str]       =  DonutImageProcessor if is_vision_available() else None

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     DonutImageProcessingTester(self					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.image_processor_tester.prepare_image_processor_dict()

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'do_resize'					)					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'size'					)					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'do_thumbnail'					)					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'do_align_long_axis'					)					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'do_pad'					)					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'do_normalize'					)					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'image_mean'					)					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'image_std'					)					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.image_processing_class.from_dict(self.image_processor_dict					)
        self.assertEqual(image_processor.size ,					{'height': 18, 'width': 20}					)

        SCREAMING_SNAKE_CASE_				:  str           =     self.image_processing_class.from_dict(self.image_processor_dict ,					size=42					)
        self.assertEqual(image_processor.size ,					{'height': 42, 'width': 42}					)

        # Previous config had dimensions in (width, height) order
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.image_processing_class.from_dict(self.image_processor_dict ,					size=(42, 84)					)
        self.assertEqual(image_processor.size ,					{'height': 84, 'width': 42}					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  @is_flaky()
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.image_processing_class(**self.image_processor_dict					)
        # create random PIL images
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     prepare_image_inputs(self.image_processor_tester ,					equal_resolution=_SCREAMING_SNAKE_CASE					)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE ,					Image.Image					)

        # Test not batched input
        SCREAMING_SNAKE_CASE_				:  Tuple           =     image_processing(image_inputs[0] ,					return_tensors='pt'					).pixel_values
        self.assertEqual(
            encoded_images.shape ,					(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,					)

        # Test batched
        SCREAMING_SNAKE_CASE_				:  Any           =     image_processing(_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					).pixel_values
        self.assertEqual(
            encoded_images.shape ,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,					)

  @is_flaky()
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        SCREAMING_SNAKE_CASE_				:  Tuple           =     prepare_image_inputs(self.image_processor_tester ,					equal_resolution=_SCREAMING_SNAKE_CASE ,					numpify=_SCREAMING_SNAKE_CASE					)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE ,					np.ndarray					)

        # Test not batched input
        SCREAMING_SNAKE_CASE_				:  str           =     image_processing(image_inputs[0] ,					return_tensors='pt'					).pixel_values
        self.assertEqual(
            encoded_images.shape ,					(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,					)

        # Test batched
        SCREAMING_SNAKE_CASE_				:  Any           =     image_processing(_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					).pixel_values
        self.assertEqual(
            encoded_images.shape ,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,					)



  @is_flaky()
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE_				:  List[str]           =     prepare_image_inputs(self.image_processor_tester ,					equal_resolution=_SCREAMING_SNAKE_CASE ,					torchify=_SCREAMING_SNAKE_CASE					)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE ,					torch.Tensor					)

        # Test not batched input
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     image_processing(image_inputs[0] ,					return_tensors='pt'					).pixel_values
        self.assertEqual(
            encoded_images.shape ,					(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,					)

        # Test batched
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     image_processing(_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					).pixel_values
        self.assertEqual(
            encoded_images.shape ,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,					)









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)






