





'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import 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 GLPNImageProcessor


class 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=7		,      _UpperCAmelCase=3		,      _UpperCAmelCase=18		,      _UpperCAmelCase=30		,      _UpperCAmelCase=400		,      _UpperCAmelCase=True		,      _UpperCAmelCase=32		,      _UpperCAmelCase=True		,      ):

          '''simple docstring'''






          __A		:					str						=			parent
          __A		:					Dict						=			batch_size
          __A		:					str						=			num_channels
          __A		:					str						=			image_size
          __A		:					str						=			min_resolution
          __A		:					Tuple						=			max_resolution
          __A		:					List[str]						=			do_resize
          __A		:					Tuple						=			size_divisor
          __A		:					Union[str, Any]						=			do_rescale


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return {
              "do_resize": self.do_resize,
              "size_divisor": self.size_divisor,
              "do_rescale": self.do_rescale,
          }




@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE       (a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			GLPNImageProcessor if is_vision_available() else None


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			GLPNImageProcessingTester(self)


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return self.image_processor_tester.prepare_image_processor_dict()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(_UpperCAmelCase		,      'do_resize'))
          self.assertTrue(hasattr(_UpperCAmelCase		,      'size_divisor'))
          self.assertTrue(hasattr(_UpperCAmelCase		,      'resample'))
          self.assertTrue(hasattr(_UpperCAmelCase		,      'do_rescale'))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          __A		:					Optional[int]						=			image_processing(image_inputs[0]		,      return_tensors='pt').pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          __A		:					Tuple						=			prepare_image_inputs(self.image_processor_tester		,      equal_resolution=_UpperCAmelCase		,      numpify=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase		,      np.ndarray)

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          __A		:					int						=			image_processing(image_inputs[0]		,      return_tensors='pt').pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          __A		:					str						=			prepare_image_inputs(self.image_processor_tester		,      equal_resolution=_UpperCAmelCase		,      torchify=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase		,      torch.Tensor)

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          __A		:					Dict						=			image_processing(image_inputs[0]		,      return_tensors='pt').pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)






'''simple docstring'''




from __future__ import annotations






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	tuple[float, list[float]]:
     __A		:					int						=			list(range(len(__snake_case     )     )     )
     __A		:					Optional[Any]						=			[v / w for v, w in zip(__snake_case					,				__snake_case     )]
     index.sort(key=lambda __snake_case     : ratio[i]					,				reverse=__snake_case     )

     __A		:					float						=			0
     __A		:					list[float]						=			[0] * len(__snake_case     )
     for i in index:
          if weight[i] <= capacity:
               __A		:					Optional[int]						=			1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __A		:					List[Any]						=			capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()