



'''simple docstring'''





import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A = True					,  A = None					,  A = 32					,  A = True					,  A = 1 / 255					,  A = True					,  A = True					,  A = [0.48145466, 0.4578275, 0.40821073]					,  A = [0.26862954, 0.26130258, 0.27577711]					,  A = True					,  A=7					,  A=30					,  A=400					,  A=3					,  )       ->	Optional[int]:


      """simple docstring"""



      _a           =    parent
      _a           =    do_resize
      _a           =    size if size is not None else {'''shortest_edge''': 288}
      _a           =    size_divisor
      _a           =    do_rescale
      _a           =    rescale_factor
      _a           =    do_normalize
      _a           =    do_center_crop
      _a           =    image_mean
      _a           =    image_std
      _a           =    do_pad
      _a           =    batch_size
      _a           =    num_channels
      _a           =    min_resolution
      _a           =    max_resolution



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }



 def    a__    (self					,  A					,  A=False			)       ->	Optional[Any]:


      """simple docstring"""



      if not batched:
           _a           =    self.size['''shortest_edge''']
           _a           =    image_inputs[0]
           if isinstance(A					,  Image.Image			):
                _a    ,       _a           =    image.size
           else:
                _a    ,       _a           =    image.shape[1], image.shape[2]
           _a           =    size / min(A					,  A			)
           if h < w:
                _a    ,       _a           =    size, scale * w
           else:
                _a    ,       _a           =    scale * h, size

           _a           =    int((1_333 / 800) * size			)
           if max(A					,  A			) > max_size:
                _a           =    max_size / max(A					,  A			)
                _a           =    newh * scale
                _a           =    neww * scale

           _a    ,       _a           =    int(newh + 0.5			), int(neww + 0.5			)
           _a    ,       _a           =    (
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

      else:
           _a           =    []
           for image in image_inputs:
                _a    ,       _a           =    self.get_expected_values([image]			)
                expected_values.append((expected_height, expected_width)			)
           _a           =    max(A					,  key=lambda A			: item[0]			)[0]
           _a           =    max(A					,  key=lambda A			: item[1]			)[1]

      return expected_height, expected_width






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[Any]		=			BridgeTowerImageProcessor if is_vision_available() else None



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      _a           =    BridgeTowerImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)
      self.assertTrue(hasattr(A					,  '''size_divisor'''			)			)



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


      """simple docstring"""



      pass



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  Image.Image			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values

      _a    ,       _a           =    self.image_processor_tester.get_expected_values(A			)
      self.assertEqual(
          encoded_images.shape					,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values

      _a    ,       _a           =    self.image_processor_tester.get_expected_values(A					,  batched=A			)
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )					,  )



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



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

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values

      _a    ,       _a           =    self.image_processor_tester.get_expected_values(A			)
      self.assertEqual(
          encoded_images.shape					,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values

      _a    ,       _a           =    self.image_processor_tester.get_expected_values(A					,  batched=A			)
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )					,  )



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



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

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values

      _a    ,       _a           =    self.image_processor_tester.get_expected_values(A			)
      self.assertEqual(
          encoded_images.shape					,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values

      _a    ,       _a           =    self.image_processor_tester.get_expected_values(A					,  batched=A			)
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )					,  )








'''simple docstring'''





class __A     :



 '''simple docstring'''







 def __init__(self					,  A			)       ->	None:


      """simple docstring"""



      _a           =    len(A			)
      _a           =    [0] * len_array

      if len_array > 0:
           _a           =    array[0]

      for i in range(1					,  A			):
           _a           =    self.prefix_sum[i - 1] + array[i]



 def    a__    (self					,  A					,  A			)       ->	int:


      """simple docstring"""



      if start == 0:
           return self.prefix_sum[end]

      return self.prefix_sum[end] - self.prefix_sum[start - 1]



 def    a__    (self					,  A			)       ->	bool:


      """simple docstring"""



      _a           =    {0}
      for sum_item in self.prefix_sum:
           if sum_item - target_sum in sums:
                return True

           sums.add(A			)

      return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()



