



"""simple docstring"""







def     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     )						->    Optional[Any]:
      __SCREAMING_SNAKE_CASE       		=   0
      __SCREAMING_SNAKE_CASE       		=   len(_snake_case     ) - 1

      while left <= right:
            # avoid divided by 0 during interpolation
            if sorted_collection[left] == sorted_collection[right]:
                  if sorted_collection[left] == item:
                        return left
                  else:
                        return None

            __SCREAMING_SNAKE_CASE       		=   left + ((item - sorted_collection[left]) * (right - left)) // (
                sorted_collection[right] - sorted_collection[left]
            )

            # out of range check
            if point < 0 or point >= len(_snake_case     ):
                  return None

            __SCREAMING_SNAKE_CASE       		=   sorted_collection[point]
            if current_item == item:
                  return point
            else:
                  if point < left:
                        __SCREAMING_SNAKE_CASE       		=   left
                        __SCREAMING_SNAKE_CASE       		=   point
                  elif point > right:
                        __SCREAMING_SNAKE_CASE       		=   right
                        __SCREAMING_SNAKE_CASE       		=   point
                  else:
                        if item < current_item:
                              __SCREAMING_SNAKE_CASE       		=   point - 1
                        else:
                              __SCREAMING_SNAKE_CASE       		=   point + 1
      return None






def     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     ,	UpperCAmelCase__     ,	UpperCAmelCase__     )						->    Dict:

      # avoid divided by 0 during interpolation
      if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                  return left
            else:
                  return None

      __SCREAMING_SNAKE_CASE       		=   left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(_snake_case     ):
            return None

      if sorted_collection[point] == item:
            return point
      elif point < left:
            return interpolation_search_by_recursion(_snake_case     ,	_snake_case     ,	_snake_case     ,	_snake_case     )
      elif point > right:
            return interpolation_search_by_recursion(_snake_case     ,	_snake_case     ,	_snake_case     ,	_snake_case     )
      else:
            if sorted_collection[point] > item:
                  return interpolation_search_by_recursion(
                      _snake_case     ,	_snake_case     ,	_snake_case     ,	point - 1     )
            else:
                  return interpolation_search_by_recursion(
                      _snake_case     ,	_snake_case     ,	point + 1     ,	_snake_case     )






def     _a (	UpperCAmelCase__     )						->    Any:
      if collection != sorted(_snake_case     ):
            raise ValueError('''Collection must be ascending sorted'''     )
      return True


if __name__ == "__main__":
       import sys
       lowerCAmelCase__									=0
       if debug == 1:
              lowerCAmelCase__									=[10, 30, 40, 45, 50, 66, 77, 93]
              try:
                     __assert_sorted(collection)
              except ValueError:
                     sys.exit("Sequence must be ascending sorted to apply interpolation search")
              lowerCAmelCase__									=67

       lowerCAmelCase__									=interpolation_search(collection, target)
       if result is not None:
              print(F'''{target} found at positions: {result}''')
       else:
              print("Not found")








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


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ChineseCLIPImageProcessor






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def __init__(				self	:Any,      snake_case	:Optional[int],      snake_case	:Optional[Any]=7,      snake_case	:str=3,      snake_case	:Optional[int]=18,      snake_case	:str=30,      snake_case	:List[Any]=400,      snake_case	:Any=True,      snake_case	:Dict=None,      snake_case	:Any=True,      snake_case	:Dict=None,      snake_case	:List[Any]=True,      snake_case	:Optional[int]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],      snake_case	:Any=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],      snake_case	:int=True,      ):



          """simple docstring"""

          _lowercase              =size if size is not None else {'height': 224, 'width': 224}
          _lowercase              =crop_size if crop_size is not None else {'height': 18, 'width': 18}
          _lowercase              =parent
          _lowercase              =batch_size
          _lowercase              =num_channels
          _lowercase              =image_size
          _lowercase              =min_resolution
          _lowercase              =max_resolution
          _lowercase              =do_resize
          _lowercase              =size
          _lowercase              =do_center_crop
          _lowercase              =crop_size
          _lowercase              =do_normalize
          _lowercase              =image_mean
          _lowercase              =image_std
          _lowercase              =do_convert_rgb





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_convert_rgb": self.do_convert_rgb,
          }





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:List[Any]=False,      snake_case	:Any=False,      snake_case	:Union[str, Any]=False):



          """simple docstring"""

          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

          if equal_resolution:
             _lowercase              =[]
             for i in range(self.batch_size):
                image_inputs.append(
                    np.random.randint(
                        255,      size=(self.num_channels, self.max_resolution, self.max_resolution),      dtype=np.uinta))
          else:
             _lowercase              =[]
             for i in range(self.batch_size):
                _lowercase       ,       _lowercase              =np.random.choice(np.arange(self.min_resolution,      self.max_resolution),      2)
                image_inputs.append(np.random.randint(255,      size=(self.num_channels, width, height),      dtype=np.uinta))

          if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             _lowercase              =[Image.fromarray(np.moveaxis(snake_case,      0,      -1)) for x in image_inputs]

          if torchify:
             _lowercase              =[torch.from_numpy(snake_case) for x in image_inputs]

          return image_inputs






@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		List[Any]					=ChineseCLIPImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =ChineseCLIPImageProcessingTester(self,      do_center_crop=snake_case)





       @property
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_resize'))
          self.assertTrue(hasattr(snake_case,      'size'))
          self.assertTrue(hasattr(snake_case,      'do_center_crop'))
          self.assertTrue(hasattr(snake_case,      'center_crop'))
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'image_mean'))
          self.assertTrue(hasattr(snake_case,      'image_std'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size,      {'height': 224, 'width': 224})
          self.assertEqual(image_processor.crop_size,      {'height': 18, 'width': 18})

          _lowercase              =self.image_processing_class.from_dict(self.image_processor_dict,      size=42,      crop_size=84)
          self.assertEqual(image_processor.size,      {'shortest_edge': 42})
          self.assertEqual(image_processor.crop_size,      {'height': 84, 'width': 84})





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          pass





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =self.image_processor_tester.prepare_inputs(equal_resolution=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case,      Image.Image)

          # Test not batched input
          _lowercase              =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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

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

          # Test not batched input
          _lowercase              =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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

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

          # Test not batched input
          _lowercase              =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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )






@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=ChineseCLIPImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =ChineseCLIPImageProcessingTester(self,      num_channels=4,      do_center_crop=snake_case)
          _lowercase              =3





       @property
       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_resize'))
          self.assertTrue(hasattr(snake_case,      'size'))
          self.assertTrue(hasattr(snake_case,      'do_center_crop'))
          self.assertTrue(hasattr(snake_case,      'center_crop'))
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'image_mean'))
          self.assertTrue(hasattr(snake_case,      'image_std'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          pass





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =self.image_processor_tester.prepare_inputs(equal_resolution=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case,      Image.Image)

          # Test not batched input
          _lowercase              =image_processing(image_inputs[0],      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(snake_case,      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_processor_tester.batch_size,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )


