


"""simple docstring"""







import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor




class 					UpperCamelCase_      (					unittest.TestCase):

	"""simple docstring"""





	def __init__(					self     :							List[Any]     ,     UpperCAmelCase__     :							Optional[int]     ,     UpperCAmelCase__     :							Tuple=7     ,     UpperCAmelCase__     :							Any=3     ,     UpperCAmelCase__     :							List[Any]=1_8     ,     UpperCAmelCase__     :							int=3_0     ,     UpperCAmelCase__     :							List[Any]=4_0_0     ,     UpperCAmelCase__     :							List[Any]=True     ,     UpperCAmelCase__     :							Any=None     ,     UpperCAmelCase__     :							List[Any]=True     ,     UpperCAmelCase__     :							List[Any]=[0.5, 0.5, 0.5]     ,     UpperCAmelCase__     :							Tuple=[0.5, 0.5, 0.5]     ,     )			->      List[Any]:
						__SCREAMING_SNAKE_CASE		  =							size if size is not None else {"height": 1_8, "width": 1_8}
						__SCREAMING_SNAKE_CASE		  =							parent
						__SCREAMING_SNAKE_CASE		  =							batch_size
						__SCREAMING_SNAKE_CASE		  =							num_channels
						__SCREAMING_SNAKE_CASE		  =							image_size
						__SCREAMING_SNAKE_CASE		  =							min_resolution
						__SCREAMING_SNAKE_CASE		  =							max_resolution
						__SCREAMING_SNAKE_CASE		  =							do_resize
						__SCREAMING_SNAKE_CASE		  =							size
						__SCREAMING_SNAKE_CASE		  =							do_normalize
						__SCREAMING_SNAKE_CASE		  =							image_mean
						__SCREAMING_SNAKE_CASE		  =							image_std




	def     UpperCAmelCase_ (					self     :							int  )			->      List[str]:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						}




@require_torch
@require_vision
class 					UpperCamelCase_      (					UpperCamelCase     ,    unittest.TestCase):

	"""simple docstring"""





	snake_case__							:    List[str]          =     DPTImageProcessor if is_vision_available() else None




	def     UpperCAmelCase_ (					self     :							List[Any]  )			->      Union[str, Any]:
						__SCREAMING_SNAKE_CASE		  =							DPTImageProcessingTester(self  )




	@property
	def     UpperCAmelCase_ (					self     :							Tuple  )			->      Dict:
						return self.image_processor_tester.prepare_image_processor_dict()




	def     UpperCAmelCase_ (					self     :							Any  )			->      str:
						__SCREAMING_SNAKE_CASE		  =							self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(lowerCamelCase_     ,     "image_mean"  )  )
						self.assertTrue(hasattr(lowerCamelCase_     ,     "image_std"  )  )
						self.assertTrue(hasattr(lowerCamelCase_     ,     "do_normalize"  )  )
						self.assertTrue(hasattr(lowerCamelCase_     ,     "do_resize"  )  )
						self.assertTrue(hasattr(lowerCamelCase_     ,     "size"  )  )




	def     UpperCAmelCase_ (					self     :							Any  )			->      Union[str, Any]:
						__SCREAMING_SNAKE_CASE		  =							self.image_processing_class.from_dict(self.image_processor_dict  )
						self.assertEqual(image_processor.size     ,     {"height": 1_8, "width": 1_8}  )

						__SCREAMING_SNAKE_CASE		  =							self.image_processing_class.from_dict(self.image_processor_dict     ,     size=4_2  )
						self.assertEqual(image_processor.size     ,     {"height": 4_2, "width": 4_2}  )




	def     UpperCAmelCase_ (					self     :							Optional[Any]  )			->      str:
						# Initialize image_processing
						__SCREAMING_SNAKE_CASE		  =							self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__SCREAMING_SNAKE_CASE		  =							prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=lowerCamelCase_  )
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_     ,     Image.Image  )

						# Test not batched input
						__SCREAMING_SNAKE_CASE		  =							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		  =							image_processing(lowerCamelCase_     ,     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     UpperCAmelCase_ (					self     :							str  )			->      Dict:
						# Initialize image_processing
						__SCREAMING_SNAKE_CASE		  =							self.image_processing_class(**self.image_processor_dict  )
						# create random numpy tensors
						__SCREAMING_SNAKE_CASE		  =							prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=lowerCamelCase_     ,     numpify=lowerCamelCase_  )
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_     ,     np.ndarray  )

						# Test not batched input
						__SCREAMING_SNAKE_CASE		  =							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		  =							image_processing(lowerCamelCase_     ,     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     UpperCAmelCase_ (					self     :							Optional[int]  )			->      Optional[int]:
						# Initialize image_processing
						__SCREAMING_SNAKE_CASE		  =							self.image_processing_class(**self.image_processor_dict  )
						# create random PyTorch tensors
						__SCREAMING_SNAKE_CASE		  =							prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=lowerCamelCase_     ,     torchify=lowerCamelCase_  )
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_     ,     torch.Tensor  )

						# Test not batched input
						__SCREAMING_SNAKE_CASE		  =							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		  =							image_processing(lowerCamelCase_     ,     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"],
						    )     ,     )



"""simple docstring"""


def   _A					(	_a	:						Optional[int]     ,   _a	:						str				):



      """simple docstring"""






      A				  =		0
      A				  =		len(_a				) - 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

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

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

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



def   _A					(	_a	:						Any     ,   _a	:						List[str]     ,   _a	:						Dict     ,   _a	:						List[str]				):



      """simple docstring"""






      if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                  return left
            else:
                  return None

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

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

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



def   _A					(	_a	:						List[Any]				):



      """simple docstring"""






      if collection != sorted(_a				):
            raise ValueError("""Collection must be ascending sorted"""				)
      return True


if __name__ == "__main__":
  import sys
  UpperCAmelCase													=0
  if debug == 1:
    UpperCAmelCase													=[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")
    UpperCAmelCase													=67

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




