


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 ViltImageProcessor





class   A							(       unittest.TestCase  ):


   def __init__(self  :		Optional[Any] ,						__UpperCAmelCase  :		Optional[int] ,						__UpperCAmelCase  :		str=7 ,						__UpperCAmelCase  :		Optional[Any]=3 ,						__UpperCAmelCase  :		List[Any]=1_8 ,						__UpperCAmelCase  :		int=3_0 ,						__UpperCAmelCase  :		Optional[int]=4_0_0 ,						__UpperCAmelCase  :		str=True ,						__UpperCAmelCase  :		List[str]=None ,						__UpperCAmelCase  :		Union[str, Any]=2 ,						__UpperCAmelCase  :		List[Any]=True ,						__UpperCAmelCase  :		Dict=[0.5, 0.5, 0.5] ,						__UpperCAmelCase  :		List[Any]=[0.5, 0.5, 0.5] ,						)		->       List[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						size if size is not None else {'shortest_edge': 3_0}
         UpperCAmelCase__			=						parent
         UpperCAmelCase__			=						batch_size
         UpperCAmelCase__			=						num_channels
         UpperCAmelCase__			=						image_size
         UpperCAmelCase__			=						min_resolution
         UpperCAmelCase__			=						max_resolution
         UpperCAmelCase__			=						do_resize
         UpperCAmelCase__			=						size
         UpperCAmelCase__			=						size_divisor
         UpperCAmelCase__			=						do_normalize
         UpperCAmelCase__			=						image_mean
         UpperCAmelCase__			=						image_std


   def     lowercase_   (self  :		int	)		->       List[Any]:
         """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     lowercase_   (self  :		Dict ,						__UpperCAmelCase  :		List[str] ,						__UpperCAmelCase  :		Tuple=False	)		->       Union[str, Any]:
         """simple docstring"""



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

               UpperCAmelCase__			=						int((1_3_3_3 / 8_0_0) * size	)
               if max(SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_	) > max_size:
                     UpperCAmelCase__			=						max_size / max(SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_	)
                     UpperCAmelCase__			=						newh * scale
                     UpperCAmelCase__			=						neww * scale

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

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

         return expected_height, expected_width





@require_torch
@require_vision
class   A							(       a__	,       unittest.TestCase  ):
   __UpperCAmelCase				:				List[Any]          =    ViltImageProcessor if is_vision_available() else None


   def     lowercase_   (self  :		Optional[Any]	)		->       Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__			=						ViltImageProcessingTester(self	)


   @property
   def     lowercase_   (self  :		List[Any]	)		->       Dict:
         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()


   def     lowercase_   (self  :		List[str]	)		->       Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						"image_mean"	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						"image_std"	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						"do_normalize"	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						"do_resize"	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						"size"	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						"size_divisor"	)	)


   def     lowercase_   (self  :		Tuple	)		->       List[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class.from_dict(self.image_processor_dict	)
         self.assertEqual(image_processor.size ,						{"shortest_edge": 3_0}	)

         UpperCAmelCase__			=						self.image_processing_class.from_dict(self.image_processor_dict ,						size=4_2	)
         self.assertEqual(image_processor.size ,						{"shortest_edge": 4_2}	)


   def     lowercase_   (self  :		str	)		->       Dict:
         """simple docstring"""



         pass


   def     lowercase_   (self  :		Optional[Any]	)		->       str:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         UpperCAmelCase__			=						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
         UpperCAmelCase__			=						image_processing(image_inputs[0] ,						return_tensors="pt"	).pixel_values

         UpperCAmelCase__			=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(
             encoded_images.shape ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,						)

         # Test batched
         UpperCAmelCase__			=						image_processing(SCREAMING_SNAKE_CASE_ ,						return_tensors="pt"	).pixel_values

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


   def     lowercase_   (self  :		Union[str, Any]	)		->       List[str]:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class(**self.image_processor_dict	)
         # create random numpy tensors
         UpperCAmelCase__			=						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
         UpperCAmelCase__			=						image_processing(image_inputs[0] ,						return_tensors="pt"	).pixel_values

         UpperCAmelCase__			=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(
             encoded_images.shape ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,						)

         # Test batched
         UpperCAmelCase__			=						image_processing(SCREAMING_SNAKE_CASE_ ,						return_tensors="pt"	).pixel_values

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





   def     lowercase_   (self  :		Any	)		->       Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class(**self.image_processor_dict	)
         # create random PyTorch tensors
         UpperCAmelCase__			=						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
         UpperCAmelCase__			=						image_processing(image_inputs[0] ,						return_tensors="pt"	).pixel_values

         UpperCAmelCase__			=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(
             encoded_images.shape ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,						)

         # Test batched
         UpperCAmelCase__			=						image_processing(SCREAMING_SNAKE_CASE_ ,						return_tensors="pt"	).pixel_values

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




from __future__ import annotations

UpperCamelCase__       							=      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class   A							:


   def __init__(self  :		Optional[int] ,						__UpperCAmelCase  :		int	)		->       None:
         """simple docstring"""



         UpperCAmelCase__			=						-1
         UpperCAmelCase__			=						-1
         UpperCAmelCase__			=						[-1] * length
         UpperCAmelCase__			=						None
         UpperCAmelCase__			=						None





   def __repr__(self  :		Union[str, Any]	)		->       str:
         """simple docstring"""



         return f"""Node(min_value={self.minn} max_value={self.maxx})"""




def      lowerCAmelCase_				(  __A							)  ->       Node | None:







      '''simple docstring'''


      UpperCAmelCase__			=						Node(len(__A							)							)
      UpperCAmelCase__	,   UpperCAmelCase__			=						min(__A							), max(__A							)
      # Leaf node case where the node contains only one unique value
      if root.minn == root.maxx:
            return root
      UpperCAmelCase__			=						(root.minn + root.maxx) // 2

      UpperCAmelCase__			=						[]
      UpperCAmelCase__			=						[]

      for index, num in enumerate(__A							):
            if num <= pivot:
                  left_arr.append(__A							)
            else:
                  right_arr.append(__A							)
            UpperCAmelCase__			=						len(__A							)
      UpperCAmelCase__			=						build_tree(__A							)
      UpperCAmelCase__			=						build_tree(__A							)
      return root




def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if index < 0 or node is None:
            return 0
      # Leaf node cases
      if node.minn == node.maxx:
            return index + 1 if node.minn == num else 0
      UpperCAmelCase__			=						(node.minn + node.maxx) // 2
      if num <= pivot:
            # go the left subtree and map index to the left subtree
            return rank_till_index(node.left,						__A,						node.map_left[index] - 1							)
      else:
            # go to the right subtree and map index to the right subtree
            return rank_till_index(node.right,						__A,						index - node.map_left[index]							)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if start > end:
            return 0
      UpperCAmelCase__			=						rank_till_index(__A,						__A,						__A							)
      UpperCAmelCase__			=						rank_till_index(__A,						__A,						start - 1							)
      return rank_till_end - rank_before_start




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if index > (end - start) or start > end or node is None:
            return -1
      # Leaf node case
      if node.minn == node.maxx:
            return node.minn
      # Number of elements in the left subtree in interval [start, end]
      UpperCAmelCase__			=						node.map_left[end] - (
          node.map_left[start - 1] if start else 0
      )
      if num_elements_in_left_tree > index:
            return quantile(
                node.left,						__A,						(node.map_left[start - 1] if start else 0),						node.map_left[end] - 1,						)
      else:
            return quantile(
                node.right,						index - num_elements_in_left_tree,						start - (node.map_left[start - 1] if start else 0),						end - node.map_left[end],						)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if (
          start > end
          or node is None
          or start_num > end_num
          or node.minn > end_num
          or node.maxx < start_num
      ):
            return 0
      if start_num <= node.minn and node.maxx <= end_num:
            return end - start + 1
      UpperCAmelCase__			=						range_counting(
          node.left,						(node.map_left[start - 1] if start else 0),						node.map_left[end] - 1,						__A,						__A,						)
      UpperCAmelCase__			=						range_counting(
          node.right,						start - (node.map_left[start - 1] if start else 0),						end - node.map_left[end],						__A,						__A,						)
      return left + right


if __name__ == "__main__":
      import doctest

      doctest.testmod()



