






"""simple docstring"""



from __future__ import annotations

_UpperCamelCase						:    Optional[int]		=						[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				,						_lowerCamelCase			):
      lowercase				       =			-1
      lowercase				       =			-1
      lowercase				       =			[-1] * length
      lowercase				       =			None
      lowercase				       =			None







    def __repr__(     self			):
      return F'Node(min_value={self.minn} max_value={self.maxx})'


def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    list[int]       ):





  '''simple docstring'''





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

  lowercase				       =			[]
  lowercase				       =			[]

  for index, num in enumerate(__snake_case       ):
    if num <= pivot:
      left_arr.append(__snake_case       )
    else:
      right_arr.append(__snake_case       )
    lowercase				       =			len(__snake_case       )
  lowercase				       =			build_tree(__snake_case       )
  lowercase				       =			build_tree(__snake_case       )
  return root
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Node | None						,   __snake_case     :    int						,   __snake_case     :    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
  lowercase				       =			(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						,   __snake_case						,   node.map_left[index] - 1       )
  else:
    # go to the right subtree and map index to the right subtree
    return rank_till_index(node.right						,   __snake_case						,   index - node.map_left[index]       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Node | None						,   __snake_case     :    int						,   __snake_case     :    int						,   __snake_case     :    int       ):





  '''simple docstring'''





  if start > end:
    return 0
  lowercase				       =			rank_till_index(__snake_case						,   __snake_case						,   __snake_case       )
  lowercase				       =			rank_till_index(__snake_case						,   __snake_case						,   start - 1       )
  return rank_till_end - rank_before_start
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Node | None						,   __snake_case     :    int						,   __snake_case     :    int						,   __snake_case     :    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]
  lowercase				       =			node.map_left[end] - (
      node.map_left[start - 1] if start else 0
  )
  if num_elements_in_left_tree > index:
    return quantile(
        node.left						,   __snake_case						,   (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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Node | None						,   __snake_case     :    int						,   __snake_case     :    int						,   __snake_case     :    int						,   __snake_case     :    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
  lowercase				       =			range_counting(
      node.left						,   (node.map_left[start - 1] if start else 0)						,   node.map_left[end] - 1						,   __snake_case						,   __snake_case						,   )
  lowercase				       =			range_counting(
      node.right						,   start - (node.map_left[start - 1] if start else 0)						,   end - node.map_left[end]						,   __snake_case						,   __snake_case						,   )
  return left + right


if __name__ == "__main__":
    import doctest

    doctest.testmod()



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 EfficientNetImageProcessor







class       __snake_case (  unittest.TestCase ):
 def __init__(	self		, snake_case__		, snake_case__=13		, snake_case__=3		, snake_case__=18		, snake_case__=30		, snake_case__=400		, snake_case__=True		, snake_case__=None		, snake_case__=True		, snake_case__=[0.5, 0.5, 0.5]		, snake_case__=[0.5, 0.5, 0.5]		, )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=size if size is not None else {'''height''': 18, '''width''': 18}
       UpperCAmelCase     :			Tuple     	=parent
       UpperCAmelCase     :			List[str]     	=batch_size
       UpperCAmelCase     :			int     	=num_channels
       UpperCAmelCase     :			List[str]     	=image_size
       UpperCAmelCase     :			str     	=min_resolution
       UpperCAmelCase     :			Optional[int]     	=max_resolution
       UpperCAmelCase     :			Optional[int]     	=do_resize
       UpperCAmelCase     :			int     	=size
       UpperCAmelCase     :			Union[str, Any]     	=do_normalize
       UpperCAmelCase     :			Dict     	=image_mean
       UpperCAmelCase     :			List[Any]     	=image_std






 def 						UpperCAmelCase__							(	self    )						->				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,
       }







@require_torch
@require_vision
class       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   EfficientNetImageProcessor if is_vision_available() else None
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=EfficientNetImageProcessorTester(self    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(snake_case__		, '''image_mean'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''image_std'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_normalize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_resize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''size'''    )    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size		, {'''height''': 18, '''width''': 18}    )

       UpperCAmelCase     :			List[str]     	=self.image_processing_class.from_dict(self.image_processor_dict		, size=42    )
       self.assertEqual(image_processor.size		, {'''height''': 42, '''width''': 42}    )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       UpperCAmelCase     :			Optional[Any]     	=prepare_image_inputs(self.image_processor_tester		, equal_resolution=snake_case__    )
       for image in image_inputs:
             self.assertIsInstance(snake_case__		, Image.Image    )

       # Test not batched input
       UpperCAmelCase     :			int     	=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
       UpperCAmelCase     :			int     	=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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       UpperCAmelCase     :			List[str]     	=prepare_image_inputs(self.image_processor_tester		, equal_resolution=snake_case__		, numpify=snake_case__    )
       for image in image_inputs:
             self.assertIsInstance(snake_case__		, np.ndarray    )

       # Test not batched input
       UpperCAmelCase     :			str     	=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
       UpperCAmelCase     :			Union[str, Any]     	=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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )






 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





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

       # Test not batched input
       UpperCAmelCase     :			List[Any]     	=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
       UpperCAmelCase     :			List[str]     	=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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )
