'''simple docstring'''





import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor






class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=7							,     lowerCAmelCase_=3							,     lowerCAmelCase_=30							,     lowerCAmelCase_=4_00							,     lowerCAmelCase_=True							,     lowerCAmelCase_=None							,     lowerCAmelCase_=True							,     lowerCAmelCase_=[0.5, 0.5, 0.5]							,     lowerCAmelCase_=[0.5, 0.5, 0.5]							,     lowerCAmelCase_=False							,     )	->Dict:
        a  :    Any				=						size if size is not None else {'''height''': 30, '''width''': 30}
        a  :    int				=						parent
        a  :    Optional[int]				=						batch_size
        a  :    Any				=						num_channels
        a  :    str				=						min_resolution
        a  :    Tuple				=						max_resolution
        a  :    str				=						do_resize
        a  :    Tuple				=						size
        a  :    Optional[Any]				=						do_normalize
        a  :    Any				=						image_mean
        a  :    List[Any]				=						image_std
        a  :    Dict				=						do_reduce_labels


   def _lowercase(    self    )	->Any:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_reduce_labels": self.do_reduce_labels,
        }







def 	UpperCAmelCase       (		)	->	int:
     a  :    List[str]				=						load_dataset('''hf-internal-testing/fixtures_ade20k'''						,      split='''test'''       )

     a  :    str				=						Image.open(dataset[0]['''file''']       )
     a  :    Tuple				=						Image.open(dataset[1]['''file''']       )

     return image, map







def 	UpperCAmelCase       (		)	->	Optional[int]:
     a  :    Optional[int]				=						load_dataset('''hf-internal-testing/fixtures_ade20k'''						,      split='''test'''       )

     a  :    Union[str, Any]				=						Image.open(dataset[0]['''file''']       )
     a  :    Optional[Any]				=						Image.open(dataset[1]['''file''']       )
     a  :    Optional[int]				=						Image.open(dataset[2]['''file''']       )
     a  :    List[Any]				=						Image.open(dataset[3]['''file''']       )

     return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      unittest.TestCase	):
   UpperCamelCase_				:						Union[str, Any] 			=   SegformerImageProcessor if is_vision_available() else None
   def _lowercase(    self    )	->Dict:
        a  :    str				=						SegformerImageProcessingTester(self    )
   @property
   def _lowercase(    self    )	->Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()
   def _lowercase(    self    )	->Dict:
        a  :    int				=						self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''do_resize'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''size'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''do_normalize'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''image_mean'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''image_std'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''do_reduce_labels'''    )    )
   def _lowercase(    self    )	->str:
        a  :    Any				=						self.image_processing_class.from_dict(self.image_processor_dict    )
        self.assertEqual(image_processor.size							,     {'''height''': 30, '''width''': 30}    )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase_    )

        a  :    Dict				=						self.image_processing_class.from_dict(self.image_processor_dict							,     size=42							,     reduce_labels=lowerCAmelCase_    )
        self.assertEqual(image_processor.size							,     {'''height''': 42, '''width''': 42}    )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase_    )
   def _lowercase(    self    )	->str:
        pass
   def _lowercase(    self    )	->Union[str, Any]:
        # Initialize image_processing
        a  :    List[Any]				=						self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        a  :    Dict				=						prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase_    )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_							,     Image.Image    )

        # Test not batched input
        a  :    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
        a  :    Any				=						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 _lowercase(    self    )	->Union[str, Any]:
        # Initialize image_processing
        a  :    List[Any]				=						self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        a  :    int				=						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
        a  :    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
        a  :    int				=						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 _lowercase(    self    )	->Dict:
        # Initialize image_processing
        a  :    int				=						self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        a  :    str				=						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
        a  :    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
        a  :    Any				=						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 _lowercase(    self    )	->str:
        # Initialize image_processing
        a  :    Tuple				=						self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        a  :    Optional[Any]				=						prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase_							,     torchify=lowerCAmelCase_    )
        a  :    Dict				=						[]
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_							,     torch.Tensor    )
             maps.append(torch.zeros(image.shape[-2:]    ).long()    )

        # Test not batched input
        a  :    List[Any]				=						image_processing(image_inputs[0]							,     maps[0]							,     return_tensors='''pt'''    )
        self.assertEqual(
            encoding['''pixel_values'''].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(
            encoding['''labels'''].shape							,     (
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(encoding['''labels'''].dtype							,     torch.long    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 2_55    )

        # Test batched
        a  :    List[str]				=						image_processing(lowerCAmelCase_							,     lowerCAmelCase_							,     return_tensors='''pt'''    )
        self.assertEqual(
            encoding['''pixel_values'''].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'''],
            )							,     )
        self.assertEqual(
            encoding['''labels'''].shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(encoding['''labels'''].dtype							,     torch.long    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 2_55    )

        # Test not batched input (PIL images)
        a  :    List[str]				=						prepare_semantic_single_inputs()

        a  :    Dict				=						image_processing(lowerCAmelCase_							,     lowerCAmelCase_							,     return_tensors='''pt'''    )
        self.assertEqual(
            encoding['''pixel_values'''].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(
            encoding['''labels'''].shape							,     (
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(encoding['''labels'''].dtype							,     torch.long    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 2_55    )

        # Test batched input (PIL images)
        a  :    int				=						prepare_semantic_batch_inputs()

        a  :    Optional[Any]				=						image_processing(lowerCAmelCase_							,     lowerCAmelCase_							,     return_tensors='''pt'''    )
        self.assertEqual(
            encoding['''pixel_values'''].shape							,     (
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(
            encoding['''labels'''].shape							,     (
                2,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(encoding['''labels'''].dtype							,     torch.long    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 2_55    )


   def _lowercase(    self    )	->Optional[int]:
        # Initialize image_processing
        a  :    Optional[Any]				=						self.image_processing_class(**self.image_processor_dict    )

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        a  :    Optional[int]				=						prepare_semantic_single_inputs()
        a  :    Optional[Any]				=						image_processing(lowerCAmelCase_							,     lowerCAmelCase_							,     return_tensors='''pt'''    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 1_50    )

        a  :    Optional[Any]				=						True
        a  :    Union[str, Any]				=						image_processing(lowerCAmelCase_							,     lowerCAmelCase_							,     return_tensors='''pt'''    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 2_55    )
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 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_    )	->None:
        a  :    int				=						-1
        a  :    int				=						-1
        a  :    list[int]				=						[-1] * length
        a  :    Node | None				=						None
        a  :    Node | None				=						None


   def __repr__(    self    )	->str:
        return F"""Node(min_value={self.minn} max_value={self.maxx})"""







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Node | None:
     a  :    Dict				=						Node(len(lowerCAmelCase_       )       )
     a			,							a  :    Tuple				=						min(lowerCAmelCase_       ), max(lowerCAmelCase_       )
     # Leaf node case where the node contains only one unique value
     if root.minn == root.maxx:
          return root
     a  :    Optional[Any]				=						(root.minn + root.maxx) // 2

     a  :    list[int]				=						[]
     a  :    list[int]				=						[]

     for index, num in enumerate(lowerCAmelCase_       ):
          if num <= pivot:
               left_arr.append(lowerCAmelCase_       )
          else:
               right_arr.append(lowerCAmelCase_       )
          a  :    int				=						len(lowerCAmelCase_       )
     a  :    List[str]				=						build_tree(lowerCAmelCase_       )
     a  :    List[str]				=						build_tree(lowerCAmelCase_       )
     return root







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     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
     a  :    List[Any]				=						(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						,      lowerCAmelCase_						,      node.map_left[index] - 1       )
     else:
          # go to the right subtree and map index to the right subtree
          return rank_till_index(node.right						,      lowerCAmelCase_						,      index - node.map_left[index]       )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if start > end:
          return 0
     a  :    Optional[Any]				=						rank_till_index(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )
     a  :    Any				=						rank_till_index(lowerCAmelCase_						,      lowerCAmelCase_						,      start - 1       )
     return rank_till_end - rank_before_start







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     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]
     a  :    Optional[int]				=						node.map_left[end] - (
         node.map_left[start - 1] if start else 0
     )
     if num_elements_in_left_tree > index:
          return quantile(
              node.left						,      lowerCAmelCase_						,      (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 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     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
     a  :    Tuple				=						range_counting(
         node.left						,      (node.map_left[start - 1] if start else 0)						,      node.map_left[end] - 1						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )
     a  :    int				=						range_counting(
         node.right						,      start - (node.map_left[start - 1] if start else 0)						,      end - node.map_left[end]						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )
     return left + right


if __name__ == "__main__":
  import doctest

  doctest.testmod()