

"""simple docstring"""
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_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VivitImageProcessor

class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase							,       _lowerCamelCase=7							,       _lowerCamelCase=3							,       _lowerCamelCase=10							,       _lowerCamelCase=18							,       _lowerCamelCase=30							,       _lowerCamelCase=400							,       _lowerCamelCase=True							,       _lowerCamelCase=None							,       _lowerCamelCase=True							,       _lowerCamelCase=[0.5, 0.5, 0.5]							,       _lowerCamelCase=[0.5, 0.5, 0.5]							,       _lowerCamelCase=None							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    size if size is not None else {"""shortest_edge""": 18}
        UpperCAmelCase__			:							Union[str, Any]							=    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

        UpperCAmelCase__			:							int							=    parent
        UpperCAmelCase__			:							int							=    batch_size
        UpperCAmelCase__			:							Optional[int]							=    num_channels
        UpperCAmelCase__			:							Optional[Any]							=    num_frames
        UpperCAmelCase__			:							Dict							=    image_size
        UpperCAmelCase__			:							List[str]							=    min_resolution
        UpperCAmelCase__			:							Optional[Any]							=    max_resolution
        UpperCAmelCase__			:							Optional[Any]							=    do_resize
        UpperCAmelCase__			:							Optional[Any]							=    size
        UpperCAmelCase__			:							Tuple							=    do_normalize
        UpperCAmelCase__			:							Tuple							=    image_mean
        UpperCAmelCase__			:							List[str]							=    image_std
        UpperCAmelCase__			:							Optional[Any]							=    crop_size




  def  _a      (self	):


        """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,
            "crop_size": self.crop_size,
        }




@require_torch
@require_vision
class       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  VivitImageProcessor if is_vision_available() else None



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    VivitImageProcessingTester(self	)



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.image_processor_tester.prepare_image_processor_dict()



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    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							,       """do_center_crop"""	)	)
        self.assertTrue(hasattr(_lowerCamelCase							,       """size"""	)	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size							,       {"""shortest_edge""": 18}	)
        self.assertEqual(image_processor.crop_size							,       {"""height""": 18, """width""": 18}	)

        UpperCAmelCase__			:							List[str]							=    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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    self.image_processing_class(**self.image_processor_dict	)
        # create random PIL videos
        UpperCAmelCase__			:							Any							=    prepare_video_inputs(self.image_processor_tester							,       equal_resolution=_lowerCamelCase	)
        for video in video_inputs:
              self.assertIsInstance(_lowerCamelCase							,       _lowerCamelCase	)
              self.assertIsInstance(video[0]							,       Image.Image	)

        # Test not batched input
        UpperCAmelCase__			:							str							=    image_processing(video_inputs[0]							,       return_tensors="""pt"""	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )							,       )

        # Test batched
        UpperCAmelCase__			:							List[str]							=    image_processing(_lowerCamelCase							,       return_tensors="""pt"""	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )							,       )



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        UpperCAmelCase__			:							int							=    prepare_video_inputs(self.image_processor_tester							,       equal_resolution=_lowerCamelCase							,       numpify=_lowerCamelCase	)
        for video in video_inputs:
              self.assertIsInstance(_lowerCamelCase							,       _lowerCamelCase	)
              self.assertIsInstance(video[0]							,       np.ndarray	)

        # Test not batched input
        UpperCAmelCase__			:							List[Any]							=    image_processing(video_inputs[0]							,       return_tensors="""pt"""	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )							,       )

        # Test batched
        UpperCAmelCase__			:							Any							=    image_processing(_lowerCamelCase							,       return_tensors="""pt"""	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )							,       )




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        UpperCAmelCase__			:							Union[str, Any]							=    prepare_video_inputs(self.image_processor_tester							,       equal_resolution=_lowerCamelCase							,       torchify=_lowerCamelCase	)
        for video in video_inputs:
              self.assertIsInstance(_lowerCamelCase							,       _lowerCamelCase	)
              self.assertIsInstance(video[0]							,       torch.Tensor	)

        # Test not batched input
        UpperCAmelCase__			:							List[Any]							=    image_processing(video_inputs[0]							,       return_tensors="""pt"""	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )							,       )

        # Test batched
        UpperCAmelCase__			:							Dict							=    image_processing(_lowerCamelCase							,       return_tensors="""pt"""	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )							,       )




"""simple docstring"""
from __future__ import annotations

import math

class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    size
        # approximate the overall size of segment tree with given value
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]
        # create array to store lazy update
        UpperCAmelCase__			:							int							=    [0 for i in range(0							,       4 * size	)]
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]  # flag for lazy update



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2 + 1



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if left_element == right_element:
              UpperCAmelCase__			:							Union[str, Any]							=    a[left_element - 1]
        else:
              UpperCAmelCase__			:							List[str]							=    (left_element + right_element) // 2
              self.build(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
              self.build(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase	)
              UpperCAmelCase__			:							int							=    max(
                  self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Dict							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Optional[Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Optional[int]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Tuple							=    True
                    UpperCAmelCase__			:							str							=    True

        if right_element < a or left_element > b:
              return True
        if left_element >= a and right_element <= b:
              UpperCAmelCase__			:							Union[str, Any]							=    val
              if left_element != right_element:
                    UpperCAmelCase__			:							Union[str, Any]							=    val
                    UpperCAmelCase__			:							List[Any]							=    val
                    UpperCAmelCase__			:							Union[str, Any]							=    True
                    UpperCAmelCase__			:							List[Any]							=    True
              return True
        UpperCAmelCase__			:							Tuple							=    (left_element + right_element) // 2
        self.update(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        self.update(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							Dict							=    max(
            self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)
        return True



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							List[Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Optional[Any]							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Tuple							=    self.lazy[idx]
                    UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							List[str]							=    True
                    UpperCAmelCase__			:							str							=    True
        if right_element < a or left_element > b:
              return -math.inf
        if left_element >= a and right_element <= b:
              return self.segment_tree[idx]
        UpperCAmelCase__			:							Union[str, Any]							=    (left_element + right_element) // 2
        UpperCAmelCase__			:							Tuple							=    self.query(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							List[Any]							=    self.query(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        return max(_lowerCamelCase							,       _lowerCamelCase	)




  def __str__(self	):


        """simple docstring"""

        return str([self.query(1							,       1							,       self.size							,       _lowerCamelCase							,       _lowerCamelCase	) for i in range(1							,       self.size + 1	)]	)


if __name__ == "__main__":
    _A							=  [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    _A							=  15
    _A							=  SegmentTree(size)
    segt.build(1, 1, size, A)
    print(segt.query(1, 1, size, 4, 6))
    print(segt.query(1, 1, size, 7, 11))
    print(segt.query(1, 1, size, 7, 12))
    segt.update(1, 1, size, 1, 3, 1_11)
    print(segt.query(1, 1, size, 1, 15))
    segt.update(1, 1, size, 7, 8, 2_35)
    print(segt)

