






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 DeiTImageProcessor




class 	lowerCamelCase__				(							unittest.TestCase ):






      def __init__(self					:    List[Any]		,		_snake_case					:    Dict		,		_snake_case					:    Any=7		,		_snake_case					:    Any=3		,		_snake_case					:    Optional[Any]=18		,		_snake_case					:    Any=30		,		_snake_case					:    Dict=400		,		_snake_case					:    str=True		,		_snake_case					:    int=None		,		_snake_case					:    Tuple=True		,		_snake_case					:    Dict=None		,		_snake_case					:    List[Any]=True		,		_snake_case					:    Optional[Any]=[0.5, 0.5, 0.5]		,		_snake_case					:    List[Any]=[0.5, 0.5, 0.5]		,		)       ->   str:


       """simple docstring"""






       lowerCamelCase_							:    Union[str, Any]							=     size if size is not None else {'height': 20, 'width': 20}
       lowerCamelCase_							:    List[str]							=     crop_size if crop_size is not None else {'height': 18, 'width': 18}

       lowerCamelCase_							:    Tuple							=     parent
       lowerCamelCase_							:    int							=     batch_size
       lowerCamelCase_							:    Union[str, Any]							=     num_channels
       lowerCamelCase_							:    str							=     image_size
       lowerCamelCase_							:    Optional[int]							=     min_resolution
       lowerCamelCase_							:    List[str]							=     max_resolution
       lowerCamelCase_							:    Tuple							=     do_resize
       lowerCamelCase_							:    int							=     size
       lowerCamelCase_							:    Tuple							=     do_center_crop
       lowerCamelCase_							:    int							=     crop_size
       lowerCamelCase_							:    List[Any]							=     do_normalize
       lowerCamelCase_							:    List[str]							=     image_mean
       lowerCamelCase_							:    int							=     image_std






      def      UpperCAmelCase_     (self					:    Union[str, Any]		)       ->   Optional[Any]:


       """simple docstring"""






       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }




@require_torch
@require_vision
class 	lowerCamelCase__				(							UpperCAmelCase,   unittest.TestCase ):
      lowerCamelCase_							:   Dict											=	DeiTImageProcessor if is_vision_available() else None
      lowerCamelCase_							:   Tuple											=	True






      def      UpperCAmelCase_     (self					:    Tuple		)       ->   List[Any]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     DeiTImageProcessingTester(self		)






      @property
      def      UpperCAmelCase_     (self					:    List[str]		)       ->   Optional[int]:


       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()






      def      UpperCAmelCase_     (self					:    int		)       ->   int:


       """simple docstring"""






       lowerCamelCase_							:    Any							=     self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(_snake_case		,		'do_resize'		)		)
       self.assertTrue(hasattr(_snake_case		,		'size'		)		)
       self.assertTrue(hasattr(_snake_case		,		'do_center_crop'		)		)
       self.assertTrue(hasattr(_snake_case		,		'center_crop'		)		)
       self.assertTrue(hasattr(_snake_case		,		'do_normalize'		)		)
       self.assertTrue(hasattr(_snake_case		,		'image_mean'		)		)
       self.assertTrue(hasattr(_snake_case		,		'image_std'		)		)






      def      UpperCAmelCase_     (self					:    Dict		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    List[Any]							=     self.image_processing_class.from_dict(self.image_processor_dict		)
       self.assertEqual(image_processor.size		,		{'height': 20, 'width': 20}		)
       self.assertEqual(image_processor.crop_size		,		{'height': 18, 'width': 18}		)

       lowerCamelCase_							:    Tuple							=     self.image_processing_class.from_dict(self.image_processor_dict		,		size=42		,		crop_size=84		)
       self.assertEqual(image_processor.size		,		{'height': 42, 'width': 42}		)
       self.assertEqual(image_processor.crop_size		,		{'height': 84, 'width': 84}		)






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   List[str]:


       """simple docstring"""






       pass






      def      UpperCAmelCase_     (self					:    Dict		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       lowerCamelCase_							:    Dict							=     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
       lowerCamelCase_							:    List[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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )		,		)

       # Test batched
       lowerCamelCase_							:    Optional[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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )		,		)






      def      UpperCAmelCase_     (self					:    str		)       ->   Optional[int]:


       """simple docstring"""






       lowerCamelCase_							:    List[Any]							=     self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       lowerCamelCase_							:    int							=     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
       lowerCamelCase_							:    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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )		,		)

       # Test batched
       lowerCamelCase_							:    Tuple							=     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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )		,		)






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


       """simple docstring"""






       lowerCamelCase_							:    Dict							=     self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       lowerCamelCase_							:    List[Any]							=     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
       lowerCamelCase_							:    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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )		,		)

       # Test batched
       lowerCamelCase_							:    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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )		,		)








import math




class 	lowerCamelCase__				:






      def __init__(self					:    int		,		_snake_case					:    Dict		)       ->   Union[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     len(_snake_case		)
       lowerCamelCase_							:    Optional[Any]							=     [0] * (
           4 * self.N
       )  # approximate the overall size of segment tree with array N
       self.build(1		,		0		,		self.N - 1		)






      def      UpperCAmelCase_     (self					:    List[str]		,		_snake_case					:    List[str]		)       ->   int:


       """simple docstring"""






       return idx * 2






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    Dict		)       ->   Optional[Any]:


       """simple docstring"""






       return idx * 2 + 1






      def      UpperCAmelCase_     (self					:    List[Any]		,		_snake_case					:    List[Any]		,		_snake_case					:    str		,		_snake_case					:    List[Any]		)       ->   Optional[Any]:  # noqa: E741


       """simple docstring"""






       if l == r:
        lowerCamelCase_							:    List[Any]							=     A[l]
       else:
        lowerCamelCase_							:    int							=     (l + r) // 2
        self.build(self.left(_snake_case		)		,		_snake_case		,		_snake_case		)
        self.build(self.right(_snake_case		)		,		mid + 1		,		_snake_case		)
        lowerCamelCase_							:    Tuple							=     max(self.st[self.left(_snake_case		)]		,		self.st[self.right(_snake_case		)]		)






      def      UpperCAmelCase_     (self					:    str		,		_snake_case					:    Tuple		,		_snake_case					:    Any		,		_snake_case					:    str		)       ->   Tuple:


       """simple docstring"""






       return self.update_recursive(1		,		0		,		self.N - 1		,		a - 1		,		b - 1		,		_snake_case		)






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    List[str]		,		_snake_case					:    Tuple		,		_snake_case					:    Optional[int]		,		_snake_case					:    int		,		_snake_case					:    Tuple		,		_snake_case					:    List[str]		)       ->   Optional[Any]:  # noqa: E741


       """simple docstring"""






       if r < a or l > b:
        return True
       if l == r:
        lowerCamelCase_							:    Tuple							=     val
        return True
       lowerCamelCase_							:    Optional[int]							=     (l + r) // 2
       self.update_recursive(self.left(_snake_case		)		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       self.update_recursive(self.right(_snake_case		)		,		mid + 1		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       lowerCamelCase_							:    Union[str, Any]							=     max(self.st[self.left(_snake_case		)]		,		self.st[self.right(_snake_case		)]		)
       return True






      def      UpperCAmelCase_     (self					:    List[Any]		,		_snake_case					:    List[Any]		,		_snake_case					:    Optional[int]		)       ->   str:


       """simple docstring"""






       return self.query_recursive(1		,		0		,		self.N - 1		,		a - 1		,		b - 1		)






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    List[Any]		,		_snake_case					:    Optional[Any]		,		_snake_case					:    int		,		_snake_case					:    Optional[int]		,		_snake_case					:    Optional[Any]		)       ->   str:  # noqa: E741


       """simple docstring"""






       if r < a or l > b:
        return -math.inf
       if l >= a and r <= b:
        return self.st[idx]
       lowerCamelCase_							:    int							=     (l + r) // 2
       lowerCamelCase_							:    Optional[Any]							=     self.query_recursive(self.left(_snake_case		)		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       lowerCamelCase_							:    Union[str, Any]							=     self.query_recursive(self.right(_snake_case		)		,		mid + 1		,		_snake_case		,		_snake_case		,		_snake_case		)
       return max(_snake_case		,		_snake_case		)






      def      UpperCAmelCase_     (self					:    str		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    Dict							=     []
       for i in range(1		,		N + 1		):
        show_list += [self.query(_snake_case		,		_snake_case		)]
       print(_snake_case		)


if __name__ == "__main__":
       UpperCamelCase											=      [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
       UpperCamelCase											=      1_5
       UpperCamelCase											=      SegmentTree(A)
       print(segt.query(4, 6))
       print(segt.query(7, 1_1))
       print(segt.query(7, 1_2))
       segt.update(1, 3, 1_1_1)
       print(segt.query(1, 1_5))
       segt.update(7, 8, 2_3_5)
       segt.show_data()
