"""simple docstring"""




class    a_   :




					def __init__(						self    :  Dict	,    snake_case__    :  int  ):
							lowerCAmelCase__   = size
							lowerCAmelCase__   = [0] * size
							lowerCAmelCase__   = [0] * size




					@staticmethod
					def     _SCREAMING_SNAKE_CASE    (						snake_case__    :  int  ):
							return index | (index + 1)




					@staticmethod
					def     _SCREAMING_SNAKE_CASE    (						snake_case__    :  int  ):
							return (index & (index + 1)) - 1




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  int	,    snake_case__    :  int  ):
							lowerCAmelCase__   = value
							while index < self.size:
									lowerCAmelCase__   = self.get_prev(snake_case__  ) + 1
									if current_left_border == index:
											lowerCAmelCase__   = value
									else:
											lowerCAmelCase__   = max(snake_case__	,    snake_case__	,    snake_case__  )
									lowerCAmelCase__   = self.get_next(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]	,    snake_case__    :  int	,    snake_case__    :  int  ):
							right -= 1  # Because of right is exclusive
							lowerCAmelCase__   = 0
							while left <= right:
									lowerCAmelCase__   = self.get_prev(snake_case__  )
									if left <= current_left:
											lowerCAmelCase__   = max(snake_case__	,    self.tree[right]  )
											lowerCAmelCase__   = current_left
									else:
											lowerCAmelCase__   = max(snake_case__	,    self.arr[right]  )
											right -= 1
							return result


if __name__ == "__main__":
			import doctest

			doctest.testmod()





"""simple docstring"""




import unittest

import numpy as np
import requests

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

			from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
			__lowerCAmelCase     :				int    				=    False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor

class    a_   (  unittest.TestCase      ):




					def __init__(						self    :  Tuple	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=7	,    snake_case__    :  Optional[int]=3	,    snake_case__    :  int=18	,    snake_case__    :  Dict=30	,    snake_case__    :  Optional[Any]=400	,    snake_case__    :  Tuple=None	,    snake_case__    :  List[Any]=True	,    snake_case__    :  Any=True	,    snake_case__    :  Dict=None	,    ):
							lowerCAmelCase__   = size if size is not None else {"""height""": 20, """width""": 20}
							lowerCAmelCase__   = parent
							lowerCAmelCase__   = batch_size
							lowerCAmelCase__   = num_channels
							lowerCAmelCase__   = image_size
							lowerCAmelCase__   = min_resolution
							lowerCAmelCase__   = max_resolution
							lowerCAmelCase__   = size
							lowerCAmelCase__   = do_normalize
							lowerCAmelCase__   = do_convert_rgb
							lowerCAmelCase__   = [512, 1024, 2048, 4096]
							lowerCAmelCase__   = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
							lowerCAmelCase__   = Image.open(requests.get(snake_case__	,    stream=snake_case__  ).raw  ).convert("""RGB"""  )
							return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Optional[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.image_processor_tester.prepare_dummy_image()

							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							lowerCAmelCase__   = 2048

							lowerCAmelCase__   = image_processor(snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  )
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()	,    torch.tensor(0.0606  )	,    atol=1E-3	,    rtol=1E-3  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = 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__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = 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__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							lowerCAmelCase__   = True

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(snake_case__  ):
											lowerCAmelCase__   = image_processor(
											    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches

									lowerCAmelCase__   = """Hello"""

									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowerCAmelCase__   = 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  )

							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowerCAmelCase__   = 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__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						List[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self	,    num_channels=4  )
							lowerCAmelCase__   = 3




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = 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__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




