


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 ViltImageProcessor





class    lowerCAmelCase__				(			unittest.TestCase       ):






						"""simple docstring"""




						def __init__(   self				,							a_				,							a_=7				,							a_=3				,							a_=18				,							a_=30				,							a_=400				,							a_=True				,							a_=None				,							a_=2				,							a_=True				,							a_=[0.5, 0.5, 0.5]				,							a_=[0.5, 0.5, 0.5]				,							):
							lowerCamelCase_			:							Any      =       size if size is not None else {"shortest_edge": 30}
							lowerCamelCase_			:							Optional[Any]      =       parent
							lowerCamelCase_			:							Dict      =       batch_size
							lowerCamelCase_			:							str      =       num_channels
							lowerCamelCase_			:							int      =       image_size
							lowerCamelCase_			:							List[Any]      =       min_resolution
							lowerCamelCase_			:							Union[str, Any]      =       max_resolution
							lowerCamelCase_			:							int      =       do_resize
							lowerCamelCase_			:							Dict      =       size
							lowerCamelCase_			:							int      =       size_divisor
							lowerCamelCase_			:							Any      =       do_normalize
							lowerCamelCase_			:							List[Any]      =       image_mean
							lowerCamelCase_			:							Union[str, Any]      =       image_std




						def 					_UpperCamelCase			(   self     ):
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "size_divisor": self.size_divisor,
							}




						def 					_UpperCamelCase			(   self				,							a_				,							a_=False     ):
							if not batched:
								lowerCamelCase_			:							List[Any]      =       self.size["shortest_edge"]
								lowerCamelCase_			:							Union[str, Any]      =       image_inputs[0]
								if isinstance(a_				,							Image.Image     ):
									lowerCamelCase_   ,lowerCamelCase_			:							Any      =       image.size
								else:
									lowerCamelCase_   ,lowerCamelCase_			:							Optional[Any]      =       image.shape[1], image.shape[2]
								lowerCamelCase_			:							int      =       size / min(a_				,							a_     )
								if h < w:
									lowerCamelCase_   ,lowerCamelCase_			:							Optional[int]      =       size, scale * w
								else:
									lowerCamelCase_   ,lowerCamelCase_			:							List[Any]      =       scale * h, size

								lowerCamelCase_			:							str      =       int((1333 / 800) * size     )
								if max(a_				,							a_     ) > max_size:
									lowerCamelCase_			:							Optional[Any]      =       max_size / max(a_				,							a_     )
									lowerCamelCase_			:							List[Any]      =       newh * scale
									lowerCamelCase_			:							List[str]      =       neww * scale

								lowerCamelCase_   ,lowerCamelCase_			:							Optional[int]      =       int(newh + 0.5     ), int(neww + 0.5     )
								lowerCamelCase_   ,lowerCamelCase_			:							Tuple      =       (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

							else:
								lowerCamelCase_			:							int      =       []
								for image in image_inputs:
									lowerCamelCase_   ,lowerCamelCase_			:							List[str]      =       self.get_expected_values([image]     )
									expected_values.append((expected_height, expected_width)     )
								lowerCamelCase_			:							Optional[Any]      =       max(a_				,							key=lambda a_     : item[0]     )[0]
								lowerCamelCase_			:							Any      =       max(a_				,							key=lambda a_     : item[1]     )[1]

							return expected_height, expected_width





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






						"""simple docstring"""
						__UpperCAmelCase   :		Tuple  					=	ViltImageProcessor if is_vision_available() else None




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Optional[int]      =       ViltImageProcessingTester(self     )




						@property
						def 					_UpperCamelCase			(   self     ):
							return self.image_processor_tester.prepare_image_processor_dict()




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							int      =       self.image_processing_class(**self.image_processor_dict     )
							self.assertTrue(hasattr(a_				,							"image_mean"     )     )
							self.assertTrue(hasattr(a_				,							"image_std"     )     )
							self.assertTrue(hasattr(a_				,							"do_normalize"     )     )
							self.assertTrue(hasattr(a_				,							"do_resize"     )     )
							self.assertTrue(hasattr(a_				,							"size"     )     )
							self.assertTrue(hasattr(a_				,							"size_divisor"     )     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Tuple      =       self.image_processing_class.from_dict(self.image_processor_dict     )
							self.assertEqual(image_processor.size				,							{"shortest_edge": 30}     )

							lowerCamelCase_			:							Tuple      =       self.image_processing_class.from_dict(self.image_processor_dict				,							size=42     )
							self.assertEqual(image_processor.size				,							{"shortest_edge": 42}     )




						def 					_UpperCamelCase			(   self     ):
							pass




						def 					_UpperCamelCase			(   self     ):
							# Initialize image_processing
							lowerCamelCase_			:							str      =       self.image_processing_class(**self.image_processor_dict     )
							# create random PIL images
							lowerCamelCase_			:							Optional[Any]      =       prepare_image_inputs(self.image_processor_tester				,							equal_resolution=a_     )
							for image in image_inputs:
								self.assertIsInstance(a_				,							Image.Image     )

							# Test not batched input
							lowerCamelCase_			:							Union[str, Any]      =       image_processing(image_inputs[0]				,							return_tensors="pt"     ).pixel_values

							lowerCamelCase_   ,lowerCamelCase_			:							Dict      =       self.image_processor_tester.get_expected_values(a_     )
							self.assertEqual(
							    encoded_images.shape				,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,							)

							# Test batched
							lowerCamelCase_			:							Dict      =       image_processing(a_				,							return_tensors="pt"     ).pixel_values

							lowerCamelCase_   ,lowerCamelCase_			:							Union[str, Any]      =       self.image_processor_tester.get_expected_values(a_				,							batched=a_     )
							self.assertEqual(
							    encoded_images.shape				,							(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,							)




						def 					_UpperCamelCase			(   self     ):
							# Initialize image_processing
							lowerCamelCase_			:							List[Any]      =       self.image_processing_class(**self.image_processor_dict     )
							# create random numpy tensors
							lowerCamelCase_			:							List[Any]      =       prepare_image_inputs(self.image_processor_tester				,							equal_resolution=a_				,							numpify=a_     )
							for image in image_inputs:
								self.assertIsInstance(a_				,							np.ndarray     )

							# Test not batched input
							lowerCamelCase_			:							Optional[int]      =       image_processing(image_inputs[0]				,							return_tensors="pt"     ).pixel_values

							lowerCamelCase_   ,lowerCamelCase_			:							Tuple      =       self.image_processor_tester.get_expected_values(a_     )
							self.assertEqual(
							    encoded_images.shape				,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,							)

							# Test batched
							lowerCamelCase_			:							Union[str, Any]      =       image_processing(a_				,							return_tensors="pt"     ).pixel_values

							lowerCamelCase_   ,lowerCamelCase_			:							List[Any]      =       self.image_processor_tester.get_expected_values(a_				,							batched=a_     )
							self.assertEqual(
							    encoded_images.shape				,							(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,							)




						def 					_UpperCamelCase			(   self     ):
							# Initialize image_processing
							lowerCamelCase_			:							Optional[int]      =       self.image_processing_class(**self.image_processor_dict     )
							# create random PyTorch tensors
							lowerCamelCase_			:							Union[str, Any]      =       prepare_image_inputs(self.image_processor_tester				,							equal_resolution=a_				,							torchify=a_     )
							for image in image_inputs:
								self.assertIsInstance(a_				,							torch.Tensor     )

							# Test not batched input
							lowerCamelCase_			:							int      =       image_processing(image_inputs[0]				,							return_tensors="pt"     ).pixel_values

							lowerCamelCase_   ,lowerCamelCase_			:							List[str]      =       self.image_processor_tester.get_expected_values(a_     )
							self.assertEqual(
							    encoded_images.shape				,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,							)

							# Test batched
							lowerCamelCase_			:							Optional[Any]      =       image_processing(a_				,							return_tensors="pt"     ).pixel_values

							lowerCamelCase_   ,lowerCamelCase_			:							Union[str, Any]      =       self.image_processor_tester.get_expected_values(a_				,							batched=a_     )
							self.assertEqual(
							    encoded_images.shape				,							(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,							)





from __future__ import annotations





class    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self				,							a_     ):
							lowerCamelCase_			:							List[str]      =       value
							lowerCamelCase_			:							Node | None      =       None
							lowerCamelCase_			:							Node | None      =       None





class    lowerCAmelCase__				:






						"""simple docstring"""
						__UpperCAmelCase   :		int




						def __init__(   self     ):
							lowerCamelCase_			:							str      =       0




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							if node is None:
								return

							if path_sum == self.target:
								self.paths += 1

							if node.left:
								self.depth_first_search(node.left				,							path_sum + node.left.value     )
							if node.right:
								self.depth_first_search(node.right				,							path_sum + node.right.value     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = None     ):
							if node is None:
								return 0
							if target is not None:
								lowerCamelCase_			:							List[str]      =       target

							self.depth_first_search(a_				,							node.value     )
							self.path_sum(node.left     )
							self.path_sum(node.right     )

							return self.paths


if __name__ == "__main__":
							import doctest

							doctest.testmod()

