



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      UpperCAmelCase_						(     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]   ,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      size if size is not None else {'shortest_edge': 30}
													__SCREAMING_SNAKE_CASE								=      parent
													__SCREAMING_SNAKE_CASE								=      batch_size
													__SCREAMING_SNAKE_CASE								=      num_channels
													__SCREAMING_SNAKE_CASE								=      image_size
													__SCREAMING_SNAKE_CASE								=      min_resolution
													__SCREAMING_SNAKE_CASE								=      max_resolution
													__SCREAMING_SNAKE_CASE								=      do_resize
													__SCREAMING_SNAKE_CASE								=      size
													__SCREAMING_SNAKE_CASE								=      size_divisor
													__SCREAMING_SNAKE_CASE								=      do_normalize
													__SCREAMING_SNAKE_CASE								=      image_mean
													__SCREAMING_SNAKE_CASE								=      image_std






						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,
													    "size_divisor": self.size_divisor,
													}






						def  _A			(					self   ,							_A   ,							_A=False	):







													'''simple docstring'''

													if not batched:
																				__SCREAMING_SNAKE_CASE								=      self.size['shortest_edge']
																				__SCREAMING_SNAKE_CASE								=      image_inputs[0]
																				if isinstance(_A   ,							Image.Image	):
																											__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      image.size
																				else:
																											__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      image.shape[1], image.shape[2]
																				__SCREAMING_SNAKE_CASE								=      size / min(_A   ,							_A	)
																				if h < w:
																											__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      size, scale * w
																				else:
																											__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      scale * h, size

																				__SCREAMING_SNAKE_CASE								=      int((1_333 / 800) * size	)
																				if max(_A   ,							_A	) > max_size:
																											__SCREAMING_SNAKE_CASE								=      max_size / max(_A   ,							_A	)
																											__SCREAMING_SNAKE_CASE								=      newh * scale
																											__SCREAMING_SNAKE_CASE								=      neww * scale

																				__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      int(newh + 0.5	), int(neww + 0.5	)
																				__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      (
																				    newh // self.size_divisor * self.size_divisor,
																				    neww // self.size_divisor * self.size_divisor,
																				)

													else:
																				__SCREAMING_SNAKE_CASE								=      []
																				for image in image_inputs:
																											__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      self.get_expected_values([image]	)
																											expected_values.append((expected_height, expected_width)	)
																				__SCREAMING_SNAKE_CASE								=      max(_A   ,							key=lambda _A	: item[0]	)[0]
																				__SCREAMING_SNAKE_CASE								=      max(_A   ,							key=lambda _A	: item[1]	)[1]

													return expected_height, expected_width







@require_torch
@require_vision
class      UpperCAmelCase_						(     UpperCamelCase_					,					unittest.TestCase ):

						'''simple docstring'''




						UpperCamelCase__    :				Optional[Any]       						=						ViltImageProcessor if is_vision_available() else None






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ViltImageProcessingTester(self	)






						@property
						def  _A			(					self	):







													'''simple docstring'''

													return self.image_processor_tester.prepare_image_processor_dict()






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      self.image_processing_class.from_dict(self.image_processor_dict	)
													self.assertEqual(image_processor.size   ,							{'shortest_edge': 30}	)

													__SCREAMING_SNAKE_CASE								=      self.image_processing_class.from_dict(self.image_processor_dict   ,							size=42	)
													self.assertEqual(image_processor.size   ,							{'shortest_edge': 42}	)






						def  _A			(					self	):







													'''simple docstring'''

													pass






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      self.image_processing_class(**self.image_processor_dict	)
													# create random PIL images
													__SCREAMING_SNAKE_CASE								=      prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=_A	)
													for image in image_inputs:
																				self.assertIsInstance(_A   ,							Image.Image	)

													# Test not batched input
													__SCREAMING_SNAKE_CASE								=      image_processing(image_inputs[0]   ,							return_tensors='pt'	).pixel_values

													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      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
													__SCREAMING_SNAKE_CASE								=      image_processing(_A   ,							return_tensors='pt'	).pixel_values

													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      self.image_processing_class(**self.image_processor_dict	)
													# create random numpy tensors
													__SCREAMING_SNAKE_CASE								=      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
													__SCREAMING_SNAKE_CASE								=      image_processing(image_inputs[0]   ,							return_tensors='pt'	).pixel_values

													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      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
													__SCREAMING_SNAKE_CASE								=      image_processing(_A   ,							return_tensors='pt'	).pixel_values

													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      self.image_processing_class(**self.image_processor_dict	)
													# create random PyTorch tensors
													__SCREAMING_SNAKE_CASE								=      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
													__SCREAMING_SNAKE_CASE								=      image_processing(image_inputs[0]   ,							return_tensors='pt'	).pixel_values

													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      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
													__SCREAMING_SNAKE_CASE								=      image_processing(_A   ,							return_tensors='pt'	).pixel_values

													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      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      UpperCAmelCase_						:

						'''simple docstring'''




						def __init__(					self   ,							_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      value
													__SCREAMING_SNAKE_CASE								=      None
													__SCREAMING_SNAKE_CASE								=      None







class      UpperCAmelCase_						:

						'''simple docstring'''




						UpperCamelCase__    :				int






						def __init__(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      0






						def  _A			(					self   ,							_A   ,							_A	):







													'''simple docstring'''

													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  _A			(					self   ,							_A   ,							_A = None	):







													'''simple docstring'''

													if node is None:
																				return 0
													if target is not None:
																				__SCREAMING_SNAKE_CASE								=      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()






