code: 
  
  
  
  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,
  													    )   ,							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  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()
  
  
  
  
  
  
  