



'''simple docstring'''






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     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







						"""simple docstring"""






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







						def 					A     (						self      :					List[Any]		)      ->		Tuple:
													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      :					str		,      __snake_case      :					Union[str, Any]		,      __snake_case      :					Any=False		)      ->		Union[str, Any]:
													if not batched:
																				UpperCAmelCase			:	List[str]										=		self.size['shortest_edge']
																				UpperCAmelCase			:	int										=		image_inputs[0]
																				if isinstance(_A		,      Image.Image		):
																											UpperCAmelCase			:	List[Any]										=		image.size
																				else:
																											UpperCAmelCase			:	Dict										=		image.shape[1], image.shape[2]
																				UpperCAmelCase			:	Dict										=		size / min(_A		,      _A		)
																				if h < w:
																											UpperCAmelCase			:	str										=		size, scale * w
																				else:
																											UpperCAmelCase			:	str										=		scale * h, size

																				UpperCAmelCase			:	Tuple										=		int((1333 / 800) * size		)
																				if max(_A		,      _A		) > max_size:
																											UpperCAmelCase			:	Optional[int]										=		max_size / max(_A		,      _A		)
																											UpperCAmelCase			:	Any										=		newh * scale
																											UpperCAmelCase			:	List[Any]										=		neww * scale

																				UpperCAmelCase			:	Dict										=		int(newh + 0.5		), int(neww + 0.5		)
																				UpperCAmelCase			:	Any										=		(
																				    newh // self.size_divisor * self.size_divisor,
																				    neww // self.size_divisor * self.size_divisor,
																				)

													else:
																				UpperCAmelCase			:	Optional[Any]										=		[]
																				for image in image_inputs:
																											UpperCAmelCase			:	Tuple										=		self.get_expected_values([image]		)
																											expected_values.append((expected_height, expected_width)		)
																				UpperCAmelCase			:	List[Any]										=		max(_A		,      key=lambda __snake_case		: item[0]		)[0]
																				UpperCAmelCase			:	Dict										=		max(_A		,      key=lambda __snake_case		: item[1]		)[1]

													return expected_height, expected_width

@require_torch
@require_vision
class     SCREAMING_SNAKE_CASE(							snake_case__							,		unittest.TestCase      ):







						"""simple docstring"""






						lowerCamelCase__      			=	ViltImageProcessor if is_vision_available() else None







						def 					A     (						self      :					Dict		)      ->		Optional[Any]:
													UpperCAmelCase			:	Union[str, Any]										=		ViltImageProcessingTester(self		)







						@property
						def 					A     (						self      :					Any		)      ->		Optional[int]:
													return self.image_processor_tester.prepare_image_processor_dict()







						def 					A     (						self      :					Tuple		)      ->		List[str]:
													UpperCAmelCase			:	Optional[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 					A     (						self      :					str		)      ->		Tuple:
													UpperCAmelCase			:	Tuple										=		self.image_processing_class.from_dict(self.image_processor_dict		)
													self.assertEqual(image_processor.size		,      {'''shortest_edge''': 30}		)

													UpperCAmelCase			:	Optional[Any]										=		self.image_processing_class.from_dict(self.image_processor_dict		,      size=42		)
													self.assertEqual(image_processor.size		,      {'''shortest_edge''': 42}		)







						def 					A     (						self      :					int		)      ->		str:
													pass







						def 					A     (						self      :					int		)      ->		List[Any]:
													# Initialize image_processing
													UpperCAmelCase			:	int										=		self.image_processing_class(**self.image_processor_dict		)
													# create random PIL images
													UpperCAmelCase			:	int										=		prepare_image_inputs(self.image_processor_tester		,      equal_resolution=_A		)
													for image in image_inputs:
																				self.assertIsInstance(_A		,      Image.Image		)

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

													UpperCAmelCase			:	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
													UpperCAmelCase			:	Any										=		image_processing(_A		,      return_tensors='''pt'''		).pixel_values

													UpperCAmelCase			:	Optional[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 					A     (						self      :					List[Any]		)      ->		List[str]:
													# Initialize image_processing
													UpperCAmelCase			:	List[str]										=		self.image_processing_class(**self.image_processor_dict		)
													# create random numpy tensors
													UpperCAmelCase			:	int										=		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
													UpperCAmelCase			:	int										=		image_processing(image_inputs[0]		,      return_tensors='''pt'''		).pixel_values

													UpperCAmelCase			:	Any										=		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
													UpperCAmelCase			:	List[Any]										=		image_processing(_A		,      return_tensors='''pt'''		).pixel_values

													UpperCAmelCase			:	int										=		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      :					int		)      ->		str:
													# Initialize image_processing
													UpperCAmelCase			:	Optional[int]										=		self.image_processing_class(**self.image_processor_dict		)
													# create random PyTorch tensors
													UpperCAmelCase			:	Dict										=		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
													UpperCAmelCase			:	str										=		image_processing(image_inputs[0]		,      return_tensors='''pt'''		).pixel_values

													UpperCAmelCase			:	int										=		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
													UpperCAmelCase			:	List[Any]										=		image_processing(_A		,      return_tensors='''pt'''		).pixel_values

													UpperCAmelCase			:	Optional[int]										=		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,
													    )		,      )










'''simple docstring'''






UpperCamelCase__:       str							     =      [0.485, 0.456, 0.406]
UpperCamelCase__:       List[str]							     =      [0.229, 0.224, 0.225]
UpperCamelCase__:       List[str]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       Optional[Any]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       List[str]							     =      [0.48145466, 0.4578275, 0.40821073]
UpperCamelCase__:       List[str]							     =      [0.26862954, 0.26130258, 0.27577711]





