
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=4_00  , A=True  , A=None  , A=2  , A=True  , A=[0.5, 0.5, 0.5]  , A=[0.5, 0.5, 0.5]  , )  ->int:
									UpperCAmelCase__		:Optional[Any] 		=    size if size is not None else {'shortest_edge': 30}
									UpperCAmelCase__		:Optional[int] 		=    parent
									UpperCAmelCase__		:Optional[Any] 		=    batch_size
									UpperCAmelCase__		:Optional[Any] 		=    num_channels
									UpperCAmelCase__		:Union[str, Any] 		=    image_size
									UpperCAmelCase__		:List[str] 		=    min_resolution
									UpperCAmelCase__		:Optional[int] 		=    max_resolution
									UpperCAmelCase__		:Optional[Any] 		=    do_resize
									UpperCAmelCase__		:Tuple 		=    size
									UpperCAmelCase__		:Any 		=    size_divisor
									UpperCAmelCase__		:List[Any] 		=    do_normalize
									UpperCAmelCase__		:str 		=    image_mean
									UpperCAmelCase__		:Dict 		=    image_std


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

												UpperCAmelCase__		:Optional[Any] 		=    int((13_33 / 8_00) * size	)
												if max(A  , A	) > max_size:
															UpperCAmelCase__		:int 		=    max_size / max(A  , A	)
															UpperCAmelCase__		:List[Any] 		=    newh * scale
															UpperCAmelCase__		:Union[str, Any] 		=    neww * scale

												UpperCAmelCase__		,       UpperCAmelCase__		:Any 		=    int(newh + 0.5	), int(neww + 0.5	)
												UpperCAmelCase__		,       UpperCAmelCase__		:List[str] 		=    (
												    newh // self.size_divisor * self.size_divisor,
												    neww // self.size_divisor * self.size_divisor,
												)

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

									return expected_height, expected_width


@require_torch
@require_vision
class 	UpperCamelCase__       (  UpperCAmelCase__						,			unittest.TestCase):

						'''simple docstring'''



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


						def    A__       (  self	)  ->Dict:
									UpperCAmelCase__		:Tuple 		=    ViltImageProcessingTester(self	)


						@property
						def    A__       (  self	)  ->Dict:
									return self.image_processor_tester.prepare_image_processor_dict()


						def    A__       (  self	)  ->Dict:
									UpperCAmelCase__		:Union[str, Any] 		=    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	)  ->List[Any]:
									UpperCAmelCase__		:Union[str, Any] 		=    self.image_processing_class.from_dict(self.image_processor_dict	)
									self.assertEqual(image_processor.size  , {'shortest_edge': 30}	)

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


						def    A__       (  self	)  ->List[Any]:
									pass


						def    A__       (  self	)  ->Dict:
									# Initialize image_processing
									UpperCAmelCase__		:str 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									UpperCAmelCase__		:Union[str, 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
									UpperCAmelCase__		:Dict 		=    image_processing(image_inputs[0]  , return_tensors='pt'	).pixel_values

									UpperCAmelCase__		,       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[str] 		=    image_processing(A  , return_tensors='pt'	).pixel_values

									UpperCAmelCase__		,       UpperCAmelCase__		: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    A__       (  self	)  ->Union[str, Any]:
									# Initialize image_processing
									UpperCAmelCase__		:List[str] 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									UpperCAmelCase__		:Tuple 		=    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__		:Dict 		=    image_processing(image_inputs[0]  , return_tensors='pt'	).pixel_values

									UpperCAmelCase__		,       UpperCAmelCase__		:List[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__		:Optional[int] 		=    image_processing(A  , return_tensors='pt'	).pixel_values

									UpperCAmelCase__		,       UpperCAmelCase__		:str 		=    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]:
									# Initialize image_processing
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									UpperCAmelCase__		:Optional[int] 		=    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__		,       UpperCAmelCase__		: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
									UpperCAmelCase__		:Optional[int] 		=    image_processing(A  , return_tensors='pt'	).pixel_values

									UpperCAmelCase__		,       UpperCAmelCase__		: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,
									    )  , )







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 DeiTImageProcessor


class 	UpperCamelCase__       (  unittest.TestCase):

						'''simple docstring'''



						def __init__(  self  , A  , A=7  , A=3  , A=18  , A=30  , A=4_00  , A=True  , A=None  , A=True  , A=None  , A=True  , A=[0.5, 0.5, 0.5]  , A=[0.5, 0.5, 0.5]  , )  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    size if size is not None else {'height': 20, 'width': 20}
									UpperCAmelCase__		:str 		=    crop_size if crop_size is not None else {'height': 18, 'width': 18}

									UpperCAmelCase__		:Dict 		=    parent
									UpperCAmelCase__		:Optional[Any] 		=    batch_size
									UpperCAmelCase__		:Optional[Any] 		=    num_channels
									UpperCAmelCase__		:str 		=    image_size
									UpperCAmelCase__		:Optional[Any] 		=    min_resolution
									UpperCAmelCase__		:Optional[int] 		=    max_resolution
									UpperCAmelCase__		:Dict 		=    do_resize
									UpperCAmelCase__		:Any 		=    size
									UpperCAmelCase__		:Any 		=    do_center_crop
									UpperCAmelCase__		:Optional[int] 		=    crop_size
									UpperCAmelCase__		:Dict 		=    do_normalize
									UpperCAmelCase__		:List[Any] 		=    image_mean
									UpperCAmelCase__		:Tuple 		=    image_std


						def    A__       (  self	)  ->str:
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}


@require_torch
@require_vision
class 	UpperCamelCase__       (  UpperCAmelCase__						,			unittest.TestCase):

						'''simple docstring'''



						__a     :    Tuple												=			DeiTImageProcessor if is_vision_available() else None
						__a     :    Tuple												=			True


						def    A__       (  self	)  ->Optional[int]:
									UpperCAmelCase__		:int 		=    DeiTImageProcessingTester(self	)


						@property
						def    A__       (  self	)  ->List[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()


						def    A__       (  self	)  ->Optional[Any]:
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									self.assertTrue(hasattr(A  , 'do_resize'	)	)
									self.assertTrue(hasattr(A  , 'size'	)	)
									self.assertTrue(hasattr(A  , 'do_center_crop'	)	)
									self.assertTrue(hasattr(A  , 'center_crop'	)	)
									self.assertTrue(hasattr(A  , 'do_normalize'	)	)
									self.assertTrue(hasattr(A  , 'image_mean'	)	)
									self.assertTrue(hasattr(A  , 'image_std'	)	)


						def    A__       (  self	)  ->Tuple:
									UpperCAmelCase__		:Union[str, Any] 		=    self.image_processing_class.from_dict(self.image_processor_dict	)
									self.assertEqual(image_processor.size  , {'height': 20, 'width': 20}	)
									self.assertEqual(image_processor.crop_size  , {'height': 18, 'width': 18}	)

									UpperCAmelCase__		:List[Any] 		=    self.image_processing_class.from_dict(self.image_processor_dict  , size=42  , crop_size=84	)
									self.assertEqual(image_processor.size  , {'height': 42, 'width': 42}	)
									self.assertEqual(image_processor.crop_size  , {'height': 84, 'width': 84}	)


						def    A__       (  self	)  ->List[str]:
									pass


						def    A__       (  self	)  ->List[str]:
									# Initialize image_processing
									UpperCAmelCase__		:int 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									UpperCAmelCase__		:Tuple 		=    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__		:int 		=    image_processing(image_inputs[0]  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )

									# Test batched
									UpperCAmelCase__		:List[str] 		=    image_processing(A  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )


						def    A__       (  self	)  ->str:
									# Initialize image_processing
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									UpperCAmelCase__		:Dict 		=    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__		:List[str] 		=    image_processing(image_inputs[0]  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )

									# Test batched
									UpperCAmelCase__		:Optional[Any] 		=    image_processing(A  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )


						def    A__       (  self	)  ->Tuple:
									# Initialize image_processing
									UpperCAmelCase__		:List[Any] 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									UpperCAmelCase__		:str 		=    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__		:List[str] 		=    image_processing(image_inputs[0]  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )

									# Test batched
									UpperCAmelCase__		:Optional[int] 		=    image_processing(A  , return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape  , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )





