



"""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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import CLIPImageProcessor







class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						def __init__(   self					:Union[str, Any]  ,    lowercase_					:Optional[Any]  ,    lowercase_					:List[str]=7  ,    lowercase_					:Optional[Any]=3  ,    lowercase_					:int=18  ,    lowercase_					:List[Any]=30  ,    lowercase_					:str=4_00  ,    lowercase_					:Union[str, Any]=True  ,    lowercase_					:Union[str, Any]=None  ,    lowercase_					:Union[str, Any]=True  ,    lowercase_					:Optional[int]=None  ,    lowercase_					:List[str]=True  ,    lowercase_					:Union[str, Any]=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,    lowercase_					:int=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,    lowercase_					:Optional[int]=True  ,    )							->     List[str]:
												UpperCAmelCase			 =					size if size is not None else {"shortest_edge": 20}
												UpperCAmelCase			 =					crop_size if crop_size is not None else {"height": 18, "width": 18}
												UpperCAmelCase			 =					parent
												UpperCAmelCase			 =					batch_size
												UpperCAmelCase			 =					num_channels
												UpperCAmelCase			 =					image_size
												UpperCAmelCase			 =					min_resolution
												UpperCAmelCase			 =					max_resolution
												UpperCAmelCase			 =					do_resize
												UpperCAmelCase			 =					size
												UpperCAmelCase			 =					do_center_crop
												UpperCAmelCase			 =					crop_size
												UpperCAmelCase			 =					do_normalize
												UpperCAmelCase			 =					image_mean
												UpperCAmelCase			 =					image_std
												UpperCAmelCase			 =					do_convert_rgb






						def       UpperCAmelCase__	(   self					:Any   )							->     List[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,
												    "do_convert_rgb": self.do_convert_rgb,
												}






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:List[Any]=False  ,    lowercase_					:Optional[Any]=False  ,    lowercase_					:Tuple=False   )							->     Any:
												assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

												if equal_resolution:
																		UpperCAmelCase			 =					[]
																		for i in range(self.batch_size   ):
																								image_inputs.append(
																								    np.random.randint(
																								        2_55  ,    size=(self.num_channels, self.max_resolution, self.max_resolution)  ,    dtype=np.uinta   )   )
												else:
																		UpperCAmelCase			 =					[]
																		for i in range(self.batch_size   ):
																								UpperCAmelCase			 =					np.random.choice(np.arange(self.min_resolution  ,    self.max_resolution   )  ,    2   )
																								image_inputs.append(np.random.randint(2_55  ,    size=(self.num_channels, width, height)  ,    dtype=np.uinta   )   )

												if not numpify and not torchify:
																		# PIL expects the channel dimension as last dimension
																		UpperCAmelCase			 =					[Image.fromarray(np.moveaxis(A_  ,    0  ,    -1   )   ) for x in image_inputs]

												if torchify:
																		UpperCAmelCase			 =					[torch.from_numpy(A_   ) for x in image_inputs]

												return image_inputs







@require_torch
@require_vision
class  A_     (	SCREAMING_SNAKE_CASE_      ,  unittest.TestCase							):



						"""simple docstring"""
						__UpperCamelCase													=				CLIPImageProcessor if is_vision_available() else None






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Any:
												UpperCAmelCase			 =					CLIPImageProcessingTester(self   )






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






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     List[Any]:
												UpperCAmelCase			 =					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'   )   )
												self.assertTrue(hasattr(A_  ,    'do_convert_rgb'   )   )






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Optional[Any]:
												UpperCAmelCase			 =					self.image_processing_class.from_dict(self.image_processor_dict   )
												self.assertEqual(image_processor.size  ,    {'shortest_edge': 20}   )
												self.assertEqual(image_processor.crop_size  ,    {'height': 18, 'width': 18}   )

												UpperCAmelCase			 =					self.image_processing_class.from_dict(self.image_processor_dict  ,    size=42  ,    crop_size=84   )
												self.assertEqual(image_processor.size  ,    {'shortest_edge': 42}   )
												self.assertEqual(image_processor.crop_size  ,    {'height': 84, 'width': 84}   )






						def       UpperCAmelCase__	(   self					:Dict   )							->     Dict:
												pass






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     List[Any]:
												UpperCAmelCase			 =					self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												UpperCAmelCase			 =					self.image_processor_tester.prepare_inputs(equal_resolution=A_   )
												for image in image_inputs:
																		self.assertIsInstance(A_  ,    Image.Image   )

												# Test not batched input
												UpperCAmelCase			 =					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			 =					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       UpperCAmelCase__	(   self					:List[Any]   )							->     Optional[int]:
												UpperCAmelCase			 =					self.image_processing_class(**self.image_processor_dict   )
												# create random numpy tensors
												UpperCAmelCase			 =					self.image_processor_tester.prepare_inputs(equal_resolution=A_  ,    numpify=A_   )
												for image in image_inputs:
																		self.assertIsInstance(A_  ,    np.ndarray   )

												# Test not batched input
												UpperCAmelCase			 =					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			 =					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       UpperCAmelCase__	(   self					:Tuple   )							->     Dict:
												UpperCAmelCase			 =					self.image_processing_class(**self.image_processor_dict   )
												# create random PyTorch tensors
												UpperCAmelCase			 =					self.image_processor_tester.prepare_inputs(equal_resolution=A_  ,    torchify=A_   )
												for image in image_inputs:
																		self.assertIsInstance(A_  ,    torch.Tensor   )

												# Test not batched input
												UpperCAmelCase			 =					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			 =					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'],
												    )  ,    )







@require_torch
@require_vision
class  A_     (	SCREAMING_SNAKE_CASE_      ,  unittest.TestCase							):



						"""simple docstring"""
						__UpperCamelCase													=				CLIPImageProcessor if is_vision_available() else None






						def       UpperCAmelCase__	(   self					:Tuple   )							->     Union[str, Any]:
												UpperCAmelCase			 =					CLIPImageProcessingTester(self  ,    num_channels=4   )
												UpperCAmelCase			 =					3






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






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Optional[Any]:
												UpperCAmelCase			 =					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'   )   )
												self.assertTrue(hasattr(A_  ,    'do_convert_rgb'   )   )






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     str:
												pass






						def       UpperCAmelCase__	(   self					:Any   )							->     Tuple:
												UpperCAmelCase			 =					self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												UpperCAmelCase			 =					self.image_processor_tester.prepare_inputs(equal_resolution=A_   )
												for image in image_inputs:
																		self.assertIsInstance(A_  ,    Image.Image   )

												# Test not batched input
												UpperCAmelCase			 =					image_processing(image_inputs[0]  ,    return_tensors='pt'   ).pixel_values
												self.assertEqual(
												    encoded_images.shape  ,    (
												        1,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )  ,    )

												# Test batched
												UpperCAmelCase			 =					image_processing(A_  ,    return_tensors='pt'   ).pixel_values
												self.assertEqual(
												    encoded_images.shape  ,    (
												        self.image_processor_tester.batch_size,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )  ,    )








import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=256					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     min(img.size					)

     if s < target_image_size:
          raise ValueError(f'''min dim for image {s} < {target_image_size}'''					)

     _lowerCAmelCase :       Dict	   =     target_image_size / s
     _lowerCAmelCase :       Dict	   =     (round(r * img.size[1]					), round(r * img.size[0]					))
     _lowerCAmelCase :       Any	   =     TF.resize(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				interpolation=PIL.Image.LANCZOS					)
     _lowerCAmelCase :       str	   =     TF.center_crop(SCREAMING_SNAKE_CASE		,				output_size=2 * [target_image_size]					)
     _lowerCAmelCase :       int	   =     torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE					)		,				0					)
     return img







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     2.0 * x - 1.0
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE="RGB"					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Optional[int]	   =     x.detach().cpu()
     if process:
          _lowerCAmelCase :       List[Any]	   =     post_process_tensor(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Optional[int]	   =     x.numpy()
     if process:
          _lowerCAmelCase :       List[Any]	   =     (255 * x).astype(np.uinta					)
     _lowerCAmelCase :       Optional[int]	   =     Image.fromarray(SCREAMING_SNAKE_CASE					)
     if not x.mode == mode:
          _lowerCAmelCase :       str	   =     x.convert(SCREAMING_SNAKE_CASE					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     torch.clamp(SCREAMING_SNAKE_CASE		,				-1.0		,				1.0					)
     _lowerCAmelCase :       Any	   =     (x + 1.0) / 2.0
     _lowerCAmelCase :       List[Any]	   =     x.permute(1		,				2		,				0					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     post_process_tensor(x.squeeze()					)
     return x.permute(2		,				0		,				1					).unsqueeze(0					)






