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 SwinaSRImageProcessor
				from transformers.image_transforms import get_image_size





class 					__snake_case    (     unittest.TestCase      ):




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase=7       ,			__UpperCamelCase=3       ,			__UpperCamelCase=18       ,			__UpperCamelCase=30       ,			__UpperCamelCase=400       ,			__UpperCamelCase=True       ,			__UpperCamelCase=1 / 255       ,			__UpperCamelCase=True       ,			__UpperCamelCase=8       ,			)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= parent
									snake_case__   :      Dict   				= batch_size
									snake_case__   :      Dict   				= num_channels
									snake_case__   :      str   				= image_size
									snake_case__   :      str   				= min_resolution
									snake_case__   :      int   				= max_resolution
									snake_case__   :      str   				= do_rescale
									snake_case__   :      Any   				= rescale_factor
									snake_case__   :      Optional[int]   				= do_pad
									snake_case__   :      str   				= pad_size




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									return {
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									    "pad_size": self.pad_size,
									}




					def      __a     (  self       ,			__UpperCamelCase=False       ,			__UpperCamelCase=False       ,			__UpperCamelCase=False						)	->    Tuple:



									'''simple docstring'''







									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
													snake_case__   :      Dict   				= []
													for i in range(self.batch_size						):
																	image_inputs.append(
																	    np.random.randint(
																	        255       ,			size=(self.num_channels, self.max_resolution, self.max_resolution)       ,			dtype=np.uinta						)						)
									else:
													snake_case__   :      str   				= []
													for i in range(self.batch_size						):
																	snake_case__				,    snake_case__   :      Optional[int]   				= np.random.choice(np.arange(self.min_resolution       ,			self.max_resolution						)       ,			2						)
																	image_inputs.append(np.random.randint(255       ,			size=(self.num_channels, width, height)       ,			dtype=np.uinta						)						)

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

									if torchify:
													snake_case__   :      Optional[int]   				= [torch.from_numpy(_lowercase						) for x in image_inputs]

									return image_inputs





@require_torch
@require_vision
class 					__snake_case    (     lowercase__       ,unittest.TestCase      ):
					__lowerCamelCase			   =  SwinaSRImageProcessor if is_vision_available() else None




					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= SwinaSRImageProcessingTester(self						)




					@property
					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									return self.image_processor_tester.prepare_image_processor_dict()




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      Dict   				= self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_lowercase       ,			'do_rescale'						)						)
									self.assertTrue(hasattr(_lowercase       ,			'rescale_factor'						)						)
									self.assertTrue(hasattr(_lowercase       ,			'do_pad'						)						)
									self.assertTrue(hasattr(_lowercase       ,			'pad_size'						)						)




					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									pass




					def      __a     (  self       ,			__UpperCamelCase						)	->    int:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Optional[Any]   				= get_image_size(_lowercase						)
									snake_case__   :      Tuple   				= self.image_processor_tester.pad_size

									snake_case__   :      Optional[int]   				= (old_height // size + 1) * size - old_height
									snake_case__   :      Union[str, Any]   				= (old_width // size + 1) * size - old_width
									return old_height + pad_height, old_width + pad_width




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									snake_case__   :      int   				= self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase						)
									for image in image_inputs:
													self.assertIsInstance(_lowercase       ,			Image.Image						)

									# Test not batched input
									snake_case__   :      Union[str, Any]   				= image_processor(image_inputs[0]       ,			return_tensors='pt'						).pixel_values
									snake_case__				,    snake_case__   :      List[Any]   				= self.calculate_expected_size(np.array(image_inputs[0]						)						)
									self.assertEqual(
									    encoded_images.shape       ,			(
									        1,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,			)




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									snake_case__   :      Optional[int]   				= self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase       ,			numpify=_lowercase						)
									for image in image_inputs:
													self.assertIsInstance(_lowercase       ,			np.ndarray						)

									# Test not batched input
									snake_case__   :      str   				= image_processor(image_inputs[0]       ,			return_tensors='pt'						).pixel_values
									snake_case__				,    snake_case__   :      List[Any]   				= self.calculate_expected_size(image_inputs[0]						)
									self.assertEqual(
									    encoded_images.shape       ,			(
									        1,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,			)




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									snake_case__   :      Dict   				= self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									snake_case__   :      Tuple   				= self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase       ,			torchify=_lowercase						)
									for image in image_inputs:
													self.assertIsInstance(_lowercase       ,			torch.Tensor						)

									# Test not batched input
									snake_case__   :      Tuple   				= image_processor(image_inputs[0]       ,			return_tensors='pt'						).pixel_values
									snake_case__				,    snake_case__   :      Optional[int]   				= self.calculate_expected_size(image_inputs[0]						)
									self.assertEqual(
									    encoded_images.shape       ,			(
									        1,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,			)



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__UpperCamelCase       :    str         =		logging.get_logger(__name__)
class   UpperCAmelCase_	(				lowercase__   ):
    def __init__(	self			:       List[Any]   ,						*_lowercase			:       Optional[Any]   ,						**_lowercase			:       str		)						->    None:
       warnings.warn(
           "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use ConvNextImageProcessor instead."   ,						_lowercase   ,						)
       super().__init__(*_lowercase   ,						**_lowercase		)