




'''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 SwinaSRImageProcessor
						from transformers.image_transforms import get_image_size





class 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					def __init__(       self,    __UpperCamelCase,    __UpperCamelCase=7,    __UpperCamelCase=3,    __UpperCamelCase=18,    __UpperCamelCase=30,    __UpperCamelCase=400,    __UpperCamelCase=True,    __UpperCamelCase=1 / 255,    __UpperCamelCase=True,    __UpperCamelCase=8,    ):
							_snake_case      			=			parent
							_snake_case      			=			batch_size
							_snake_case      			=			num_channels
							_snake_case      			=			image_size
							_snake_case      			=			min_resolution
							_snake_case      			=			max_resolution
							_snake_case      			=			do_rescale
							_snake_case      			=			rescale_factor
							_snake_case      			=			do_pad
							_snake_case      			=			pad_size






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase=False,    __UpperCamelCase=False,    __UpperCamelCase=False					):
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
									_snake_case      			=			[]
									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      			=			[]
									for i in range(self.batch_size					):
											_snake_case			,       _snake_case      			=			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      			=			[Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE,    0,    -1					)					) for x in image_inputs]

							if torchify:
									_snake_case      			=			[torch.from_numpy(_SCREAMING_SNAKE_CASE					) for x in image_inputs]

							return image_inputs





@require_torch
@require_vision
class 	lowerCamelCase_				(			lowerCAmelCase_,			unittest.TestCase				):




					'''simple docstring'''






					lowercase__       =					SwinaSRImageProcessor if is_vision_available() else None






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			SwinaSRImageProcessingTester(self					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self.image_processor_tester.prepare_image_processor_dict()






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE,    "do_rescale"					)					)
							self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE,    "rescale_factor"					)					)
							self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE,    "do_pad"					)					)
							self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE,    "pad_size"					)					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_image_size(_SCREAMING_SNAKE_CASE					)
							_snake_case      			=			self.image_processor_tester.pad_size

							_snake_case      			=			(old_height // size + 1) * size - old_height
							_snake_case      			=			(old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							_snake_case      			=			self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE					)
							for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE,    Image.Image					)

							# Test not batched input
							_snake_case      			=			image_processor(image_inputs[0],    return_tensors="pt"					).pixel_values
							_snake_case			,       _snake_case      			=			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 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							_snake_case      			=			self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE,    numpify=_SCREAMING_SNAKE_CASE					)
							for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE,    np.ndarray					)

							# Test not batched input
							_snake_case      			=			image_processor(image_inputs[0],    return_tensors="pt"					).pixel_values
							_snake_case			,       _snake_case      			=			self.calculate_expected_size(image_inputs[0]					)
							self.assertEqual(
							    encoded_images.shape,    (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ),    )






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							_snake_case      			=			self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE,    torchify=_SCREAMING_SNAKE_CASE					)
							for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE,    torch.Tensor					)

							# Test not batched input
							_snake_case      			=			image_processor(image_inputs[0],    return_tensors="pt"					).pixel_values
							_snake_case			,       _snake_case      			=			self.calculate_expected_size(image_inputs[0]					)
							self.assertEqual(
							    encoded_images.shape,    (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ),    )












import warnings

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


_UpperCAmelCase										=					logging.get_logger(__name__)



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def __init__(		self:			Tuple       , *_SCREAMING_SNAKE_CASE:			List[Any]       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			None:




              """simple docstring"""


              warnings.warn(
                  "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
                  " Please use ConvNextImageProcessor instead."       , _SCREAMING_SNAKE_CASE       , )
              super().__init__(*_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

