



"""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 ConvNextImageProcessor
class 						lowerCAmelCase__	(  unittest.TestCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=7    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=3    ,					_SCREAMING_SNAKE_CASE							:					List[str]=18    ,					_SCREAMING_SNAKE_CASE							:					str=30    ,					_SCREAMING_SNAKE_CASE							:					str=400    ,					_SCREAMING_SNAKE_CASE							:					Any=True    ,					_SCREAMING_SNAKE_CASE							:					int=None    ,					_SCREAMING_SNAKE_CASE							:					Dict=0.8_7_5    ,					_SCREAMING_SNAKE_CASE							:					List[str]=True    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=[0.5, 0.5, 0.5]    ,					_SCREAMING_SNAKE_CASE							:					str=[0.5, 0.5, 0.5]    ,					)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    size if size is not None else {'shortest_edge': 20}
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    parent
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    batch_size
					SCREAMING_SNAKE_CASE	:       Any 							=    num_channels
					SCREAMING_SNAKE_CASE	:       Any 							=    image_size
					SCREAMING_SNAKE_CASE	:       str 							=    min_resolution
					SCREAMING_SNAKE_CASE	:       Dict 							=    max_resolution
					SCREAMING_SNAKE_CASE	:       Dict 							=    do_resize
					SCREAMING_SNAKE_CASE	:       List[str] 							=    size
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    crop_pct
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    do_normalize
					SCREAMING_SNAKE_CASE	:       Dict 							=    image_mean
					SCREAMING_SNAKE_CASE	:       str 							=    image_std







	def  _lowerCAmelCase    (			self							:					Dict						)	->  Any:





					"""simple docstring"""


					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "crop_pct": self.crop_pct,
					}




@require_torch
@require_vision
class 						lowerCAmelCase__	(  _lowerCamelCase       ,       unittest.TestCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   ConvNextImageProcessor if is_vision_available() else None





	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    ConvNextImageProcessingTester(self						)





	@property
	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Dict:





					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()





	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.image_processing_class(**self.image_processor_dict						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'do_resize'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'size'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'crop_pct'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'do_normalize'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'image_mean'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'image_std'						)						)





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.image_processing_class.from_dict(self.image_processor_dict						)
					self.assertEqual(image_processor.size    ,					{'shortest_edge': 20}						)

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





	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


					pass





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random PIL images
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					Image.Image						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       str 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)





	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random numpy tensors
					SCREAMING_SNAKE_CASE	:       Tuple 							=    prepare_image_inputs(self.image_processor_tester    ,					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
					SCREAMING_SNAKE_CASE	:       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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)







	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE	:       Tuple 							=    prepare_image_inputs(self.image_processor_tester    ,					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
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       Any 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)










"""simple docstring"""

import warnings

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


A_	:						Any				=			logging.get_logger(__name__)
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					*_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  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						)





