code: 
  
  
  '''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 _snake_case   ( unittest.TestCase   ):
  
  
  
  
  
  
  
  							def __init__(   self			,				__A			,				__A=7			,				__A=3			,				__A=18			,				__A=30			,				__A=400			,				__A=True			,				__A=None			,				__A=0.875			,				__A=True			,				__A=[0.5, 0.5, 0.5]			,				__A=[0.5, 0.5, 0.5]			,				)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					size if size is not None else {"shortest_edge": 20}
  											__lowerCamelCase		:      Union[str, Any]											=					parent
  											__lowerCamelCase		:      Optional[int]											=					batch_size
  											__lowerCamelCase		:      Optional[Any]											=					num_channels
  											__lowerCamelCase		:      List[str]											=					image_size
  											__lowerCamelCase		:      Optional[Any]											=					min_resolution
  											__lowerCamelCase		:      Tuple											=					max_resolution
  											__lowerCamelCase		:      Dict											=					do_resize
  											__lowerCamelCase		:      Tuple											=					size
  											__lowerCamelCase		:      Any											=					crop_pct
  											__lowerCamelCase		:      Optional[int]											=					do_normalize
  											__lowerCamelCase		:      List[Any]											=					image_mean
  											__lowerCamelCase		:      List[Any]											=					image_std
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[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 _snake_case   ( a__    ,   unittest.TestCase   ):
  							__a												=     ConvNextImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->str:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[str]											=					ConvNextImageProcessingTester(self	)
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					self.image_processing_class(**self.image_processor_dict	)
  											self.assertTrue(hasattr(__A			,				"do_resize"	)	)
  											self.assertTrue(hasattr(__A			,				"size"	)	)
  											self.assertTrue(hasattr(__A			,				"crop_pct"	)	)
  											self.assertTrue(hasattr(__A			,				"do_normalize"	)	)
  											self.assertTrue(hasattr(__A			,				"image_mean"	)	)
  											self.assertTrue(hasattr(__A			,				"image_std"	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[Any]											=					self.image_processing_class.from_dict(self.image_processor_dict	)
  											self.assertEqual(image_processor.size			,				{"shortest_edge": 20}	)
  
  											__lowerCamelCase		:      List[Any]											=					self.image_processing_class.from_dict(self.image_processor_dict			,				size=42	)
  											self.assertEqual(image_processor.size			,				{"shortest_edge": 42}	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											pass
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					self.image_processing_class(**self.image_processor_dict	)
  											# create random PIL images
  											__lowerCamelCase		:      Optional[int]											=					prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__A	)
  											for image in image_inputs:
  															self.assertIsInstance(__A			,				Image.Image	)
  
  											# Test not batched input
  											__lowerCamelCase		:      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
  											__lowerCamelCase		:      Tuple											=					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.size["shortest_edge"],
  											        self.image_processor_tester.size["shortest_edge"],
  											    )			,				)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					self.image_processing_class(**self.image_processor_dict	)
  											# create random numpy tensors
  											__lowerCamelCase		:      Union[str, Any]											=					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
  											__lowerCamelCase		:      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
  											__lowerCamelCase		:      Union[str, 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.size["shortest_edge"],
  											        self.image_processor_tester.size["shortest_edge"],
  											    )			,				)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					self.image_processing_class(**self.image_processor_dict	)
  											# create random PyTorch tensors
  											__lowerCamelCase		:      List[Any]											=					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
  											__lowerCamelCase		:      Optional[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
  											__lowerCamelCase		:      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.size["shortest_edge"],
  											        self.image_processor_tester.size["shortest_edge"],
  											    )			,				)
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  def 							a_       (						lowercase    )			->str:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				return " ".join(sorted(set(sentence.split()    )    )    )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  