


'''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"],
											    )			,				)









'''simple docstring'''




def 							a_       (						lowercase    )			->str:






				'''simple docstring'''

				return " ".join(sorted(set(sentence.split()    )    )    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()





