


"""simple docstring"""
import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor





class        __A       (unittest.TestCase):





							'''simple docstring'''

							def __init__(			self							: Optional[Any]						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: Dict=7						,    UpperCAmelCase_							: Any=3						,    UpperCAmelCase_							: Any=18						,    UpperCAmelCase_							: Any=30						,    UpperCAmelCase_							: Dict=400						,    UpperCAmelCase_							: Any=True						,    UpperCAmelCase_							: Dict=None						,    UpperCAmelCase_							: Union[str, Any]=True						,    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						size if size is not None else {"""height""": 18, """width""": 18}
														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_resize
														snake_case_          =						size
														snake_case_          =						do_normalize



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



														"""simple docstring"""



														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
														            [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
														        ]    ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}





@require_torch
@require_vision
class        __A       (snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						Union[str, Any]							 =      ImageGPTImageProcessor if is_vision_available() else None



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_          =						ImageGPTImageProcessingTester(self    )



							@property
							def 						lowerCAmelCase    (			self							: List[str]    )   ->List[str]:



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						self.image_processing_class(**self.image_processor_dict    )
														self.assertTrue(hasattr(UpperCAmelCase_						,    """clusters"""    )    )
														self.assertTrue(hasattr(UpperCAmelCase_						,    """do_resize"""    )    )
														self.assertTrue(hasattr(UpperCAmelCase_						,    """size"""    )    )
														self.assertTrue(hasattr(UpperCAmelCase_						,    """do_normalize"""    )    )



							def 						lowerCAmelCase    (			self							: Dict    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						self.image_processing_class.from_dict(self.image_processor_dict    )
														self.assertEqual(image_processor.size						,    {"""height""": 18, """width""": 18}    )

														snake_case_          =						self.image_processing_class.from_dict(self.image_processor_dict						,    size=42    )
														self.assertEqual(image_processor.size						,    {"""height""": 42, """width""": 42}    )



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



														"""simple docstring"""



														snake_case_          =						self.image_processing_class(**self.image_processor_dict    )
														snake_case_          =						json.loads(image_processor.to_json_string()    )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_						,    obj[key]    )    )
																					else:
																												self.assertEqual(obj[key]						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->str:



														"""simple docstring"""



														snake_case_          =						self.image_processing_class(**self.image_processor_dict    )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					snake_case_          =						os.path.join(UpperCAmelCase_						,    """image_processor.json"""    )
																					image_processor_first.to_json_file(UpperCAmelCase_    )
																					snake_case_          =						self.image_processing_class.from_json_file(UpperCAmelCase_    ).to_dict()

														snake_case_          =						image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_						,    image_processor_second[key]    )    )
																					else:
																												self.assertEqual(image_processor_first[key]						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: List[str]    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_          =						self.image_processing_class(**self.image_processor_dict    )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(UpperCAmelCase_    )
																					snake_case_          =						self.image_processing_class.from_pretrained(UpperCAmelCase_    ).to_dict()

														snake_case_          =						image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_						,    image_processor_second[key]    )    )
																					else:
																												self.assertEqual(image_processor_first[key]						,    UpperCAmelCase_    )



							@unittest.skip("""ImageGPT requires clusters at initialization"""    )
							def 						lowerCAmelCase    (			self							: str    )   ->str:



														"""simple docstring"""



														pass







def   _a			( )      ->			List[str]:
							snake_case_          =						load_dataset("""hf-internal-testing/fixtures_image_utils"""			,	split="""test"""			)

							snake_case_          =						Image.open(dataset[4]["""file"""]			)
							snake_case_          =						Image.open(dataset[5]["""file"""]			)

							snake_case_          =						[imagea, imagea]

							return images





@require_vision
@require_torch
class        __A       (unittest.TestCase):





							'''simple docstring'''

							@slow
							def 						lowerCAmelCase    (			self							: int    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""    )

														snake_case_          =						prepare_images()

														# test non-batched
														snake_case_          =						image_processing(images[0]						,    return_tensors="""pt"""    )

														self.assertIsInstance(encoding.input_ids						,    torch.LongTensor    )
														self.assertEqual(encoding.input_ids.shape						,    (1, 1_024)    )

														snake_case_          =						[306, 191, 191]
														self.assertEqual(encoding.input_ids[0, :3].tolist()						,    UpperCAmelCase_    )

														# test batched
														snake_case_          =						image_processing(UpperCAmelCase_						,    return_tensors="""pt"""    )

														self.assertIsInstance(encoding.input_ids						,    torch.LongTensor    )
														self.assertEqual(encoding.input_ids.shape						,    (2, 1_024)    )

														snake_case_          =						[303, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()						,    UpperCAmelCase_    )







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			list[int]:
							snake_case_          =						len(_SCREAMING_SNAKE_CASE			)
							for i in range(_SCREAMING_SNAKE_CASE			):
														for j in range(i + 1			,	_SCREAMING_SNAKE_CASE			):
																					if numbers[j] < numbers[i]:
																												snake_case_    ,  snake_case_          =						numbers[j], numbers[i]
							return numbers


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      input('Enter numbers separated by a comma:\n').strip()
				__SCREAMING_SNAKE_CASE							:      List[str]						=      [int(item) for item in user_input.split(',')]
				print(exchange_sort(unsorted))



