



'''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 ChineseCLIPImageProcessor

class __snake_case( unittest.TestCase   ):


						'''simple docstring'''



						def __init__(   self      ,			A_      ,			A_=7      ,			A_=3      ,			A_=18      ,			A_=30      ,			A_=400      ,			A_=True      ,			A_=None      ,			A_=True      ,			A_=None      ,			A_=True      ,			A_=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]      ,			A_=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]      ,			A_=True      ,			)      ->      List[Any]:
							lowerCAmelCase       =      size if size is not None else {"height": 224, "width": 224}
							lowerCAmelCase       =      crop_size if crop_size is not None else {"height": 18, "width": 18}
							lowerCAmelCase       =      parent
							lowerCAmelCase       =      batch_size
							lowerCAmelCase       =      num_channels
							lowerCAmelCase       =      image_size
							lowerCAmelCase       =      min_resolution
							lowerCAmelCase       =      max_resolution
							lowerCAmelCase       =      do_resize
							lowerCAmelCase       =      size
							lowerCAmelCase       =      do_center_crop
							lowerCAmelCase       =      crop_size
							lowerCAmelCase       =      do_normalize
							lowerCAmelCase       =      image_mean
							lowerCAmelCase       =      image_std
							lowerCAmelCase       =      do_convert_rgb


						def 							__snake_case       (   self )      ->      Union[str, Any]:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_convert_rgb": self.do_convert_rgb,
							}




						def 							__snake_case       (   self      ,			A_=False      ,			A_=False      ,			A_=False )      ->      str:
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

							if torchify:
								lowerCAmelCase       =      [torch.from_numpy(A_ ) for x in image_inputs]

							return image_inputs







@require_torch
@require_vision
class __snake_case( __snake_case						,				unittest.TestCase   ):


						'''simple docstring'''



						UpperCAmelCase				:       List[Any] 	=						ChineseCLIPImageProcessor if is_vision_available() else None


						def 							__snake_case       (   self )      ->      List[str]:
							lowerCAmelCase       =      ChineseCLIPImageProcessingTester(self      ,			do_center_crop=A_ )


						@property
						def 							__snake_case       (   self )      ->      Optional[int]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 							__snake_case       (   self )      ->      Union[str, Any]:
							lowerCAmelCase       =      self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(A_      ,			"""do_resize""" ) )
							self.assertTrue(hasattr(A_      ,			"""size""" ) )
							self.assertTrue(hasattr(A_      ,			"""do_center_crop""" ) )
							self.assertTrue(hasattr(A_      ,			"""center_crop""" ) )
							self.assertTrue(hasattr(A_      ,			"""do_normalize""" ) )
							self.assertTrue(hasattr(A_      ,			"""image_mean""" ) )
							self.assertTrue(hasattr(A_      ,			"""image_std""" ) )
							self.assertTrue(hasattr(A_      ,			"""do_convert_rgb""" ) )


						def 							__snake_case       (   self )      ->      str:
							lowerCAmelCase       =      self.image_processing_class.from_dict(self.image_processor_dict )
							self.assertEqual(image_processor.size      ,			{"""height""": 224, """width""": 224} )
							self.assertEqual(image_processor.crop_size      ,			{"""height""": 18, """width""": 18} )

							lowerCAmelCase       =      self.image_processing_class.from_dict(self.image_processor_dict      ,			size=42      ,			crop_size=84 )
							self.assertEqual(image_processor.size      ,			{"""shortest_edge""": 42} )
							self.assertEqual(image_processor.crop_size      ,			{"""height""": 84, """width""": 84} )


						def 							__snake_case       (   self )      ->      str:
							pass


						def 							__snake_case       (   self )      ->      Optional[int]:
							lowerCAmelCase       =      self.image_processing_class(**self.image_processor_dict )
							# create random PIL images
							lowerCAmelCase       =      self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
							for image in image_inputs:
								self.assertIsInstance(A_      ,			Image.Image )

							# Test not batched input
							lowerCAmelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )      ,			)

							# Test batched
							lowerCAmelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )      ,			)


						def 							__snake_case       (   self )      ->      Union[str, Any]:
							lowerCAmelCase       =      self.image_processing_class(**self.image_processor_dict )
							# create random numpy tensors
							lowerCAmelCase       =      self.image_processor_tester.prepare_inputs(equal_resolution=A_      ,			numpify=A_ )
							for image in image_inputs:
								self.assertIsInstance(A_      ,			np.ndarray )

							# Test not batched input
							lowerCAmelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )      ,			)

							# Test batched
							lowerCAmelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )      ,			)




						def 							__snake_case       (   self )      ->      Optional[int]:
							lowerCAmelCase       =      self.image_processing_class(**self.image_processor_dict )
							# create random PyTorch tensors
							lowerCAmelCase       =      self.image_processor_tester.prepare_inputs(equal_resolution=A_      ,			torchify=A_ )
							for image in image_inputs:
								self.assertIsInstance(A_      ,			torch.Tensor )

							# Test not batched input
							lowerCAmelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )      ,			)

							# Test batched
							lowerCAmelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )      ,			)







@require_torch
@require_vision
class __snake_case( __snake_case						,				unittest.TestCase   ):


						'''simple docstring'''



						UpperCAmelCase				:       Tuple 	=						ChineseCLIPImageProcessor if is_vision_available() else None


						def 							__snake_case       (   self )      ->      str:
							lowerCAmelCase       =      ChineseCLIPImageProcessingTester(self      ,			num_channels=4      ,			do_center_crop=A_ )
							lowerCAmelCase       =      3


						@property
						def 							__snake_case       (   self )      ->      int:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 							__snake_case       (   self )      ->      Optional[int]:
							lowerCAmelCase       =      self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(A_      ,			"""do_resize""" ) )
							self.assertTrue(hasattr(A_      ,			"""size""" ) )
							self.assertTrue(hasattr(A_      ,			"""do_center_crop""" ) )
							self.assertTrue(hasattr(A_      ,			"""center_crop""" ) )
							self.assertTrue(hasattr(A_      ,			"""do_normalize""" ) )
							self.assertTrue(hasattr(A_      ,			"""image_mean""" ) )
							self.assertTrue(hasattr(A_      ,			"""image_std""" ) )
							self.assertTrue(hasattr(A_      ,			"""do_convert_rgb""" ) )


						def 							__snake_case       (   self )      ->      List[str]:
							pass




						def 							__snake_case       (   self )      ->      int:
							lowerCAmelCase       =      self.image_processing_class(**self.image_processor_dict )
							# create random PIL images
							lowerCAmelCase       =      self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
							for image in image_inputs:
								self.assertIsInstance(A_      ,			Image.Image )

							# Test not batched input
							lowerCAmelCase       =      image_processing(image_inputs[0]      ,			return_tensors="""pt""" ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,			(
							        1,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )      ,			)

							# Test batched
							lowerCAmelCase       =      image_processing(A_      ,			return_tensors="""pt""" ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,			(
							        self.image_processor_tester.batch_size,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )      ,			)






def 						A_							(	_lowerCAmelCase = 1000	)		->					int:
 UpperCamelCase      :   Optional[int]			      =  -1
 UpperCamelCase      :   int			      =  0
 for a in range(1				,  n // 3	):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  UpperCamelCase      :   Optional[Any]			      =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  UpperCamelCase      :   List[str]			      =  n - a - b
  if c * c == (a * a + b * b):
   UpperCamelCase      :   Union[str, Any]			      =  a * b * c
   if candidate >= product:
    UpperCamelCase      :   Tuple			      =  candidate
 return product


if __name__ == "__main__":
    print(f"""{solution() = }""")
