

"""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 DeiTImageProcessor

class        _UpperCamelCase	(						unittest.TestCase	):


  """simple docstring"""





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


         '''simple docstring'''





         __magic_name__			:		List[str]        =     size if size is not None else {"""height""": 20, """width""": 20}
         __magic_name__			:		Tuple        =     crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

         __magic_name__			:		Tuple        =     parent
         __magic_name__			:		Dict        =     batch_size
         __magic_name__			:		List[str]        =     num_channels
         __magic_name__			:		Any        =     image_size
         __magic_name__			:		Optional[Any]        =     min_resolution
         __magic_name__			:		Any        =     max_resolution
         __magic_name__			:		Dict        =     do_resize
         __magic_name__			:		int        =     size
         __magic_name__			:		int        =     do_center_crop
         __magic_name__			:		Any        =     crop_size
         __magic_name__			:		Dict        =     do_normalize
         __magic_name__			:		Tuple        =     image_mean
         __magic_name__			:		Dict        =     image_std




  def 							_UpperCAmelCase      ( self					: Tuple      )	->			Tuple:


         '''simple docstring'''





         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,
         }

@require_torch
@require_vision
class        _UpperCamelCase	(						lowerCamelCase__   ,				unittest.TestCase	):


  """simple docstring"""





  snake_case_        =    DeiTImageProcessor if is_vision_available() else None
  snake_case_        =    True



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


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     DeiTImageProcessingTester(self      )



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


         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()



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


         '''simple docstring'''





         __magic_name__			:		str        =     self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(snake_case       ,       '''do_resize'''      )      )
         self.assertTrue(hasattr(snake_case       ,       '''size'''      )      )
         self.assertTrue(hasattr(snake_case       ,       '''do_center_crop'''      )      )
         self.assertTrue(hasattr(snake_case       ,       '''center_crop'''      )      )
         self.assertTrue(hasattr(snake_case       ,       '''do_normalize'''      )      )
         self.assertTrue(hasattr(snake_case       ,       '''image_mean'''      )      )
         self.assertTrue(hasattr(snake_case       ,       '''image_std'''      )      )



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


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     self.image_processing_class.from_dict(self.image_processor_dict      )
         self.assertEqual(image_processor.size       ,       {'''height''': 20, '''width''': 20}      )
         self.assertEqual(image_processor.crop_size       ,       {'''height''': 18, '''width''': 18}      )

         __magic_name__			:		Optional[Any]        =     self.image_processing_class.from_dict(self.image_processor_dict       ,       size=42       ,       crop_size=84      )
         self.assertEqual(image_processor.size       ,       {'''height''': 42, '''width''': 42}      )
         self.assertEqual(image_processor.crop_size       ,       {'''height''': 84, '''width''': 84}      )



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


         '''simple docstring'''





         pass



  def 							_UpperCAmelCase      ( self					: Any      )	->			Tuple:


         '''simple docstring'''





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

         # Test not batched input
         __magic_name__			:		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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,       )

         # Test batched
         __magic_name__			:		Dict        =     image_processing(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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,       )



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


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     self.image_processing_class(**self.image_processor_dict      )
         # create random numpy tensors
         __magic_name__			:		str        =     prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=snake_case       ,       numpify=snake_case      )
         for image in image_inputs:
                self.assertIsInstance(snake_case       ,       np.ndarray      )

         # Test not batched input
         __magic_name__			:		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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,       )

         # Test batched
         __magic_name__			:		Optional[Any]        =     image_processing(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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,       )




  def 							_UpperCAmelCase      ( self					: int      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     self.image_processing_class(**self.image_processor_dict      )
         # create random PyTorch tensors
         __magic_name__			:		Any        =     prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=snake_case       ,       torchify=snake_case      )
         for image in image_inputs:
                self.assertIsInstance(snake_case       ,       torch.Tensor      )

         # Test not batched input
         __magic_name__			:		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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,       )

         # Test batched
         __magic_name__			:		List[Any]        =     image_processing(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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,       )








"""simple docstring"""



from itertools import combinations, product




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int   )		->bool:
						if xa == ya == 0 or xa == ya == 0:
												return False
						A__    :	List[str]			=	xa * xa + ya * ya
						A__    :	List[Any]			=	xa * xa + ya * ya
						A__    :	Optional[int]			=	(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
						return (
						    a_square + b_square == c_square
						    or a_square + c_square == b_square
						    or b_square + c_square == a_square
						)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int = 5_0   )		->int:
						return sum(
						    1
						    for pta, pta in combinations(product(range(limit + 1   ),    repeat=2   ),    2   )
						    if is_right(*UpperCAmelCase__,    *UpperCAmelCase__   )   )


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






