'''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 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_=7 ,						snake_case_=3 ,						snake_case_=1_8 ,						snake_case_=3_0 ,						snake_case_=4_0_0 ,						snake_case_=True ,						snake_case_=None ,						snake_case_=0.8_75 ,						snake_case_=True ,						snake_case_=[0.5, 0.5, 0.5] ,						snake_case_=[0.5, 0.5, 0.5] ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = size if size is not None else {'shortest_edge': 2_0}
       UpperCAmelCase_				:  List[str]  = parent
       UpperCAmelCase_				:  Union[str, Any]  = batch_size
       UpperCAmelCase_				:  Tuple  = num_channels
       UpperCAmelCase_				:  List[str]  = image_size
       UpperCAmelCase_				:  List[Any]  = min_resolution
       UpperCAmelCase_				:  Any  = max_resolution
       UpperCAmelCase_				:  Any  = do_resize
       UpperCAmelCase_				:  str  = size
       UpperCAmelCase_				:  Any  = crop_pct
       UpperCAmelCase_				:  Tuple  = do_normalize
       UpperCAmelCase_				:  Any  = image_mean
       UpperCAmelCase_				:  int  = image_std






  def _UpperCamelCase	(					self		):




       '''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 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Optional[int]		       =						ConvNextImageProcessor if is_vision_available() else None


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = ConvNextImageProcessingTester(self		)


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = 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_ ,						'crop_pct'		)		)
       self.assertTrue(hasattr(snake_case_ ,						'do_normalize'		)		)
       self.assertTrue(hasattr(snake_case_ ,						'image_mean'		)		)
       self.assertTrue(hasattr(snake_case_ ,						'image_std'		)		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.image_processing_class.from_dict(self.image_processor_dict		)
       self.assertEqual(image_processor.size ,						{'shortest_edge': 2_0}		)

       UpperCAmelCase_				:  Optional[int]  = self.image_processing_class.from_dict(self.image_processor_dict ,						size=4_2		)
       self.assertEqual(image_processor.size ,						{'shortest_edge': 4_2}		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       UpperCAmelCase_				:  List[str]  = 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
       UpperCAmelCase_				:  Dict  = 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
       UpperCAmelCase_				:  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.size['shortest_edge'],
               self.image_processor_tester.size['shortest_edge'],
           ) ,						)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       UpperCAmelCase_				:  Union[str, Any]  = 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
       UpperCAmelCase_				:  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.size['shortest_edge'],
               self.image_processor_tester.size['shortest_edge'],
           ) ,						)

       # Test batched
       UpperCAmelCase_				:  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.size['shortest_edge'],
               self.image_processor_tester.size['shortest_edge'],
           ) ,						)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       UpperCAmelCase_				:  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
       UpperCAmelCase_				:  List[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
       UpperCAmelCase_				:  str  = 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.size['shortest_edge'],
               self.image_processor_tester.size['shortest_edge'],
           ) ,						)









'''simple docstring'''


from itertools import combinations, product







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	UpperCAmelCase_		:   int													=       xa * xa + ya * ya
	UpperCAmelCase_		:   List[str]													=       xa * xa + ya * ya
	UpperCAmelCase_		:   Optional[Any]													=       (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     SCREAMING_SNAKE_CASE(	UpperCamelCase = 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() = }')






