


'''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   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=18     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=4_00     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=[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]     ,	SCREAMING_SNAKE_CASE_=[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]     ,	SCREAMING_SNAKE_CASE_=True     ,	)							->	Optional[Any]:
         __lowerCamelCase  :      int														=						size if size is not None else {'height': 2_24, 'width': 2_24}
         __lowerCamelCase  :      Optional[Any]														=						crop_size if crop_size is not None else {'height': 18, 'width': 18}
         __lowerCamelCase  :      Tuple														=						parent
         __lowerCamelCase  :      int														=						batch_size
         __lowerCamelCase  :      Dict														=						num_channels
         __lowerCamelCase  :      Any														=						image_size
         __lowerCamelCase  :      Dict														=						min_resolution
         __lowerCamelCase  :      List[Any]														=						max_resolution
         __lowerCamelCase  :      str														=						do_resize
         __lowerCamelCase  :      Union[str, Any]														=						size
         __lowerCamelCase  :      Any														=						do_center_crop
         __lowerCamelCase  :      List[str]														=						crop_size
         __lowerCamelCase  :      Union[str, Any]														=						do_normalize
         __lowerCamelCase  :      Optional[int]														=						image_mean
         __lowerCamelCase  :      int														=						image_std
         __lowerCamelCase  :      List[str]														=						do_convert_rgb







    def  lowercase_							(				self      )							->	List[str]:
         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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False      )							->	Dict:
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

         if equal_resolution:
              __lowerCamelCase  :      Dict														=						[]
              for i in range(self.batch_size      ):
                   image_inputs.append(
                       np.random.randint(
                           2_55     ,	size=(self.num_channels, self.max_resolution, self.max_resolution)     ,	dtype=np.uinta      )      )
         else:
              __lowerCamelCase  :      int														=						[]
              for i in range(self.batch_size      ):
                   __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						np.random.choice(np.arange(self.min_resolution     ,	self.max_resolution      )     ,	2      )
                   image_inputs.append(np.random.randint(2_55     ,	size=(self.num_channels, width, height)     ,	dtype=np.uinta      )      )

         if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              __lowerCamelCase  :      int														=						[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_     ,	0     ,	-1      )      ) for x in image_inputs]

         if torchify:
              __lowerCamelCase  :      Dict														=						[torch.from_numpy(SCREAMING_SNAKE_CASE_      ) for x in image_inputs]

         return image_inputs







@require_torch
@require_vision
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Dict					=     ChineseCLIPImageProcessor if is_vision_available() else None







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Any														=						ChineseCLIPImageProcessingTester(self     ,	do_center_crop=SCREAMING_SNAKE_CASE_      )







    @property
    def  lowercase_							(				self      )							->	Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()







    def  lowercase_							(				self      )							->	List[str]:
         __lowerCamelCase  :      int														=						self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_resize'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'size'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_center_crop'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'center_crop'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_normalize'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'image_mean'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'image_std'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_convert_rgb'      )      )







    def  lowercase_							(				self      )							->	List[str]:
         __lowerCamelCase  :      List[str]														=						self.image_processing_class.from_dict(self.image_processor_dict      )
         self.assertEqual(image_processor.size     ,	{'height': 2_24, 'width': 2_24}      )
         self.assertEqual(image_processor.crop_size     ,	{'height': 18, 'width': 18}      )

         __lowerCamelCase  :      str														=						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  lowercase_							(				self      )							->	str:
         pass







    def  lowercase_							(				self      )							->	str:
         # Initialize image_processing
         __lowerCamelCase  :      int														=						self.image_processing_class(**self.image_processor_dict      )
         # create random PIL images
         __lowerCamelCase  :      Any														=						self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_      )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	Image.Image      )

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

         # Test batched
         __lowerCamelCase  :      Tuple														=						image_processing(SCREAMING_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  lowercase_							(				self      )							->	Tuple:
         # Initialize image_processing
         __lowerCamelCase  :      Any														=						self.image_processing_class(**self.image_processor_dict      )
         # create random numpy tensors
         __lowerCamelCase  :      Optional[int]														=						self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_     ,	numpify=SCREAMING_SNAKE_CASE_      )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	np.ndarray      )

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

         # Test batched
         __lowerCamelCase  :      int														=						image_processing(SCREAMING_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  lowercase_							(				self      )							->	int:
         # Initialize image_processing
         __lowerCamelCase  :      Optional[int]														=						self.image_processing_class(**self.image_processor_dict      )
         # create random PyTorch tensors
         __lowerCamelCase  :      Optional[int]														=						self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_     ,	torchify=SCREAMING_SNAKE_CASE_      )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	torch.Tensor      )

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

         # Test batched
         __lowerCamelCase  :      List[str]														=						image_processing(SCREAMING_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'],
             )     ,	)







@require_torch
@require_vision
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   int					=     ChineseCLIPImageProcessor if is_vision_available() else None







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      str														=						ChineseCLIPImageProcessingTester(self     ,	num_channels=4     ,	do_center_crop=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[str]														=						3







    @property
    def  lowercase_							(				self      )							->	str:
         return self.image_processor_tester.prepare_image_processor_dict()







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      List[str]														=						self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_resize'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'size'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_center_crop'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'center_crop'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_normalize'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'image_mean'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'image_std'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_convert_rgb'      )      )







    def  lowercase_							(				self      )							->	Union[str, Any]:
         pass







    def  lowercase_							(				self      )							->	Dict:
         # Initialize image_processing
         __lowerCamelCase  :      Any														=						self.image_processing_class(**self.image_processor_dict      )
         # create random PIL images
         __lowerCamelCase  :      Dict														=						self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_      )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	Image.Image      )

         # Test not batched input
         __lowerCamelCase  :      Any														=						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  :      List[Any]														=						image_processing(SCREAMING_SNAKE_CASE_     ,	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'],
             )     ,	)










'''simple docstring'''



from collections import defaultdict
from math import gcd



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 1_50_00_00				)	->						int:
     __lowerCamelCase  :      defaultdict														=						defaultdict(UpperCAmelCase_				)
     __lowerCamelCase  :      Any														=						2
     while 2 * euclid_m * (euclid_m + 1) <= limit:
          for euclid_n in range((euclid_m % 2) + 1 ,       UpperCAmelCase_ ,       2				):
               if gcd(UpperCAmelCase_ ,       UpperCAmelCase_				) > 1:
                    continue
               __lowerCamelCase  :      Any														=						2 * euclid_m * (euclid_m + euclid_n)
               for perimeter in range(UpperCAmelCase_ ,       limit + 1 ,       UpperCAmelCase_				):
                    frequencies[perimeter] += 1
          euclid_m += 1
     return sum(1 for frequency in frequencies.values() if frequency == 1				)


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






