




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 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    def __init__(			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	Dict=7  ,      SCREAMING_SNAKE_CASE__	:	Tuple=3  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=18  ,      SCREAMING_SNAKE_CASE__	:	List[str]=30  ,      SCREAMING_SNAKE_CASE__	:	Dict=400  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=True  ,      SCREAMING_SNAKE_CASE__	:	Tuple=None  ,      SCREAMING_SNAKE_CASE__	:	Dict=True  ,      SCREAMING_SNAKE_CASE__	:	str=None  ,      SCREAMING_SNAKE_CASE__	:	str=True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=[0.48_145_466, 0.4_578_275, 0.40_821_073]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=True  ,      ) ->			int:
         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   a (			self	:	Union[str, Any]  ) ->			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   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	List[str]=False  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=False  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=False  ) ->			Optional[Any]:
         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__      ,						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(SCREAMING_SNAKE_CASE__  ,      0  ,      -1  )  ) for x in image_inputs]

         if torchify:
              lowerCAmelCase__            =					[torch.from_numpy(SCREAMING_SNAKE_CASE__  ) for x in image_inputs]

         return image_inputs






@require_torch
@require_vision
class 					__lowerCamelCase			(       UpperCamelCase__      ,							unittest.TestCase       ):




    """simple docstring"""

    snake_case__   = ChineseCLIPImageProcessor if is_vision_available() else None


    def   a (			self	:	Optional[Any]  ) ->			List[str]:
         lowerCAmelCase__            =					ChineseCLIPImageProcessingTester(self  ,      do_center_crop=SCREAMING_SNAKE_CASE__  )


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


    def   a (			self	:	Tuple  ) ->			int:
         lowerCAmelCase__            =					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   a (			self	:	Dict  ) ->			int:
         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   a (			self	:	str  ) ->			Optional[int]:
         pass


    def   a (			self	:	Tuple  ) ->			Union[str, Any]:
         # Initialize image_processing
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         lowerCAmelCase__            =					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__            =					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(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   a (			self	:	Union[str, Any]  ) ->			Dict:
         # Initialize image_processing
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         lowerCAmelCase__            =					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__            =					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(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   a (			self	:	Optional[Any]  ) ->			Any:
         # Initialize image_processing
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         lowerCAmelCase__            =					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__            =					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(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 					__lowerCamelCase			(       UpperCamelCase__      ,							unittest.TestCase       ):




    """simple docstring"""

    snake_case__   = ChineseCLIPImageProcessor if is_vision_available() else None


    def   a (			self	:	Optional[int]  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					ChineseCLIPImageProcessingTester(self  ,      num_channels=4  ,      do_center_crop=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					3


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


    def   a (			self	:	List[str]  ) ->			Dict:
         lowerCAmelCase__            =					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   a (			self	:	Any  ) ->			Optional[int]:
         pass


    def   a (			self	:	str  ) ->			Union[str, Any]:
         # Initialize image_processing
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         lowerCAmelCase__            =					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__            =					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(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"],
             )  ,      )







def      A__       (    SCREAMING_SNAKE_CASE_   :      int = 2_00_00_00						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   [0 for i in range(n + 1						)]
   _UpperCAmelCase           =   1
   _UpperCAmelCase           =   1

   for i in range(2				,	int(n**0.5						) + 1						):
      if primality_list[i] == 0:
         for j in range(i * i				,	n + 1				,	SCREAMING_SNAKE_CASE_						):
            _UpperCAmelCase           =   1
   _UpperCAmelCase           =   0
   for i in range(SCREAMING_SNAKE_CASE_						):
      if primality_list[i] == 0:
         sum_of_primes += i
   return sum_of_primes


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