





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    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 def __init__(self				:   Optional[Any]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   int=7       , UpperCAmelCase_				:   Tuple=3       , UpperCAmelCase_				:   Optional[Any]=18       , UpperCAmelCase_				:   Any=30       , UpperCAmelCase_				:   int=400       , UpperCAmelCase_				:   Optional[Any]=True       , UpperCAmelCase_				:   List[Any]=None       , UpperCAmelCase_				:   Union[str, Any]=True       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   Dict=True       , UpperCAmelCase_				:   List[Any]=[0.4814_5466, 0.457_8275, 0.4082_1073]       , UpperCAmelCase_				:   str=[0.2686_2954, 0.2613_0258, 0.2757_7711]       , UpperCAmelCase_				:   int=True       , )       ->int:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =size if size is not None else {"height": 224, "width": 224}
   lowerCamelCase__:      Dict			  =crop_size if crop_size is not None else {"height": 18, "width": 18}
   lowerCamelCase__:      Dict			  =parent
   lowerCamelCase__:      Union[str, Any]			  =batch_size
   lowerCamelCase__:      Any			  =num_channels
   lowerCamelCase__:      Any			  =image_size
   lowerCamelCase__:      Dict			  =min_resolution
   lowerCamelCase__:      int			  =max_resolution
   lowerCamelCase__:      Union[str, Any]			  =do_resize
   lowerCamelCase__:      Any			  =size
   lowerCamelCase__:      Union[str, Any]			  =do_center_crop
   lowerCamelCase__:      Optional[int]			  =crop_size
   lowerCamelCase__:      Dict			  =do_normalize
   lowerCamelCase__:      Optional[int]			  =image_mean
   lowerCamelCase__:      int			  =image_std
   lowerCamelCase__:      int			  =do_convert_rgb

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->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,
       "do_convert_rgb": self.do_convert_rgb,
   }







 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   Optional[int]=False       , UpperCAmelCase_				:   Tuple=False       , UpperCAmelCase_				:   Union[str, Any]=False)       ->Optional[int]:



   '''simple docstring'''



   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   if equal_resolution:
     lowerCamelCase__:      str			  =[]
     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__:      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(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__:      List[str]			  =[Image.fromarray(np.moveaxis(UpperCAmelCase_       , 0       , -1)) for x in image_inputs]

   if torchify:
     lowerCamelCase__:      Any			  =[torch.from_numpy(UpperCAmelCase_) for x in image_inputs]

   return image_inputs



@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			ChineseCLIPImageProcessor if is_vision_available() else None

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =ChineseCLIPImageProcessingTester(self       , do_center_crop=UpperCAmelCase_)

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



   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_resize"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "size"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_center_crop"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "center_crop"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_normalize"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_mean"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_std"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_convert_rgb"))

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =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__:      Union[str, Any]			  =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 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Optional[Any]:



   '''simple docstring'''



   pass

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



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowerCamelCase__:      List[Any]			  =self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , 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.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )       , )

   # Test batched
   lowerCamelCase__:      str			  =image_processing(UpperCAmelCase_       , 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 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      int			  =self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowerCamelCase__:      List[str]			  =self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_       , numpify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , np.ndarray)

   # 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__:      Optional[int]			  =image_processing(UpperCAmelCase_       , 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 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowerCamelCase__:      Union[str, Any]			  =self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_       , torchify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , torch.Tensor)

   # Test not batched input
   lowerCamelCase__:      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
   lowerCamelCase__:      List[str]			  =image_processing(UpperCAmelCase_       , 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    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			ChineseCLIPImageProcessor if is_vision_available() else None

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



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =ChineseCLIPImageProcessingTester(self       , num_channels=4       , do_center_crop=UpperCAmelCase_)
   lowerCamelCase__:      Dict			  =3

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Optional[int]:



   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_resize"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "size"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_center_crop"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "center_crop"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_normalize"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_mean"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_std"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_convert_rgb"))

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



   '''simple docstring'''



   pass







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->int:



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowerCamelCase__:      str			  =self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , Image.Image)

   # Test not batched input
   lowerCamelCase__:      Optional[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__:      Optional[int]			  =image_processing(UpperCAmelCase_       , 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"],
       )       , )








import itertools
import math





def      lowerCAmelCase_ (       __a		)     ->		bool:







  """simple docstring"""





  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5	,    int(math.sqrt(__a		) + 1		)	,    6		):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True





def      lowerCAmelCase_ (       )     ->		str:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =2
  while True:
    if is_prime(__a		):
      yield num
    num += 1





def      lowerCAmelCase_ (       __a = 10001		)     ->		int:







  """simple docstring"""





  return next(itertools.islice(prime_generator()	,    nth - 1	,    __a		)		)


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

