



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 PoolFormerImageProcessor


class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					List[str]=7			,						__lowerCAmelCase					:					Union[str, Any]=3			,						__lowerCAmelCase					:					Optional[int]=30			,						__lowerCAmelCase					:					Optional[int]=4_00			,						__lowerCAmelCase					:					List[Any]=True			,						__lowerCAmelCase					:					Optional[Any]=None			,						__lowerCAmelCase					:					Optional[Any]=0.9			,						__lowerCAmelCase					:					List[Any]=None			,						__lowerCAmelCase					:					int=True			,						__lowerCAmelCase					:					Optional[int]=[0.5, 0.5, 0.5]			,						__lowerCAmelCase					:					Union[str, Any]=[0.5, 0.5, 0.5]			,						)   ->	List[Any]:
    _A      =       size if size is not None else {'''shortest_edge''': 30}
    _A      =       crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
    _A      =       parent
    _A      =       batch_size
    _A      =       num_channels
    _A      =       min_resolution
    _A      =       max_resolution
    _A      =       do_resize_and_center_crop
    _A      =       size
    _A      =       crop_pct
    _A      =       crop_size
    _A      =       do_normalize
    _A      =       image_mean
    _A      =       image_std

 def     snake_case_      (    self					:					Tuple      )   ->	int:
    return {
        "size": self.size,
        "do_resize_and_center_crop": self.do_resize_and_center_crop,
        "crop_pct": self.crop_pct,
        "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 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Dict =  PoolFormerImageProcessor if is_vision_available() else None

 def     snake_case_      (    self					:					List[str]      )   ->	Optional[int]:
    _A      =       PoolFormerImageProcessingTester(self      )

 @property
 def     snake_case_      (    self					:					List[Any]      )   ->	List[Any]:
    return self.image_processor_tester.prepare_image_processor_dict()

 def     snake_case_      (    self					:					Optional[int]      )   ->	List[str]:
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_resize_and_center_crop'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''size'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''crop_pct'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_normalize'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''image_mean'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''image_std'''      )      )

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[int]:
    _A      =       self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size			,						{'''shortest_edge''': 30}      )
    self.assertEqual(image_processor.crop_size			,						{'''height''': 30, '''width''': 30}      )

    _A      =       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     snake_case_      (    self					:					List[Any]      )   ->	Union[str, Any]:
    pass

 def     snake_case_      (    self					:					List[str]      )   ->	List[Any]:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						Image.Image      )

    # Test not batched input
    _A      =       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
    _A      =       image_processing(__lowerCAmelCase			,						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     snake_case_      (    self					:					Any      )   ->	int:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase			,						numpify=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						np.ndarray      )

    # Test not batched input
    _A      =       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
    _A      =       image_processing(__lowerCAmelCase			,						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     snake_case_      (    self					:					int      )   ->	List[str]:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase			,						torchify=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						torch.Tensor      )

    # Test not batched input
    _A      =       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
    _A      =       image_processing(__lowerCAmelCase			,						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'''],
        )			,						)








import itertools
import math

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     bool:

   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(_snake_case				) + 1				)	,   6				):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Dict:
   _A      =       2
   while True:
      if is_prime(_snake_case				):
         yield num
      num += 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 10_001				)  ->     int:
   return next(itertools.islice(prime_generator()	,   nth - 1	,   _snake_case				)				)


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



