





'''simple docstring'''


import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class    lowerCAmelCase_		(		unittest.TestCase		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase = True			,     _lowerCAmelCase = None			,     _lowerCAmelCase = 32			,     _lowerCAmelCase = True			,     _lowerCAmelCase = 1 / 255			,     _lowerCAmelCase = True			,     _lowerCAmelCase = True			,     _lowerCAmelCase = [0.48145466, 0.4578275, 0.40821073]			,     _lowerCAmelCase = [0.26862954, 0.26130258, 0.27577711]			,     _lowerCAmelCase = True			,     _lowerCAmelCase=7			,     _lowerCAmelCase=30			,     _lowerCAmelCase=400			,     _lowerCAmelCase=3			,     )    ->							Tuple:
     _lowerCAmelCase				=      parent
     _lowerCAmelCase				=      do_resize
     _lowerCAmelCase				=      size if size is not None else {"shortest_edge": 288}
     _lowerCAmelCase				=      size_divisor
     _lowerCAmelCase				=      do_rescale
     _lowerCAmelCase				=      rescale_factor
     _lowerCAmelCase				=      do_normalize
     _lowerCAmelCase				=      do_center_crop
     _lowerCAmelCase				=      image_mean
     _lowerCAmelCase				=      image_std
     _lowerCAmelCase				=      do_pad
     _lowerCAmelCase				=      batch_size
     _lowerCAmelCase				=      num_channels
     _lowerCAmelCase				=      min_resolution
     _lowerCAmelCase				=      max_resolution




 def 							_snake_case   (	self		)    ->							Any:
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "size_divisor": self.size_divisor,
     }





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase=False		)    ->							Optional[int]:
     if not batched:
         _lowerCAmelCase				=      self.size["shortest_edge"]
         _lowerCAmelCase				=      image_inputs[0]
         if isinstance(_lowerCAmelCase			,     Image.Image		):
             _lowerCAmelCase			,			_lowerCAmelCase				=      image.size
         else:
             _lowerCAmelCase			,			_lowerCAmelCase				=      image.shape[1], image.shape[2]
         _lowerCAmelCase				=      size / min(_lowerCAmelCase			,     _lowerCAmelCase		)
         if h < w:
             _lowerCAmelCase			,			_lowerCAmelCase				=      size, scale * w
         else:
             _lowerCAmelCase			,			_lowerCAmelCase				=      scale * h, size

         _lowerCAmelCase				=      int((1333 / 800) * size		)
         if max(_lowerCAmelCase			,     _lowerCAmelCase		) > max_size:
             _lowerCAmelCase				=      max_size / max(_lowerCAmelCase			,     _lowerCAmelCase		)
             _lowerCAmelCase				=      newh * scale
             _lowerCAmelCase				=      neww * scale

         _lowerCAmelCase			,			_lowerCAmelCase				=      int(newh + 0.5		), int(neww + 0.5		)
         _lowerCAmelCase			,			_lowerCAmelCase				=      (
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

     else:
         _lowerCAmelCase				=      []
         for image in image_inputs:
             _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_expected_values([image]		)
             expected_values.append((expected_height, expected_width)		)
         _lowerCAmelCase				=      max(_lowerCAmelCase			,     key=lambda _lowerCAmelCase		: item[0]		)[0]
         _lowerCAmelCase				=      max(_lowerCAmelCase			,     key=lambda _lowerCAmelCase		: item[1]		)[1]

     return expected_height, expected_width







@require_torch
@require_vision
class    lowerCAmelCase_		(		__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							Dict         =					BridgeTowerImageProcessor if is_vision_available() else None




 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      BridgeTowerImageProcessingTester(self		)




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




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      self.image_processing_class(**self.image_processor_dict		)
     self.assertTrue(hasattr(_lowerCAmelCase			,     "image_mean"		)		)
     self.assertTrue(hasattr(_lowerCAmelCase			,     "image_std"		)		)
     self.assertTrue(hasattr(_lowerCAmelCase			,     "do_normalize"		)		)
     self.assertTrue(hasattr(_lowerCAmelCase			,     "do_resize"		)		)
     self.assertTrue(hasattr(_lowerCAmelCase			,     "size"		)		)
     self.assertTrue(hasattr(_lowerCAmelCase			,     "size_divisor"		)		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     pass




 def 							_snake_case   (	self		)    ->							int:
     # Initialize image processor
     _lowerCAmelCase				=      self.image_processing_class(**self.image_processor_dict		)
     # create random PIL images
     _lowerCAmelCase				=      prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_lowerCAmelCase		)
     for image in image_inputs:
         self.assertIsInstance(_lowerCAmelCase			,     Image.Image		)

     # Test not batched input
     _lowerCAmelCase				=      image_processing(image_inputs[0]			,     return_tensors="pt"		).pixel_values

     _lowerCAmelCase			,			_lowerCAmelCase				=      self.image_processor_tester.get_expected_values(_lowerCAmelCase		)
     self.assertEqual(
         encoded_images.shape			,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,     )

     # Test batched
     _lowerCAmelCase				=      image_processing(_lowerCAmelCase			,     return_tensors="pt"		).pixel_values

     _lowerCAmelCase			,			_lowerCAmelCase				=      self.image_processor_tester.get_expected_values(_lowerCAmelCase			,     batched=_lowerCAmelCase		)
     self.assertEqual(
         encoded_images.shape			,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )			,     )




 def 							_snake_case   (	self		)    ->							Any:
     # Initialize image processor
     _lowerCAmelCase				=      self.image_processing_class(**self.image_processor_dict		)
     # create random numpy tensors
     _lowerCAmelCase				=      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
     _lowerCAmelCase				=      image_processing(image_inputs[0]			,     return_tensors="pt"		).pixel_values

     _lowerCAmelCase			,			_lowerCAmelCase				=      self.image_processor_tester.get_expected_values(_lowerCAmelCase		)
     self.assertEqual(
         encoded_images.shape			,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,     )

     # Test batched
     _lowerCAmelCase				=      image_processing(_lowerCAmelCase			,     return_tensors="pt"		).pixel_values

     _lowerCAmelCase			,			_lowerCAmelCase				=      self.image_processor_tester.get_expected_values(_lowerCAmelCase			,     batched=_lowerCAmelCase		)
     self.assertEqual(
         encoded_images.shape			,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )			,     )





 def 							_snake_case   (	self		)    ->							Tuple:
     # Initialize image processor
     _lowerCAmelCase				=      self.image_processing_class(**self.image_processor_dict		)
     # create random PyTorch tensors
     _lowerCAmelCase				=      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
     _lowerCAmelCase				=      image_processing(image_inputs[0]			,     return_tensors="pt"		).pixel_values

     _lowerCAmelCase			,			_lowerCAmelCase				=      self.image_processor_tester.get_expected_values(_lowerCAmelCase		)
     self.assertEqual(
         encoded_images.shape			,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,     )

     # Test batched
     _lowerCAmelCase				=      image_processing(_lowerCAmelCase			,     return_tensors="pt"		).pixel_values

     _lowerCAmelCase			,			_lowerCAmelCase				=      self.image_processor_tester.get_expected_values(_lowerCAmelCase			,     batched=_lowerCAmelCase		)
     self.assertEqual(
         encoded_images.shape			,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )			,     )









'''simple docstring'''


from math import isqrt, loga


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      [True] * max_number
    for i in range(2		,       isqrt(max_number - 1    ) + 1    ):
        if is_prime[i]:
            for j in range(i**2		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
                _lowerCAmelCase				=      False

    return [i for i in range(2		,       SCREAMING_SNAKE_CASE_    ) if is_prime[i]]


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 800800		,       SCREAMING_SNAKE_CASE_  :					int = 800800    ):



    '''simple docstring'''





    _lowerCAmelCase				=      degree * loga(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      int(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      calculate_prime_numbers(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      len(SCREAMING_SNAKE_CASE_    ) - 1
    while left < right:
        while (
            prime_numbers[right] * loga(prime_numbers[left]    )
            + prime_numbers[left] * loga(prime_numbers[right]    )
            > upper_bound
        ):
            right -= 1
        hybrid_integers_count += right - left
        left += 1

    return hybrid_integers_count


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


