'''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, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ViltImageProcessor

class      lowerCamelCase_     (unittest.TestCase   ):
      '''simple docstring'''

      def __init__(	self						:    Union[str, Any]						, A						:    Union[str, Any]						, A						:    Optional[Any]=7						, A						:    Union[str, Any]=3						, A						:    Optional[int]=18						, A						:    Dict=30						, A						:    int=400						, A						:    Optional[int]=True						, A						:    str=None						, A						:    List[str]=2						, A						:    Optional[int]=True						, A						:    List[str]=[0.5, 0.5, 0.5]						, A						:    Union[str, Any]=[0.5, 0.5, 0.5]						, ):
             _UpperCAmelCase					:  List[Any]											=   size if size is not None else {"shortest_edge": 30}
             _UpperCAmelCase					:  str											=   parent
             _UpperCAmelCase					:  List[str]											=   batch_size
             _UpperCAmelCase					:  Optional[int]											=   num_channels
             _UpperCAmelCase					:  List[Any]											=   image_size
             _UpperCAmelCase					:  List[Any]											=   min_resolution
             _UpperCAmelCase					:  Any											=   max_resolution
             _UpperCAmelCase					:  List[str]											=   do_resize
             _UpperCAmelCase					:  Dict											=   size
             _UpperCAmelCase					:  Dict											=   size_divisor
             _UpperCAmelCase					:  List[Any]											=   do_normalize
             _UpperCAmelCase					:  Tuple											=   image_mean
             _UpperCAmelCase					:  Any											=   image_std




      def 					_A     (	self						:    Union[str, 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 					_A     (	self						:    Dict						, A						:    Tuple						, A						:    List[Any]=False	):
             if not batched:
                    _UpperCAmelCase					:  List[str]											=   self.size["shortest_edge"]
                    _UpperCAmelCase					:  Dict											=   image_inputs[0]
                    if isinstance(__UpperCamelCase						, Image.Image	):
                           _UpperCAmelCase				,  _UpperCAmelCase					:  List[str]											=   image.size
                    else:
                           _UpperCAmelCase				,  _UpperCAmelCase					:  List[str]											=   image.shape[1], image.shape[2]
                    _UpperCAmelCase					:  List[str]											=   size / min(__UpperCamelCase						, __UpperCamelCase	)
                    if h < w:
                           _UpperCAmelCase				,  _UpperCAmelCase					:  Optional[int]											=   size, scale * w
                    else:
                           _UpperCAmelCase				,  _UpperCAmelCase					:  List[Any]											=   scale * h, size

                    _UpperCAmelCase					:  Optional[int]											=   int((1333 / 800) * size	)
                    if max(__UpperCamelCase						, __UpperCamelCase	) > max_size:
                           _UpperCAmelCase					:  List[str]											=   max_size / max(__UpperCamelCase						, __UpperCamelCase	)
                           _UpperCAmelCase					:  Dict											=   newh * scale
                           _UpperCAmelCase					:  Any											=   neww * scale

                    _UpperCAmelCase				,  _UpperCAmelCase					:  str											=   int(newh + 0.5	), int(neww + 0.5	)
                    _UpperCAmelCase				,  _UpperCAmelCase					:  Any											=   (
                        newh // self.size_divisor * self.size_divisor,
                        neww // self.size_divisor * self.size_divisor,
                    )

             else:
                    _UpperCAmelCase					:  Optional[Any]											=   []
                    for image in image_inputs:
                           _UpperCAmelCase				,  _UpperCAmelCase					:  Optional[int]											=   self.get_expected_values([image]	)
                           expected_values.append((expected_height, expected_width)	)
                    _UpperCAmelCase					:  Optional[int]											=   max(__UpperCamelCase						, key=lambda A	: item[0]	)[0]
                    _UpperCAmelCase					:  Dict											=   max(__UpperCamelCase						, key=lambda A	: item[1]	)[1]

             return expected_height, expected_width

@require_torch
@require_vision
class      lowerCamelCase_     (UpperCAmelCase_							,			unittest.TestCase   ):
      '''simple docstring'''

      __UpperCamelCase:  Dict      =						ViltImageProcessor if is_vision_available() else None




      def 					_A     (	self						:    str	):
             _UpperCAmelCase					:  Dict											=   ViltImageProcessingTester(self	)




      @property
      def 					_A     (	self						:    Dict	):
             return self.image_processor_tester.prepare_image_processor_dict()




      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase					:  Tuple											=   self.image_processing_class(**self.image_processor_dict	)
             self.assertTrue(hasattr(__UpperCamelCase						, "image_mean"	)	)
             self.assertTrue(hasattr(__UpperCamelCase						, "image_std"	)	)
             self.assertTrue(hasattr(__UpperCamelCase						, "do_normalize"	)	)
             self.assertTrue(hasattr(__UpperCamelCase						, "do_resize"	)	)
             self.assertTrue(hasattr(__UpperCamelCase						, "size"	)	)
             self.assertTrue(hasattr(__UpperCamelCase						, "size_divisor"	)	)




      def 					_A     (	self						:    str	):
             _UpperCAmelCase					:  List[Any]											=   self.image_processing_class.from_dict(self.image_processor_dict	)
             self.assertEqual(image_processor.size						, {"shortest_edge": 30}	)

             _UpperCAmelCase					:  Tuple											=   self.image_processing_class.from_dict(self.image_processor_dict						, size=42	)
             self.assertEqual(image_processor.size						, {"shortest_edge": 42}	)




      def 					_A     (	self						:    str	):
             pass




      def 					_A     (	self						:    Tuple	):
             # Initialize image_processing
             _UpperCAmelCase					:  Union[str, Any]											=   self.image_processing_class(**self.image_processor_dict	)
             # create random PIL images
             _UpperCAmelCase					:  Dict											=   prepare_image_inputs(self.image_processor_tester						, equal_resolution=__UpperCamelCase	)
             for image in image_inputs:
                    self.assertIsInstance(__UpperCamelCase						, Image.Image	)

             # Test not batched input
             _UpperCAmelCase					:  List[Any]											=   image_processing(image_inputs[0]						, return_tensors="pt"	).pixel_values

             _UpperCAmelCase				,  _UpperCAmelCase					:  int											=   self.image_processor_tester.get_expected_values(__UpperCamelCase	)
             self.assertEqual(
                 encoded_images.shape						, (1, self.image_processor_tester.num_channels, expected_height, expected_width)						, )

             # Test batched
             _UpperCAmelCase					:  Tuple											=   image_processing(__UpperCamelCase						, return_tensors="pt"	).pixel_values

             _UpperCAmelCase				,  _UpperCAmelCase					:  int											=   self.image_processor_tester.get_expected_values(__UpperCamelCase						, batched=__UpperCamelCase	)
             self.assertEqual(
                 encoded_images.shape						, (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )						, )




      def 					_A     (	self						:    Tuple	):
             # Initialize image_processing
             _UpperCAmelCase					:  Dict											=   self.image_processing_class(**self.image_processor_dict	)
             # create random numpy tensors
             _UpperCAmelCase					:  List[Any]											=   prepare_image_inputs(self.image_processor_tester						, equal_resolution=__UpperCamelCase						, numpify=__UpperCamelCase	)
             for image in image_inputs:
                    self.assertIsInstance(__UpperCamelCase						, np.ndarray	)

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

             _UpperCAmelCase				,  _UpperCAmelCase					:  Tuple											=   self.image_processor_tester.get_expected_values(__UpperCamelCase	)
             self.assertEqual(
                 encoded_images.shape						, (1, self.image_processor_tester.num_channels, expected_height, expected_width)						, )

             # Test batched
             _UpperCAmelCase					:  Tuple											=   image_processing(__UpperCamelCase						, return_tensors="pt"	).pixel_values

             _UpperCAmelCase				,  _UpperCAmelCase					:  Union[str, Any]											=   self.image_processor_tester.get_expected_values(__UpperCamelCase						, batched=__UpperCamelCase	)
             self.assertEqual(
                 encoded_images.shape						, (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )						, )





      def 					_A     (	self						:    Union[str, Any]	):
             # Initialize image_processing
             _UpperCAmelCase					:  Optional[int]											=   self.image_processing_class(**self.image_processor_dict	)
             # create random PyTorch tensors
             _UpperCAmelCase					:  Optional[int]											=   prepare_image_inputs(self.image_processor_tester						, equal_resolution=__UpperCamelCase						, torchify=__UpperCamelCase	)
             for image in image_inputs:
                    self.assertIsInstance(__UpperCamelCase						, torch.Tensor	)

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

             _UpperCAmelCase				,  _UpperCAmelCase					:  List[str]											=   self.image_processor_tester.get_expected_values(__UpperCamelCase	)
             self.assertEqual(
                 encoded_images.shape						, (1, self.image_processor_tester.num_channels, expected_height, expected_width)						, )

             # Test batched
             _UpperCAmelCase					:  List[Any]											=   image_processing(__UpperCamelCase						, return_tensors="pt"	).pixel_values

             _UpperCAmelCase				,  _UpperCAmelCase					:  Union[str, Any]											=   self.image_processor_tester.get_expected_values(__UpperCamelCase						, batched=__UpperCamelCase	)
             self.assertEqual(
                 encoded_images.shape						, (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )						, )


'''simple docstring'''




from itertools import compress, repeat
from math import ceil, sqrt

def      UpperCamelCase_							( _UpperCAmelCase  :				int   )   ->   list[int]:


       """simple docstring"""







       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       _UpperCAmelCase					:  Optional[int]											=   bytearray(b"\x01"   ) * ((num >> 1) - 1)

       for i in range(3      ,     int(sqrt(_UpperCAmelCase   )   ) + 1      ,     2   ):
              if sieve[(i >> 1) - 1]:
                     _UpperCAmelCase					:  List[Any]											=   i**2
                     _UpperCAmelCase					:  Optional[int]											=   repeat(
                         0      ,     ceil((num - i_squared) / (i << 1)   )   )

       return [2] + list(compress(range(3      ,     _UpperCAmelCase      ,     2   )      ,     _UpperCAmelCase   )   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

