


'''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 MobileViTImageProcessor
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=7    ,   UpperCamelCase_=3    ,   UpperCamelCase_=18    ,   UpperCamelCase_=30    ,   UpperCamelCase_=400    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   UpperCamelCase_=True    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= size if size is not None else {'''shortest_edge''': 20}
							UpperCamelCase__   :Tuple												= crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							UpperCamelCase__   :Optional[Any]												= parent
							UpperCamelCase__   :str												= batch_size
							UpperCamelCase__   :Dict												= num_channels
							UpperCamelCase__   :Optional[Any]												= image_size
							UpperCamelCase__   :Optional[int]												= min_resolution
							UpperCamelCase__   :Union[str, Any]												= max_resolution
							UpperCamelCase__   :Optional[int]												= do_resize
							UpperCamelCase__   :str												= size
							UpperCamelCase__   :Union[str, Any]												= do_center_crop
							UpperCamelCase__   :str												= crop_size
							UpperCamelCase__   :Tuple												= do_flip_channel_order




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_flip_channel_order": self.do_flip_channel_order,
							}



@require_torch
@require_vision
class    lowercase	(							lowerCAmelCase_	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				MobileViTImageProcessor if is_vision_available() else None




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= MobileViTImageProcessingTester(self							)




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(__lowerCAmelCase    ,   '''do_resize'''							)							)
							self.assertTrue(hasattr(__lowerCAmelCase    ,   '''size'''							)							)
							self.assertTrue(hasattr(__lowerCAmelCase    ,   '''do_center_crop'''							)							)
							self.assertTrue(hasattr(__lowerCAmelCase    ,   '''center_crop'''							)							)
							self.assertTrue(hasattr(__lowerCAmelCase    ,   '''do_flip_channel_order'''							)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.image_processing_class.from_dict(self.image_processor_dict							)
							self.assertEqual(image_processor.size    ,   {'''shortest_edge''': 20}							)
							self.assertEqual(image_processor.crop_size    ,   {'''height''': 18, '''width''': 18}							)

							UpperCamelCase__   :str												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							pass




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__   :Optional[int]												= prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=__lowerCAmelCase							)
							for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase    ,   Image.Image							)

							# Test not batched input
							UpperCamelCase__   :List[str]												= 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
							UpperCamelCase__   :Tuple												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							UpperCamelCase__   :Any												= 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
							UpperCamelCase__   :List[str]												= 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
							UpperCamelCase__   :str												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							UpperCamelCase__   :Tuple												= 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
							UpperCamelCase__   :Tuple												= 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
							UpperCamelCase__   :Tuple												= 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'''],
							    )    ,   )
"""simple docstring"""


import math






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					2
  _UpperCAmelCase         =					int(math.sqrt(lowercase						)						)  # Size of every segment
  _UpperCAmelCase         =					[True] * (end + 1)
  _UpperCAmelCase         =					[]

  while start <= end:
    if temp[start] is True:
      in_prime.append(lowercase						)
      for i in range(start * start      ,end + 1      ,lowercase						):
        _UpperCAmelCase         =					False
    start += 1
  prime += in_prime

  _UpperCAmelCase         =					end + 1
  _UpperCAmelCase         =					min(2 * end      ,lowercase						)

  while low <= n:
    _UpperCAmelCase         =					[True] * (high - low + 1)
    for each in in_prime:
      _UpperCAmelCase         =					math.floor(low / each						) * each
      if t < low:
        t += each

      for j in range(lowercase      ,high + 1      ,lowercase						):
        _UpperCAmelCase         =					False

    for j in range(len(lowercase						)						):
      if temp[j] is True:
        prime.append(j + low						)

    _UpperCAmelCase         =					high + 1
    _UpperCAmelCase         =					min(high + end      ,lowercase						)

  return prime

print(sieve(1_0**6))
