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 EfficientNetImageProcessor







class        UpperCAmelCase_					(				unittest.TestCase     ):




    def __init__(       self	: Optional[int]   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: Any=13   ,   lowerCAmelCase	: Dict=3   ,   lowerCAmelCase	: Union[str, Any]=18   ,   lowerCAmelCase	: str=30   ,   lowerCAmelCase	: List[Any]=400   ,   lowerCAmelCase	: List[str]=True   ,   lowerCAmelCase	: List[Any]=None   ,   lowerCAmelCase	: List[str]=True   ,   lowerCAmelCase	: Optional[int]=[0.5, 0.5, 0.5]   ,   lowerCAmelCase	: Union[str, Any]=[0.5, 0.5, 0.5]   ,   )						->			Tuple:
      a__  		=  size if size is not None else {"height": 18, "width": 18}
      a__  		=  parent
      a__  		=  batch_size
      a__  		=  num_channels
      a__  		=  image_size
      a__  		=  min_resolution
      a__  		=  max_resolution
      a__  		=  do_resize
      a__  		=  size
      a__  		=  do_normalize
      a__  		=  image_mean
      a__  		=  image_std




    def     __lowerCamelCase			(       self	: Optional[Any] )						->			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,
      }







@require_torch
@require_vision
class        UpperCAmelCase_					(				_a  ,  unittest.TestCase     ):
    UpperCAmelCase_      :							Optional[Any]    			=				EfficientNetImageProcessor if is_vision_available() else None




    def     __lowerCamelCase			(       self	: str )						->			Any:
      a__  		=  EfficientNetImageProcessorTester(self )




    @property
    def     __lowerCamelCase			(       self	: Tuple )						->			Optional[int]:
      return self.image_processor_tester.prepare_image_processor_dict()




    def     __lowerCamelCase			(       self	: List[str] )						->			str:
      a__  		=  self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(_A   ,   "image_mean" ) )
      self.assertTrue(hasattr(_A   ,   "image_std" ) )
      self.assertTrue(hasattr(_A   ,   "do_normalize" ) )
      self.assertTrue(hasattr(_A   ,   "do_resize" ) )
      self.assertTrue(hasattr(_A   ,   "size" ) )




    def     __lowerCamelCase			(       self	: Optional[Any] )						->			List[Any]:
      a__  		=  self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size   ,   {"height": 18, "width": 18} )

      a__  		=  self.image_processing_class.from_dict(self.image_processor_dict   ,   size=42 )
      self.assertEqual(image_processor.size   ,   {"height": 42, "width": 42} )




    def     __lowerCamelCase			(       self	: int )						->			int:
      # 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=_A )
      for image in image_inputs:
        self.assertIsInstance(_A   ,   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.size["height"],
              self.image_processor_tester.size["width"],
          )   ,   )

      # Test batched
      a__  		=  image_processing(_A   ,   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.size["height"],
              self.image_processor_tester.size["width"],
          )   ,   )




    def     __lowerCamelCase			(       self	: Tuple )						->			str:
      # 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=_A   ,   numpify=_A )
      for image in image_inputs:
        self.assertIsInstance(_A   ,   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.size["height"],
              self.image_processor_tester.size["width"],
          )   ,   )

      # Test batched
      a__  		=  image_processing(_A   ,   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.size["height"],
              self.image_processor_tester.size["width"],
          )   ,   )




    def     __lowerCamelCase			(       self	: Optional[int] )						->			List[Any]:
      # 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=_A   ,   torchify=_A )
      for image in image_inputs:
        self.assertIsInstance(_A   ,   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.size["height"],
              self.image_processor_tester.size["width"],
          )   ,   )

      # Test batched
      a__  		=  image_processing(_A   ,   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.size["height"],
              self.image_processor_tester.size["width"],
          )   ,   )






from itertools import compress, repeat
from math import ceil, sqrt
def 						snake_case__							(       __lowercase							)		->		list[int]:







						"""simple docstring"""

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

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

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

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()