import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor







class __snake_case    (						unittest.TestCase		):




   def __init__(   self      :    Any	,     _lowercase      :    List[Any]	,     _lowercase      :    List[Any]=7	,     _lowercase      :    int=3	,     _lowercase      :    str=30	,     _lowercase      :    List[str]=4_00	,     _lowercase      :    Dict=True	,     _lowercase      :    int=None	,     _lowercase      :    List[str]=True	,     _lowercase      :    Optional[Any]=[0.5, 0.5, 0.5]	,     _lowercase      :    Union[str, Any]=[0.5, 0.5, 0.5]	,     _lowercase      :    Tuple=True	,     _lowercase      :    Optional[Any]=1 / 2_55	,     _lowercase      :    str=True	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
     SCREAMING_SNAKE_CASE__      							=				parent
     SCREAMING_SNAKE_CASE__      							=				batch_size
     SCREAMING_SNAKE_CASE__      							=				num_channels
     SCREAMING_SNAKE_CASE__      							=				min_resolution
     SCREAMING_SNAKE_CASE__      							=				max_resolution
     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				do_normalize
     SCREAMING_SNAKE_CASE__      							=				image_mean
     SCREAMING_SNAKE_CASE__      							=				image_std
     SCREAMING_SNAKE_CASE__      							=				do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_pad




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }




   def      __a						(   self      :    List[Any]	,     _lowercase      :    int	,     _lowercase      :    Dict=False				):



     """simple docstring"""






     if not batched:
       SCREAMING_SNAKE_CASE__      							=				image_inputs[0]
       if isinstance(_lowercase	,     Image.Image				):
         SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				image.size
       else:
         SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				image.shape[1], image.shape[2]
       if w < h:
         SCREAMING_SNAKE_CASE__      							=				int(self.size["""shortest_edge"""] * h / w				)
         SCREAMING_SNAKE_CASE__      							=				self.size["""shortest_edge"""]
       elif w > h:
         SCREAMING_SNAKE_CASE__      							=				self.size["""shortest_edge"""]
         SCREAMING_SNAKE_CASE__      							=				int(self.size["""shortest_edge"""] * w / h				)
       else:
         SCREAMING_SNAKE_CASE__      							=				self.size["""shortest_edge"""]
         SCREAMING_SNAKE_CASE__      							=				self.size["""shortest_edge"""]

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

     return expected_height, expected_width







@require_torch
@require_vision
class __snake_case    (						lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							DetaImageProcessor if is_vision_available() else None




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				DetaImageProcessingTester(self				)




   @property
   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     self.assertTrue(hasattr(_lowercase	,     """image_mean"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """image_std"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """do_normalize"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """do_resize"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """do_rescale"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """do_pad"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """size"""				)				)




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class.from_dict(self.image_processor_dict				)
     self.assertEqual(image_processor.size	,     {"""shortest_edge""": 18, """longest_edge""": 13_33}				)
     self.assertEqual(image_processor.do_pad	,     _lowercase				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     pass




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     # create random PIL images
     SCREAMING_SNAKE_CASE__      							=				prepare_image_inputs(self.image_processor_tester	,     equal_resolution=_lowercase				)
     for image in image_inputs:
       self.assertIsInstance(_lowercase	,     Image.Image				)

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

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processor_tester.get_expected_values(_lowercase				)

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

     # Test batched
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processor_tester.get_expected_values(_lowercase	,     batched=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				image_processing(_lowercase	,     return_tensors="""pt"""				).pixel_values
     self.assertEqual(
         encoded_images.shape	,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )	,     )




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__      							=				prepare_image_inputs(self.image_processor_tester	,     equal_resolution=_lowercase	,     numpify=_lowercase				)
     for image in image_inputs:
       self.assertIsInstance(_lowercase	,     np.ndarray				)

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

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processor_tester.get_expected_values(_lowercase				)

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

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

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processor_tester.get_expected_values(_lowercase	,     batched=_lowercase				)

     self.assertEqual(
         encoded_images.shape	,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )	,     )




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__      							=				prepare_image_inputs(self.image_processor_tester	,     equal_resolution=_lowercase	,     torchify=_lowercase				)
     for image in image_inputs:
       self.assertIsInstance(_lowercase	,     torch.Tensor				)

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

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processor_tester.get_expected_values(_lowercase				)

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

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

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.image_processor_tester.get_expected_values(_lowercase	,     batched=_lowercase				)

     self.assertEqual(
         encoded_images.shape	,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )	,     )




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
     with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""	,     """r"""				) as f:
       SCREAMING_SNAKE_CASE__      							=				json.loads(f.read()				)

     SCREAMING_SNAKE_CASE__      							=				{"""image_id""": 3_97_69, """annotations""": target}

     # encode them
     SCREAMING_SNAKE_CASE__      							=				DetaImageProcessor()
     SCREAMING_SNAKE_CASE__      							=				image_processing(images=_lowercase	,     annotations=_lowercase	,     return_tensors="""pt"""				)

     # verify pixel values
     SCREAMING_SNAKE_CASE__      							=				torch.Size([1, 3, 8_00, 10_66]				)
     self.assertEqual(encoding["""pixel_values"""].shape	,     _lowercase				)

     SCREAMING_SNAKE_CASE__      							=				torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]	,     _lowercase	,     atol=1E-4				)				)

     # verify area
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]	,     _lowercase				)				)
     # verify boxes
     SCREAMING_SNAKE_CASE__      							=				torch.Size([6, 4]				)
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,     _lowercase				)
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]	,     _lowercase	,     atol=1E-3				)				)
     # verify image_id
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([3_97_69]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,     _lowercase				)				)
     # verify is_crowd
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([0, 0, 0, 0, 0, 0]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,     _lowercase				)				)
     # verify class_labels
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([75, 75, 63, 65, 17, 17]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,     _lowercase				)				)
     # verify orig_size
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([4_80, 6_40]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,     _lowercase				)				)
     # verify size
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([8_00, 10_66]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,     _lowercase				)				)




   @slow
   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
     with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""	,     """r"""				) as f:
       SCREAMING_SNAKE_CASE__      							=				json.loads(f.read()				)

     SCREAMING_SNAKE_CASE__      							=				{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

     SCREAMING_SNAKE_CASE__      							=				pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

     # encode them
     SCREAMING_SNAKE_CASE__      							=				DetaImageProcessor(format="""coco_panoptic"""				)
     SCREAMING_SNAKE_CASE__      							=				image_processing(images=_lowercase	,     annotations=_lowercase	,     masks_path=_lowercase	,     return_tensors="""pt"""				)

     # verify pixel values
     SCREAMING_SNAKE_CASE__      							=				torch.Size([1, 3, 8_00, 10_66]				)
     self.assertEqual(encoding["""pixel_values"""].shape	,     _lowercase				)

     SCREAMING_SNAKE_CASE__      							=				torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]	,     _lowercase	,     atol=1E-4				)				)

     # verify area
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]	,     _lowercase				)				)
     # verify boxes
     SCREAMING_SNAKE_CASE__      							=				torch.Size([6, 4]				)
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,     _lowercase				)
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]	,     _lowercase	,     atol=1E-3				)				)
     # verify image_id
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([3_97_69]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,     _lowercase				)				)
     # verify is_crowd
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([0, 0, 0, 0, 0, 0]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,     _lowercase				)				)
     # verify class_labels
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([17, 17, 63, 75, 75, 93]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,     _lowercase				)				)
     # verify masks
     SCREAMING_SNAKE_CASE__      							=				82_28_73
     self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()	,     _lowercase				)
     # verify orig_size
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([4_80, 6_40]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,     _lowercase				)				)
     # verify size
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([8_00, 10_66]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,     _lowercase				)				)

from math import isqrt



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> list[int]:







  """simple docstring"""





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

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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int = 10**8     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				calculate_prime_numbers(max_number // 2     )

  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     ) - 1
  while left <= right:
    while prime_numbers[left] * prime_numbers[right] >= max_number:
      right -= 1
    semiprimes_count += right - left + 1
    left += 1

  return semiprimes_count


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