
'''simple docstring'''



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    _a				(unittest.TestCase):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__=7       ,							A__=3       ,							A__=30       ,							A__=4_00       ,							A__=True       ,							A__=None       ,							A__=True       ,							A__=[0.5, 0.5, 0.5]       ,							A__=[0.5, 0.5, 0.5]       ,							A__=True       ,							A__=1 / 2_55       ,							A__=True       ,							)    ->			Optional[int]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          _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        UpperCamelCase				(							self      )    ->			List[str]:
          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        UpperCamelCase				(							self       ,							A__       ,							A__=False      )    ->			List[str]:
          if not batched:
             _SCREAMING_SNAKE_CASE      =				image_inputs[0]
             if isinstance(A__       ,							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(A__       ,							key=lambda A__      : item[0]      )[0]
             _SCREAMING_SNAKE_CASE      =				max(A__       ,							key=lambda A__      : item[1]      )[1]

          return expected_height, expected_width






@require_torch
@require_vision
class    _a				(_lowerCamelCase  ,					unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  DetaImageProcessor if is_vision_available() else None






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				DetaImageProcessingTester(self      )






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






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				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__       ,							"""do_rescale"""      )      )
          self.assertTrue(hasattr(A__       ,							"""do_pad"""      )      )
          self.assertTrue(hasattr(A__       ,							"""size"""      )      )






       def        UpperCamelCase				(							self      )    ->			Dict:
          _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       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			List[str]:
          pass






       def        UpperCamelCase				(							self      )    ->			List[str]:
          # Initialize image_processing
          _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=A__      )
          for image in image_inputs:
             self.assertIsInstance(A__       ,							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(A__      )

          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(A__       ,							batched=A__      )

          _SCREAMING_SNAKE_CASE      =				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,
                  expected_height,
                  expected_width,
              )       ,							)






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          # Initialize image_processing
          _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=A__       ,							numpify=A__      )
          for image in image_inputs:
             self.assertIsInstance(A__       ,							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(A__      )

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

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

          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.image_processor_tester.get_expected_values(A__       ,							batched=A__      )

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






       def        UpperCamelCase				(							self      )    ->			List[Any]:
          # Initialize image_processing
          _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=A__       ,							torchify=A__      )
          for image in image_inputs:
             self.assertIsInstance(A__       ,							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(A__      )

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

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

          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.image_processor_tester.get_expected_values(A__       ,							batched=A__      )

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






       @slow
       def        UpperCamelCase				(							self      )    ->			Tuple:
          # prepare image and target
          _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=A__       ,							annotations=A__       ,							return_tensors="""pt"""      )

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

          _SCREAMING_SNAKE_CASE      =				torch.tensor([0.2796, 0.3138, 0.3481]      )
          self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,							A__       ,							atol=1E-4      )      )

          # verify area
          _SCREAMING_SNAKE_CASE      =				torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,							A__      )      )
          # verify boxes
          _SCREAMING_SNAKE_CASE      =				torch.Size([6, 4]      )
          self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,							A__       ,							atol=1E-3      )      )
          # verify image_id
          _SCREAMING_SNAKE_CASE      =				torch.tensor([3_97_69]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,							A__      )      )
          # verify is_crowd
          _SCREAMING_SNAKE_CASE      =				torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,							A__      )      )
          # verify class_labels
          _SCREAMING_SNAKE_CASE      =				torch.tensor([75, 75, 63, 65, 17, 17]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,							A__      )      )
          # verify orig_size
          _SCREAMING_SNAKE_CASE      =				torch.tensor([4_80, 6_40]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,							A__      )      )
          # verify size
          _SCREAMING_SNAKE_CASE      =				torch.tensor([8_00, 10_66]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,							A__      )      )






       @slow
       def        UpperCamelCase				(							self      )    ->			List[str]:
          # prepare image, target and masks_path
          _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=A__       ,							annotations=A__       ,							masks_path=A__       ,							return_tensors="""pt"""      )

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

          _SCREAMING_SNAKE_CASE      =				torch.tensor([0.2796, 0.3138, 0.3481]      )
          self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,							A__       ,							atol=1E-4      )      )

          # verify area
          _SCREAMING_SNAKE_CASE      =				torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,							A__      )      )
          # verify boxes
          _SCREAMING_SNAKE_CASE      =				torch.Size([6, 4]      )
          self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,							A__       ,							atol=1E-3      )      )
          # verify image_id
          _SCREAMING_SNAKE_CASE      =				torch.tensor([3_97_69]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,							A__      )      )
          # verify is_crowd
          _SCREAMING_SNAKE_CASE      =				torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,							A__      )      )
          # verify class_labels
          _SCREAMING_SNAKE_CASE      =				torch.tensor([17, 17, 63, 75, 75, 93]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,							A__      )      )
          # verify masks
          _SCREAMING_SNAKE_CASE      =				82_28_73
          self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,							A__      )
          # verify orig_size
          _SCREAMING_SNAKE_CASE      =				torch.tensor([4_80, 6_40]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,							A__      )      )
          # verify size
          _SCREAMING_SNAKE_CASE      =				torch.tensor([8_00, 10_66]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,							A__      )      )








'''simple docstring'''



import sys

UpperCamelCase__      :		int       							=  (
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_ = N						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				-sys.maxsize - 1
   for i in range(len(SCREAMING_SNAKE_CASE_						) - 12						):
      _SCREAMING_SNAKE_CASE      =				1
      for j in range(13						):
         product *= int(n[i + j]						)
      if product > largest_product:
         _SCREAMING_SNAKE_CASE      =				product
   return largest_product


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






