


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 ConditionalDetrImageProcessor







class 		_snake_case  (      unittest.TestCase							):




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase=7					,  _lowerCamelCase=3					,  _lowerCamelCase=30					,  _lowerCamelCase=400					,  _lowerCamelCase=True					,  _lowerCamelCase=None					,  _lowerCamelCase=True					,  _lowerCamelCase=[0.5, 0.5, 0.5]					,  _lowerCamelCase=[0.5, 0.5, 0.5]					,  _lowerCamelCase=True					,  _lowerCamelCase=1 / 255					,  _lowerCamelCase=True					,  ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       a						:int 							=       size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
       a						:Union[str, Any] 							=       parent
       a						:Dict 							=       batch_size
       a						:Any 							=       num_channels
       a						:Optional[int] 							=       min_resolution
       a						:Dict 							=       max_resolution
       a						:Tuple 							=       do_resize
       a						:Union[str, Any] 							=       size
       a						:Optional[int] 							=       do_normalize
       a						:Tuple 							=       image_mean
       a						:Tuple 							=       image_std
       a						:Any 							=       do_rescale
       a						:Optional[int] 							=       rescale_factor
       a						:Union[str, Any] 							=       do_pad




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=False    ):
       if not batched:
             a						:Optional[Any] 							=       image_inputs[0]
             if isinstance(_lowerCamelCase					,  Image.Image    ):
                   a							,				a						:Tuple 							=       image.size
             else:
                   a							,				a						:int 							=       image.shape[1], image.shape[2]
             if w < h:
                   a						:int 							=       int(self.size['''shortest_edge'''] * h / w    )
                   a						:List[Any] 							=       self.size['''shortest_edge''']
             elif w > h:
                   a						:Optional[Any] 							=       self.size['''shortest_edge''']
                   a						:Union[str, Any] 							=       int(self.size['''shortest_edge'''] * w / h    )
             else:
                   a						:Dict 							=       self.size['''shortest_edge''']
                   a						:Any 							=       self.size['''shortest_edge''']

       else:
             a						:Dict 							=       []
             for image in image_inputs:
                   a							,				a						:Tuple 							=       self.get_expected_values([image]    )
                   expected_values.append((expected_height, expected_width)    )
             a						:Union[str, Any] 							=       max(_lowerCamelCase					,  key=lambda _lowerCamelCase    : item[0]    )[0]
             a						:Tuple 							=       max(_lowerCamelCase					,  key=lambda _lowerCamelCase    : item[1]    )[1]

       return expected_height, expected_width







@require_torch
@require_vision
class 		_snake_case  (      _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       ConditionalDetrImageProcessor if is_vision_available() else None




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       ConditionalDetrImageProcessingTester(self    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return self.image_processor_tester.prepare_image_processor_dict()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''image_mean'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''image_std'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_normalize'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_resize'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''size'''    )    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size					,  {'''shortest_edge''': 18, '''longest_edge''': 1333}    )
       self.assertEqual(image_processor.do_pad					,  _lowerCamelCase    )

       a						:Dict 							=       self.image_processing_class.from_dict(
           self.image_processor_dict					,  size=42					,  max_size=84					,  pad_and_return_pixel_mask=_lowerCamelCase    )
       self.assertEqual(image_processor.size					,  {'''shortest_edge''': 42, '''longest_edge''': 84}    )
       self.assertEqual(image_processor.do_pad					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:Any 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       a						:Tuple 							=       prepare_image_inputs(self.image_processor_tester					,  equal_resolution=_lowerCamelCase    )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase					,  Image.Image    )

       # Test not batched input
       a						:Dict 							=       image_processing(image_inputs[0]					,  return_tensors='''pt'''    ).pixel_values

       a							,				a						:Any 							=       self.image_processor_tester.get_expected_values(_lowerCamelCase    )

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

       # Test batched
       a							,				a						:Optional[int] 							=       self.image_processor_tester.get_expected_values(_lowerCamelCase					,  batched=_lowerCamelCase    )

       a						:int 							=       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,
               expected_height,
               expected_width,
           )					,  )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:Optional[int] 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       a						:Dict 							=       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
       a						:Any 							=       image_processing(image_inputs[0]					,  return_tensors='''pt'''    ).pixel_values

       a							,				a						:str 							=       self.image_processor_tester.get_expected_values(_lowerCamelCase    )

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

       # Test batched
       a						:Dict 							=       image_processing(_lowerCamelCase					,  return_tensors='''pt'''    ).pixel_values

       a							,				a						:Dict 							=       self.image_processor_tester.get_expected_values(_lowerCamelCase					,  batched=_lowerCamelCase    )

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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:str 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       a						:int 							=       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
       a						:Tuple 							=       image_processing(image_inputs[0]					,  return_tensors='''pt'''    ).pixel_values

       a							,				a						:Tuple 							=       self.image_processor_tester.get_expected_values(_lowerCamelCase    )

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

       # Test batched
       a						:List[Any] 							=       image_processing(_lowerCamelCase					,  return_tensors='''pt'''    ).pixel_values

       a							,				a						:List[str] 							=       self.image_processor_tester.get_expected_values(_lowerCamelCase					,  batched=_lowerCamelCase    )

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




 @slow
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # prepare image and target
       a						:Any 							=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''					,  '''r'''    ) as f:
             a						:Any 							=       json.loads(f.read()    )

       a						:str 							=       {'''image_id''': 3_9769, '''annotations''': target}

       # encode them
       a						:int 							=       ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''    )
       a						:Union[str, Any] 							=       image_processing(images=_lowerCamelCase					,  annotations=_lowerCamelCase					,  return_tensors='''pt'''    )

       # verify pixel values
       a						:List[str] 							=       torch.Size([1, 3, 800, 1066]    )
       self.assertEqual(encoding['''pixel_values'''].shape					,  _lowerCamelCase    )

       a						:Optional[int] 							=       torch.tensor([0.2796, 0.3138, 0.3481]    )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]					,  _lowerCamelCase					,  atol=1e-4    )    )

       # verify area
       a						:Tuple 							=       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''']					,  _lowerCamelCase    )    )
       # verify boxes
       a						:Any 							=       torch.Size([6, 4]    )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					,  _lowerCamelCase    )
       a						:str 							=       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					,  _lowerCamelCase					,  atol=1e-3    )    )
       # verify image_id
       a						:Dict 							=       torch.tensor([3_9769]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					,  _lowerCamelCase    )    )
       # verify is_crowd
       a						:Optional[Any] 							=       torch.tensor([0, 0, 0, 0, 0, 0]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					,  _lowerCamelCase    )    )
       # verify class_labels
       a						:List[str] 							=       torch.tensor([75, 75, 63, 65, 17, 17]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					,  _lowerCamelCase    )    )
       # verify orig_size
       a						:Tuple 							=       torch.tensor([480, 640]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					,  _lowerCamelCase    )    )
       # verify size
       a						:Any 							=       torch.tensor([800, 1066]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					,  _lowerCamelCase    )    )




 @slow
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # prepare image, target and masks_path
       a						:List[Any] 							=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''					,  '''r'''    ) as f:
             a						:List[Any] 							=       json.loads(f.read()    )

       a						:Dict 							=       {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

       a						:Tuple 							=       pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''    )

       # encode them
       a						:int 							=       ConditionalDetrImageProcessor(format='''coco_panoptic'''    )
       a						:Optional[Any] 							=       image_processing(images=_lowerCamelCase					,  annotations=_lowerCamelCase					,  masks_path=_lowerCamelCase					,  return_tensors='''pt'''    )

       # verify pixel values
       a						:Optional[Any] 							=       torch.Size([1, 3, 800, 1066]    )
       self.assertEqual(encoding['''pixel_values'''].shape					,  _lowerCamelCase    )

       a						:str 							=       torch.tensor([0.2796, 0.3138, 0.3481]    )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]					,  _lowerCamelCase					,  atol=1e-4    )    )

       # verify area
       a						:Optional[int] 							=       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''']					,  _lowerCamelCase    )    )
       # verify boxes
       a						:int 							=       torch.Size([6, 4]    )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					,  _lowerCamelCase    )
       a						:Any 							=       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					,  _lowerCamelCase					,  atol=1e-3    )    )
       # verify image_id
       a						:int 							=       torch.tensor([3_9769]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					,  _lowerCamelCase    )    )
       # verify is_crowd
       a						:Union[str, Any] 							=       torch.tensor([0, 0, 0, 0, 0, 0]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					,  _lowerCamelCase    )    )
       # verify class_labels
       a						:List[str] 							=       torch.tensor([17, 17, 63, 75, 75, 93]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					,  _lowerCamelCase    )    )
       # verify masks
       a						:Optional[Any] 							=       82_2873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()					,  _lowerCamelCase    )
       # verify orig_size
       a						:Optional[Any] 							=       torch.tensor([480, 640]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					,  _lowerCamelCase    )    )
       # verify size
       a						:int 							=       torch.tensor([800, 1066]    )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					,  _lowerCamelCase    )    )









from maths.prime_factors import prime_factors
def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_ ,  UpperCAmelCase_						):
            a						:Dict 							=       F'''Input value of [number={number}] must be an integer'''
            raise TypeError(UpperCAmelCase_						)
      if number < 1:
            raise ValueError('''Input must be a positive integer'''						)
      return -1 if len(prime_factors(UpperCAmelCase_						)						) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





