


'''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 ConditionalDetrImageProcessor
class 							__SCREAMING_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 ,   )							->Dict:



         '''simple docstring'''
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         __a				     =    size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
         __a				     =    parent
         __a				     =    batch_size
         __a				     =    num_channels
         __a				     =    min_resolution
         __a				     =    max_resolution
         __a				     =    do_resize
         __a				     =    size
         __a				     =    do_normalize
         __a				     =    image_mean
         __a				     =    image_std
         __a				     =    do_rescale
         __a				     =    rescale_factor
         __a				     =    do_pad




      def  __UpperCamelCase							(  self      )							->int:



         '''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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=False      )							->Union[str, Any]:



         '''simple docstring'''
         if not batched:
            __a				     =    image_inputs[0]
            if isinstance(UpperCAmelCase_ ,   Image.Image      ):
               __a     ,			__a				     =    image.size
            else:
               __a     ,			__a				     =    image.shape[1], image.shape[2]
            if w < h:
               __a				     =    int(self.size['shortest_edge'] * h / w      )
               __a				     =    self.size['shortest_edge']
            elif w > h:
               __a				     =    self.size['shortest_edge']
               __a				     =    int(self.size['shortest_edge'] * w / h      )
            else:
               __a				     =    self.size['shortest_edge']
               __a				     =    self.size['shortest_edge']

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

         return expected_height, expected_width







@require_torch
@require_vision
class 							__SCREAMING_SNAKE_CASE       (							__UpperCAmelCase      ,      unittest.TestCase     ):
      __a  					=ConditionalDetrImageProcessor if is_vision_available() else None




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    ConditionalDetrImageProcessingTester(self      )




      @property
      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         return self.image_processor_tester.prepare_image_processor_dict()




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(UpperCAmelCase_ ,   'image_mean'      )      )
         self.assertTrue(hasattr(UpperCAmelCase_ ,   'image_std'      )      )
         self.assertTrue(hasattr(UpperCAmelCase_ ,   'do_normalize'      )      )
         self.assertTrue(hasattr(UpperCAmelCase_ ,   'do_resize'      )      )
         self.assertTrue(hasattr(UpperCAmelCase_ ,   'size'      )      )




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    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 ,   UpperCAmelCase_      )

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




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         # 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=UpperCAmelCase_      )
         for image in image_inputs:
            self.assertIsInstance(UpperCAmelCase_ ,   Image.Image      )

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

         __a     ,			__a				     =    self.image_processor_tester.get_expected_values(UpperCAmelCase_      )

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

         # Test batched
         __a     ,			__a				     =    self.image_processor_tester.get_expected_values(UpperCAmelCase_ ,   batched=UpperCAmelCase_      )

         __a				     =    image_processing(UpperCAmelCase_ ,   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      )							->List[str]:



         '''simple docstring'''
         # 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=UpperCAmelCase_ ,   numpify=UpperCAmelCase_      )
         for image in image_inputs:
            self.assertIsInstance(UpperCAmelCase_ ,   np.ndarray      )

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

         __a     ,			__a				     =    self.image_processor_tester.get_expected_values(UpperCAmelCase_      )

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

         # Test batched
         __a				     =    image_processing(UpperCAmelCase_ ,   return_tensors='pt'      ).pixel_values

         __a     ,			__a				     =    self.image_processor_tester.get_expected_values(UpperCAmelCase_ ,   batched=UpperCAmelCase_      )

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




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         # 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=UpperCAmelCase_ ,   torchify=UpperCAmelCase_      )
         for image in image_inputs:
            self.assertIsInstance(UpperCAmelCase_ ,   torch.Tensor      )

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

         __a     ,			__a				     =    self.image_processor_tester.get_expected_values(UpperCAmelCase_      )

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

         # Test batched
         __a				     =    image_processing(UpperCAmelCase_ ,   return_tensors='pt'      ).pixel_values

         __a     ,			__a				     =    self.image_processor_tester.get_expected_values(UpperCAmelCase_ ,   batched=UpperCAmelCase_      )

         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      )							->int:



         '''simple docstring'''
         # prepare image and target
         __a				     =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' ,   'r'      ) as f:
            __a				     =    json.loads(f.read()      )

         __a				     =    {'image_id': 3_9769, 'annotations': target}

         # encode them
         __a				     =    ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'      )
         __a				     =    image_processing(images=UpperCAmelCase_ ,   annotations=UpperCAmelCase_ ,   return_tensors='pt'      )

         # verify pixel values
         __a				     =    torch.Size([1, 3, 800, 1066]      )
         self.assertEqual(encoding['pixel_values'].shape ,   UpperCAmelCase_      )

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

         # verify area
         __a				     =    torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,   UpperCAmelCase_      )      )
         # verify boxes
         __a				     =    torch.Size([6, 4]      )
         self.assertEqual(encoding['labels'][0]['boxes'].shape ,   UpperCAmelCase_      )
         __a				     =    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,   UpperCAmelCase_ ,   atol=1e-3      )      )
         # verify image_id
         __a				     =    torch.tensor([3_9769]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,   UpperCAmelCase_      )      )
         # verify is_crowd
         __a				     =    torch.tensor([0, 0, 0, 0, 0, 0]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,   UpperCAmelCase_      )      )
         # verify class_labels
         __a				     =    torch.tensor([75, 75, 63, 65, 17, 17]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,   UpperCAmelCase_      )      )
         # verify orig_size
         __a				     =    torch.tensor([480, 640]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,   UpperCAmelCase_      )      )
         # verify size
         __a				     =    torch.tensor([800, 1066]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,   UpperCAmelCase_      )      )






      @slow
      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         # prepare image, target and masks_path
         __a				     =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' ,   'r'      ) as f:
            __a				     =    json.loads(f.read()      )

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

         __a				     =    pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'      )

         # encode them
         __a				     =    ConditionalDetrImageProcessor(format='coco_panoptic'      )
         __a				     =    image_processing(images=UpperCAmelCase_ ,   annotations=UpperCAmelCase_ ,   masks_path=UpperCAmelCase_ ,   return_tensors='pt'      )

         # verify pixel values
         __a				     =    torch.Size([1, 3, 800, 1066]      )
         self.assertEqual(encoding['pixel_values'].shape ,   UpperCAmelCase_      )

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

         # verify area
         __a				     =    torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,   UpperCAmelCase_      )      )
         # verify boxes
         __a				     =    torch.Size([6, 4]      )
         self.assertEqual(encoding['labels'][0]['boxes'].shape ,   UpperCAmelCase_      )
         __a				     =    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,   UpperCAmelCase_ ,   atol=1e-3      )      )
         # verify image_id
         __a				     =    torch.tensor([3_9769]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,   UpperCAmelCase_      )      )
         # verify is_crowd
         __a				     =    torch.tensor([0, 0, 0, 0, 0, 0]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,   UpperCAmelCase_      )      )
         # verify class_labels
         __a				     =    torch.tensor([17, 17, 63, 75, 75, 93]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,   UpperCAmelCase_      )      )
         # verify masks
         __a				     =    82_2873
         self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,   UpperCAmelCase_      )
         # verify orig_size
         __a				     =    torch.tensor([480, 640]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,   UpperCAmelCase_      )      )
         # verify size
         __a				     =    torch.tensor([800, 1066]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,   UpperCAmelCase_      )      )







'''simple docstring'''



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	bool = False    )  ->			bool:

   """simple docstring"""
   if n == 2:
      return True
   if not n % 2 or n < 2:
      return False
   if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
      return False
   if n > 3317044064679887385961981 and not allow_probable:
      raise ValueError(
          'Warning: upper bound of deterministic test is exceeded. '
          'Pass allow_probable=True to allow probabilistic test. '
          'A return value of True indicates a probable prime.'    )
   # array bounds provided by analysis
   __a				     =    [
       2047,
       1373653,
       25326001,
       3215031751,
       2152302898747,
       3474749660383,
       341550071728321,
       1,
       3825123056546413051,
       1,
       1,
       318665857834031151167461,
       3317044064679887385961981,
   ]

   __a				     =    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
   for idx, _p in enumerate(SCREAMING_SNAKE_CASE__,					1    ):
      if n < _p:
         # then we have our last prime to check
         __a				     =    primes[:idx]
         break
   __a     ,			__a				     =    n - 1, 0
   # break up n -1 into a power of 2 (s) and
   # remaining odd component
   # essentially, solve for d * 2 ** s == n - 1
   while d % 2 == 0:
      d //= 2
      s += 1
   for prime in plist:
      __a				     =    False
      for r in range(SCREAMING_SNAKE_CASE__    ):
         __a				     =    pow(SCREAMING_SNAKE_CASE__,					d * 2**r,					SCREAMING_SNAKE_CASE__    )
         # see article for analysis explanation for m
         if (r == 0 and m == 1) or ((m + 1) % n == 0):
            __a				     =    True
            # this loop will not determine compositeness
            break
      if pr:
         continue
      # if pr is False, then the above loop never evaluated to true,
      # and the n MUST be composite
      return False
   return True



def    __UpperCAmelCase	(				)  ->			None:

   """simple docstring"""
   assert not miller_rabin(561    )
   assert miller_rabin(563    )
   # 2047

   assert not miller_rabin(838201    )
   assert miller_rabin(838207    )
   # 1_373_653

   assert not miller_rabin(17316001    )
   assert miller_rabin(17316017    )
   # 25_326_001

   assert not miller_rabin(3078386641    )
   assert miller_rabin(3078386653    )
   # 3_215_031_751

   assert not miller_rabin(1713045574801    )
   assert miller_rabin(1713045574819    )
   # 2_152_302_898_747

   assert not miller_rabin(2779799728307    )
   assert miller_rabin(2779799728327    )
   # 3_474_749_660_383

   assert not miller_rabin(113850023909441    )
   assert miller_rabin(113850023909527    )
   # 341_550_071_728_321

   assert not miller_rabin(1275041018848804351    )
   assert miller_rabin(1275041018848804391    )
   # 3_825_123_056_546_413_051

   assert not miller_rabin(79666464458507787791867    )
   assert miller_rabin(79666464458507787791951    )
   # 318_665_857_834_031_151_167_461

   assert not miller_rabin(552840677446647897660333    )
   assert miller_rabin(552840677446647897660359    )
   # 3_317_044_064_679_887_385_961_981
   # upper limit for probabilistic test


if __name__ == "__main__":
 test_miller_rabin()