






"""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    __lowerCamelCase    (  unittest.TestCase  ):

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       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 ,				snake_case_ ,				snake_case_=False      )		->       int:
       if not batched:
           UpperCamelCase__         =    image_inputs[0]
           if isinstance(snake_case_ ,				Image.Image      ):
               UpperCamelCase__       ,						UpperCamelCase__         =    image.size
           else:
               UpperCamelCase__       ,						UpperCamelCase__         =    image.shape[1], image.shape[2]
           if w < h:
               UpperCamelCase__         =    int(self.size['shortest_edge'] * h / w      )
               UpperCamelCase__         =    self.size['shortest_edge']
           elif w > h:
               UpperCamelCase__         =    self.size['shortest_edge']
               UpperCamelCase__         =    int(self.size['shortest_edge'] * w / h      )
           else:
               UpperCamelCase__         =    self.size['shortest_edge']
               UpperCamelCase__         =    self.size['shortest_edge']

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

       return expected_height, expected_width







@require_torch
@require_vision
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Dict 						=ConditionalDetrImageProcessor if is_vision_available() else None

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    ConditionalDetrImageProcessingTester(self      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       return self.image_processor_tester.prepare_image_processor_dict()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(snake_case_ ,				'image_mean'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'image_std'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'do_normalize'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'do_resize'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'size'      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    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 ,				snake_case_      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       pass

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       # Initialize image_processing
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       UpperCamelCase__         =    prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case_      )
       for image in image_inputs:
           self.assertIsInstance(snake_case_ ,				Image.Image      )

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

       UpperCamelCase__       ,						UpperCamelCase__         =    self.image_processor_tester.get_expected_values(snake_case_      )

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

       # Test batched
       UpperCamelCase__       ,						UpperCamelCase__         =    self.image_processor_tester.get_expected_values(snake_case_ ,				batched=snake_case_      )

       UpperCamelCase__         =    image_processing(snake_case_ ,				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      )		->       int:
       # Initialize image_processing
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       UpperCamelCase__         =    prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case_ ,				numpify=snake_case_      )
       for image in image_inputs:
           self.assertIsInstance(snake_case_ ,				np.ndarray      )

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

       UpperCamelCase__       ,						UpperCamelCase__         =    self.image_processor_tester.get_expected_values(snake_case_      )

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

       # Test batched
       UpperCamelCase__         =    image_processing(snake_case_ ,				return_tensors='pt'      ).pixel_values

       UpperCamelCase__       ,						UpperCamelCase__         =    self.image_processor_tester.get_expected_values(snake_case_ ,				batched=snake_case_      )

       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      )		->       Any:
       # Initialize image_processing
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       UpperCamelCase__         =    prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case_ ,				torchify=snake_case_      )
       for image in image_inputs:
           self.assertIsInstance(snake_case_ ,				torch.Tensor      )

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

       UpperCamelCase__       ,						UpperCamelCase__         =    self.image_processor_tester.get_expected_values(snake_case_      )

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

       # Test batched
       UpperCamelCase__         =    image_processing(snake_case_ ,				return_tensors='pt'      ).pixel_values

       UpperCamelCase__       ,						UpperCamelCase__         =    self.image_processor_tester.get_expected_values(snake_case_ ,				batched=snake_case_      )

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

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

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

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

       UpperCamelCase__         =    torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,				snake_case_ ,				atol=1E-4      )      )

       # verify area
       UpperCamelCase__         =    torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,				snake_case_      )      )
       # verify boxes
       UpperCamelCase__         =    torch.Size([6, 4]      )
       self.assertEqual(encoding['labels'][0]['boxes'].shape ,				snake_case_      )
       UpperCamelCase__         =    torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,				snake_case_ ,				atol=1E-3      )      )
       # verify image_id
       UpperCamelCase__         =    torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,				snake_case_      )      )
       # verify is_crowd
       UpperCamelCase__         =    torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,				snake_case_      )      )
       # verify class_labels
       UpperCamelCase__         =    torch.tensor([75, 75, 63, 65, 17, 17]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,				snake_case_      )      )
       # verify orig_size
       UpperCamelCase__         =    torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,				snake_case_      )      )
       # verify size
       UpperCamelCase__         =    torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,				snake_case_      )      )







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

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

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

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

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

       UpperCamelCase__         =    torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,				snake_case_ ,				atol=1E-4      )      )

       # verify area
       UpperCamelCase__         =    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,				snake_case_      )      )
       # verify boxes
       UpperCamelCase__         =    torch.Size([6, 4]      )
       self.assertEqual(encoding['labels'][0]['boxes'].shape ,				snake_case_      )
       UpperCamelCase__         =    torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,				snake_case_ ,				atol=1E-3      )      )
       # verify image_id
       UpperCamelCase__         =    torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,				snake_case_      )      )
       # verify is_crowd
       UpperCamelCase__         =    torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,				snake_case_      )      )
       # verify class_labels
       UpperCamelCase__         =    torch.tensor([17, 17, 63, 75, 75, 93]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,				snake_case_      )      )
       # verify masks
       UpperCamelCase__         =    82_2873
       self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,				snake_case_      )
       # verify orig_size
       UpperCamelCase__         =    torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,				snake_case_      )      )
       # verify size
       UpperCamelCase__         =    torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,				snake_case_      )      )












"""simple docstring"""





from collections import defaultdict
from math import ceil, sqrt







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 1_00_00_00 ,	SCREAMING_SNAKE_CASE = 10					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    defaultdict(SCREAMING_SNAKE_CASE					)

    for outer_width in range(3 ,	(t_limit // 4) + 2					):
        if outer_width * outer_width > t_limit:
            UpperCamelCase__         =    max(
                ceil(sqrt(outer_width * outer_width - t_limit					)					) ,	1					)
        else:
            UpperCamelCase__         =    1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(SCREAMING_SNAKE_CASE ,	outer_width - 1 ,	2					):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10					)


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




