




'''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    __lowercase						(  unittest.TestCase ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase=7      ,							UpperCamelCase=3      ,							UpperCamelCase=30      ,							UpperCamelCase=400      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase=[0.5, 0.5, 0.5]      ,							UpperCamelCase=[0.5, 0.5, 0.5]      ,							UpperCamelCase=True      ,							UpperCamelCase=1 / 255      ,							UpperCamelCase=True      ,							)				->    Optional[Any]:
     # 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      )				->    Union[str, 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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=False      )				->    Dict:
     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 UpperCamelCase      : item[0]      )[0]
       __a			=					max(UpperCAmelCase_      ,							key=lambda UpperCamelCase      : item[1]      )[1]

     return expected_height, expected_width





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







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					ConditionalDetrImageProcessingTester(self      )







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     return self.image_processor_tester.prepare_image_processor_dict()







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __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      )				->    Union[str, Any]:
     __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      )				->    Dict:
     pass







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     # 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      )				->    Union[str, Any]:
     # 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      )				->    Optional[int]:
     # 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      )				->    List[str]:
     # 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([0.2_796, 0.3_138, 0.3_481]      )
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,							UpperCAmelCase_      ,							atol=1e-4      )      )

     # verify area
     __a			=					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']      ,							UpperCAmelCase_      )      )
     # verify boxes
     __a			=					torch.Size([6, 4]      )
     self.assertEqual(encoding['labels'][0]['boxes'].shape      ,							UpperCAmelCase_      )
     __a			=					torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
     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      )				->    List[str]:
     # 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([0.2_796, 0.3_138, 0.3_481]      )
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,							UpperCAmelCase_      ,							atol=1e-4      )      )

     # verify area
     __a			=					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']      ,							UpperCAmelCase_      )      )
     # verify boxes
     __a			=					torch.Size([6, 4]      )
     self.assertEqual(encoding['labels'][0]['boxes'].shape      ,							UpperCAmelCase_      )
     __a			=					torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
     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 						SCREAMING_SNAKE_CASE       (      a_	:							int = 10**12			):
  __a			=					1
  __a			=					0

  __a			=					1
  __a			=					1

  while numerator <= 2 * min_total - 1:
    prev_numerator += 2 * numerator
    numerator += 2 * prev_numerator

    prev_denominator += 2 * denominator
    denominator += 2 * prev_denominator

  return (denominator + 1) // 2


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



