





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




 def 				_snake_case (						self		)     ->				Dict:
     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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=False		)     ->				Union[str, Any]:
     if not batched:
         _lowerCAmelCase						      =   image_inputs[0]
         if isinstance(_lowerCAmelCase      ,  Image.Image		):
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   image.size
         else:
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   image.shape[1], image.shape[2]
         if w < h:
             _lowerCAmelCase						      =   int(self.size["shortest_edge"] * h / w		)
             _lowerCAmelCase						      =   self.size["shortest_edge"]
         elif w > h:
             _lowerCAmelCase						      =   self.size["shortest_edge"]
             _lowerCAmelCase						      =   int(self.size["shortest_edge"] * w / h		)
         else:
             _lowerCAmelCase						      =   self.size["shortest_edge"]
             _lowerCAmelCase						      =   self.size["shortest_edge"]

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

     return expected_height, expected_width







@require_torch
@require_vision
class  lowerCAmelCase_		(		__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Union[str, Any]								=       ConditionalDetrImageProcessor if is_vision_available() else None




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   ConditionalDetrImageProcessingTester(self		)




 @property
 def 				_snake_case (						self		)     ->				str:
     return self.image_processor_tester.prepare_image_processor_dict()




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   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		)

     _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				Tuple:
     pass




 def 				_snake_case (						self		)     ->				List[str]:
     # Initialize image_processing
     _lowerCAmelCase						      =   self.image_processing_class(**self.image_processor_dict		)
     # create random PIL images
     _lowerCAmelCase						      =   prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=_lowerCAmelCase		)
     for image in image_inputs:
         self.assertIsInstance(_lowerCAmelCase      ,  Image.Image		)

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

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   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
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.image_processor_tester.get_expected_values(_lowerCAmelCase      ,  batched=_lowerCAmelCase		)

     _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				Optional[Any]:
     # Initialize image_processing
     _lowerCAmelCase						      =   self.image_processing_class(**self.image_processor_dict		)
     # create random numpy tensors
     _lowerCAmelCase						      =   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
     _lowerCAmelCase						      =   image_processing(image_inputs[0]      ,  return_tensors="pt"		).pixel_values

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   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
     _lowerCAmelCase						      =   image_processing(_lowerCAmelCase      ,  return_tensors="pt"		).pixel_values

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				Optional[Any]:
     # Initialize image_processing
     _lowerCAmelCase						      =   self.image_processing_class(**self.image_processor_dict		)
     # create random PyTorch tensors
     _lowerCAmelCase						      =   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
     _lowerCAmelCase						      =   image_processing(image_inputs[0]      ,  return_tensors="pt"		).pixel_values

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   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
     _lowerCAmelCase						      =   image_processing(_lowerCAmelCase      ,  return_tensors="pt"		).pixel_values

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				str:
     # prepare image and target
     _lowerCAmelCase						      =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
     with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"      ,  "r"		) as f:
         _lowerCAmelCase						      =   json.loads(f.read()		)

     _lowerCAmelCase						      =   {"image_id": 39769, "annotations": target}

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

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

     _lowerCAmelCase						      =   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
     _lowerCAmelCase						      =   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,  _lowerCAmelCase		)		)
     # verify boxes
     _lowerCAmelCase						      =   torch.Size([6, 4]		)
     self.assertEqual(encoding["labels"][0]["boxes"].shape      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   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
     _lowerCAmelCase						      =   torch.tensor([39769]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,  _lowerCAmelCase		)		)
     # verify is_crowd
     _lowerCAmelCase						      =   torch.tensor([0, 0, 0, 0, 0, 0]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,  _lowerCAmelCase		)		)
     # verify class_labels
     _lowerCAmelCase						      =   torch.tensor([75, 75, 63, 65, 17, 17]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,  _lowerCAmelCase		)		)
     # verify orig_size
     _lowerCAmelCase						      =   torch.tensor([480, 640]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,  _lowerCAmelCase		)		)
     # verify size
     _lowerCAmelCase						      =   torch.tensor([800, 1066]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,  _lowerCAmelCase		)		)





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

     _lowerCAmelCase						      =   {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

     _lowerCAmelCase						      =   pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"		)

     # encode them
     _lowerCAmelCase						      =   ConditionalDetrImageProcessor(format="coco_panoptic"		)
     _lowerCAmelCase						      =   image_processing(images=_lowerCAmelCase      ,  annotations=_lowerCAmelCase      ,  masks_path=_lowerCAmelCase      ,  return_tensors="pt"		)

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

     _lowerCAmelCase						      =   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
     _lowerCAmelCase						      =   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,  _lowerCAmelCase		)		)
     # verify boxes
     _lowerCAmelCase						      =   torch.Size([6, 4]		)
     self.assertEqual(encoding["labels"][0]["boxes"].shape      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   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
     _lowerCAmelCase						      =   torch.tensor([39769]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,  _lowerCAmelCase		)		)
     # verify is_crowd
     _lowerCAmelCase						      =   torch.tensor([0, 0, 0, 0, 0, 0]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,  _lowerCAmelCase		)		)
     # verify class_labels
     _lowerCAmelCase						      =   torch.tensor([17, 17, 63, 75, 75, 93]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,  _lowerCAmelCase		)		)
     # verify masks
     _lowerCAmelCase						      =   822873
     self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,  _lowerCAmelCase		)
     # verify orig_size
     _lowerCAmelCase						      =   torch.tensor([480, 640]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,  _lowerCAmelCase		)		)
     # verify size
     _lowerCAmelCase						      =   torch.tensor([800, 1066]		)
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,  _lowerCAmelCase		)		)



"""simple docstring"""






import math


def      UpperCAmelCase__			(							lowerCAmelCase__	:int						)					->       bool:
      '''simple docstring'''


      lowercase								=      math.loga(math.sqrt(4 * positive_integer + 1						) / 2 + 1 / 2						)

      return exponent == int(lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:float = 1 / 1_2_3_4_5						)					->       int:
      '''simple docstring'''


      lowercase								=      0
      lowercase								=      0

      lowercase								=      3
      while True:
            lowercase								=      (integer**2 - 1) / 4
            # if candidate is an integer, then there is a partition for k
            if partition_candidate == int(lowerCAmelCase__						):
                  lowercase								=      int(lowerCAmelCase__						)
                  total_partitions += 1
                  if check_partition_perfect(lowerCAmelCase__						):
                        perfect_partitions += 1
            if perfect_partitions > 0:
                  if perfect_partitions / total_partitions < max_proportion:
                        return int(lowerCAmelCase__						)
            integer += 1


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



