"""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 DeformableDetrImageProcessor




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     				=  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 		a__ (				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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=False		):
        if not batched:
               __a     				=  image_inputs[0]
               if isinstance(lowerCamelCase  ,						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(lowerCamelCase  ,						key=lambda lowerCamelCase		: item[0]		)[0]
               __a     				=  max(lowerCamelCase  ,						key=lambda lowerCamelCase		: item[1]		)[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 				snake_case__     (   snake_case_,		unittest.TestCase   ):
 _snake_case   :			int           =		DeformableDetrImageProcessor if is_vision_available() else None


 def 		a__ (				self		):
        __a     				=  DeformableDetrImageProcessingTester(self		)


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


 def 		a__ (				self		):
        __a     				=  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  ,						"do_rescale"		)		)
        self.assertTrue(hasattr(lowerCamelCase  ,						"do_pad"		)		)
        self.assertTrue(hasattr(lowerCamelCase  ,						"size"		)		)


 def 		a__ (				self		):
        __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  ,						lowerCamelCase		)

        __a     				=  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 		a__ (				self		):
        pass


 def 		a__ (				self		):
        # 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=lowerCamelCase		)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase  ,						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(lowerCamelCase		)

        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(lowerCamelCase  ,						batched=lowerCamelCase		)

        __a     				=  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 		a__ (				self		):
        # 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=lowerCamelCase  ,						numpify=lowerCamelCase		)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase  ,						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(lowerCamelCase		)

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

        # Test batched
        __a     				=  image_processing(lowerCamelCase  ,						return_tensors="pt"		).pixel_values

        __a				, __a     				=  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 		a__ (				self		):
        # 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=lowerCamelCase  ,						torchify=lowerCamelCase		)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase  ,						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(lowerCamelCase		)

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

        # Test batched
        __a     				=  image_processing(lowerCamelCase  ,						return_tensors="pt"		).pixel_values

        __a				, __a     				=  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 		a__ (				self		):
        # 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": 39769, "annotations": target}

        # encode them
        __a     				=  DeformableDetrImageProcessor()
        __a     				=  image_processing(images=lowerCamelCase  ,						annotations=lowerCamelCase  ,						return_tensors="pt"		)

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

        __a     				=  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     				=  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
        __a     				=  torch.Size([6, 4]		)
        self.assertEqual(encoding["labels"][0]["boxes"].shape  ,						lowerCamelCase		)
        __a     				=  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     				=  torch.tensor([39769]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,						lowerCamelCase		)		)
        # verify is_crowd
        __a     				=  torch.tensor([0, 0, 0, 0, 0, 0]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,						lowerCamelCase		)		)
        # verify class_labels
        __a     				=  torch.tensor([75, 75, 63, 65, 17, 17]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,						lowerCamelCase		)		)
        # verify orig_size
        __a     				=  torch.tensor([480, 640]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,						lowerCamelCase		)		)
        # verify size
        __a     				=  torch.tensor([800, 1066]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,						lowerCamelCase		)		)






 @slow
 def 		a__ (				self		):
        # 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": 39769, "segments_info": target}

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

        # encode them
        __a     				=  DeformableDetrImageProcessor(format="coco_panoptic"		)
        __a     				=  image_processing(images=lowerCamelCase  ,						annotations=lowerCamelCase  ,						masks_path=lowerCamelCase  ,						return_tensors="pt"		)

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

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

"""simple docstring"""



def 					_lowerCamelCase(					a							):
       __a     				=  len(a							)
       for i in range(1 ,  a							):
              __a     				=  collection[i]
              __a     				=  0
              __a     				=  i - 1

              while low <= high:
                     __a     				=  (low + high) // 2
                     if val < collection[mid]:
                            __a     				=  mid - 1
                     else:
                            __a     				=  mid + 1
              for j in range(a ,  a ,  -1							):
                     __a     				=  collection[j - 1]
              __a     				=  val
       return collection


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:str			=	input("""Enter numbers separated by a comma:\n""").strip()
   SCREAMING_SNAKE_CASE__:Any			=	[int(item) for item in user_input.split(""",""")]
   print(binary_insertion_sort(unsorted))
