
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   __UpperCamelCase      (				unittest.TestCase    ):






     """simple docstring"""







     def __init__(							self				:				int			,		_A				:				List[str]			,		_A				:				Union[str, Any]=7			,		_A				:				Tuple=3			,		_A				:				Optional[Any]=30			,		_A				:				int=400			,		_A				:				Union[str, Any]=True			,		_A				:				str=None			,		_A				:				Optional[Any]=True			,		_A				:				List[Any]=[0.5, 0.5, 0.5]			,		_A				:				List[str]=[0.5, 0.5, 0.5]			,		_A				:				Any=True			,		_A				:				Tuple=1 / 255			,		_A				:				str=True			,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     str =						size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
       __SCREAMING_SNAKE_CASE	:     str =						parent
       __SCREAMING_SNAKE_CASE	:     Optional[int] =						batch_size
       __SCREAMING_SNAKE_CASE	:     Optional[int] =						num_channels
       __SCREAMING_SNAKE_CASE	:     str =						min_resolution
       __SCREAMING_SNAKE_CASE	:     List[Any] =						max_resolution
       __SCREAMING_SNAKE_CASE	:     Tuple =						do_resize
       __SCREAMING_SNAKE_CASE	:     List[str] =						size
       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						do_normalize
       __SCREAMING_SNAKE_CASE	:     str =						image_mean
       __SCREAMING_SNAKE_CASE	:     List[Any] =						image_std
       __SCREAMING_SNAKE_CASE	:     Optional[int] =						do_rescale
       __SCREAMING_SNAKE_CASE	:     Any =						rescale_factor
       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						do_pad



     def       UpperCAmelCase__				(							self				:				str      ):







       """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				:				List[str]			,		_A				:				Optional[int]			,		_A				:				str=False      ):







       """simple docstring"""




       if not batched:
         __SCREAMING_SNAKE_CASE	:     Optional[Any] =						image_inputs[0]
         if isinstance(_SCREAMING_SNAKE_CASE			,		Image.Image      ):
           __SCREAMING_SNAKE_CASE	:     Any =						image.size
         else:
           __SCREAMING_SNAKE_CASE	:     Optional[Any] =						image.shape[1], image.shape[2]
         if w < h:
           __SCREAMING_SNAKE_CASE	:     Any =						int(self.size['''shortest_edge'''] * h / w      )
           __SCREAMING_SNAKE_CASE	:     List[Any] =						self.size["shortest_edge"]
         elif w > h:
           __SCREAMING_SNAKE_CASE	:     str =						self.size["shortest_edge"]
           __SCREAMING_SNAKE_CASE	:     Optional[int] =						int(self.size['''shortest_edge'''] * w / h      )
         else:
           __SCREAMING_SNAKE_CASE	:     List[str] =						self.size["shortest_edge"]
           __SCREAMING_SNAKE_CASE	:     str =						self.size["shortest_edge"]

       else:
         __SCREAMING_SNAKE_CASE	:     Optional[int] =						[]
         for image in image_inputs:
           __SCREAMING_SNAKE_CASE	:     Dict =						self.get_expected_values([image]      )
           expected_values.append((expected_height, expected_width)      )
         __SCREAMING_SNAKE_CASE	:     int =						max(_SCREAMING_SNAKE_CASE			,		key=lambda _A      : item[0]      )[0]
         __SCREAMING_SNAKE_CASE	:     Optional[Any] =						max(_SCREAMING_SNAKE_CASE			,		key=lambda _A      : item[1]      )[1]

       return expected_height, expected_width






@require_torch
@require_vision
class   __UpperCamelCase      (				__SCREAMING_SNAKE_CASE      ,		unittest.TestCase    ):






     """simple docstring"""







     lowerCAmelCase_									=       ConditionalDetrImageProcessor if is_vision_available() else None



     def       UpperCAmelCase__				(							self				:				List[Any]      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     str =						ConditionalDetrImageProcessingTester(self      )



     @property
     def       UpperCAmelCase__				(							self				:				str      ):







       """simple docstring"""




       return self.image_processor_tester.prepare_image_processor_dict()



     def       UpperCAmelCase__				(							self				:				int      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     Tuple =						self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,		'''image_mean'''      )      )
       self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,		'''image_std'''      )      )
       self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,		'''do_normalize'''      )      )
       self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,		'''do_resize'''      )      )
       self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,		'''size'''      )      )



     def       UpperCAmelCase__				(							self				:				Any      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     Tuple =						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			,		_SCREAMING_SNAKE_CASE      )

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



     def       UpperCAmelCase__				(							self				:				int      ):







       """simple docstring"""




       pass



     def       UpperCAmelCase__				(							self				:				Tuple      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       __SCREAMING_SNAKE_CASE	:     Any =						prepare_image_inputs(self.image_processor_tester			,		equal_resolution=_SCREAMING_SNAKE_CASE      )
       for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE			,		Image.Image      )

       # Test not batched input
       __SCREAMING_SNAKE_CASE	:     List[str] =						image_processing(image_inputs[0]			,		return_tensors='''pt'''      ).pixel_values

       __SCREAMING_SNAKE_CASE	:     Optional[int] =						self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      )

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

       # Test batched
       __SCREAMING_SNAKE_CASE	:     List[Any] =						self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE			,		batched=_SCREAMING_SNAKE_CASE      )

       __SCREAMING_SNAKE_CASE	:     str =						image_processing(_SCREAMING_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       UpperCAmelCase__				(							self				:				int      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     Dict =						self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						prepare_image_inputs(self.image_processor_tester			,		equal_resolution=_SCREAMING_SNAKE_CASE			,		numpify=_SCREAMING_SNAKE_CASE      )
       for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE			,		np.ndarray      )

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

       __SCREAMING_SNAKE_CASE	:     List[str] =						self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      )

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

       # Test batched
       __SCREAMING_SNAKE_CASE	:     Any =						image_processing(_SCREAMING_SNAKE_CASE			,		return_tensors='''pt'''      ).pixel_values

       __SCREAMING_SNAKE_CASE	:     List[Any] =						self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE			,		batched=_SCREAMING_SNAKE_CASE      )

       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[Any]      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE	:     List[str] =						prepare_image_inputs(self.image_processor_tester			,		equal_resolution=_SCREAMING_SNAKE_CASE			,		torchify=_SCREAMING_SNAKE_CASE      )
       for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE			,		torch.Tensor      )

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

       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      )

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

       # Test batched
       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						image_processing(_SCREAMING_SNAKE_CASE			,		return_tensors='''pt'''      ).pixel_values

       __SCREAMING_SNAKE_CASE	:     Optional[int] =						self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE			,		batched=_SCREAMING_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       UpperCAmelCase__				(							self				:				Dict      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     List[Any] =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''			,		'''r'''      ) as f:
         __SCREAMING_SNAKE_CASE	:     List[Any] =						json.loads(f.read()      )

       __SCREAMING_SNAKE_CASE	:     Tuple =						{"image_id": 3_9769, "annotations": target}

       # encode them
       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''      )
       __SCREAMING_SNAKE_CASE	:     int =						image_processing(images=_SCREAMING_SNAKE_CASE			,		annotations=_SCREAMING_SNAKE_CASE			,		return_tensors='''pt'''      )

       # verify pixel values
       __SCREAMING_SNAKE_CASE	:     str =						torch.Size([1, 3, 800, 1066]      )
       self.assertEqual(encoding['''pixel_values'''].shape			,		_SCREAMING_SNAKE_CASE      )

       __SCREAMING_SNAKE_CASE	:     List[str] =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,		_SCREAMING_SNAKE_CASE			,		atol=1e-4      )      )

       # verify area
       __SCREAMING_SNAKE_CASE	:     int =						torch.tensor([58_87.96_00, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,		_SCREAMING_SNAKE_CASE      )      )
       # verify boxes
       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						torch.Size([6, 4]      )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,		_SCREAMING_SNAKE_CASE      )
       __SCREAMING_SNAKE_CASE	:     str =						torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,		_SCREAMING_SNAKE_CASE			,		atol=1e-3      )      )
       # verify image_id
       __SCREAMING_SNAKE_CASE	:     List[Any] =						torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,		_SCREAMING_SNAKE_CASE      )      )
       # verify is_crowd
       __SCREAMING_SNAKE_CASE	:     List[str] =						torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,		_SCREAMING_SNAKE_CASE      )      )
       # verify class_labels
       __SCREAMING_SNAKE_CASE	:     Tuple =						torch.tensor([75, 75, 63, 65, 17, 17]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,		_SCREAMING_SNAKE_CASE      )      )
       # verify orig_size
       __SCREAMING_SNAKE_CASE	:     List[Any] =						torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,		_SCREAMING_SNAKE_CASE      )      )
       # verify size
       __SCREAMING_SNAKE_CASE	:     int =						torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,		_SCREAMING_SNAKE_CASE      )      )




     @slow
     def       UpperCAmelCase__				(							self				:				Dict      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     Optional[int] =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''			,		'''r'''      ) as f:
         __SCREAMING_SNAKE_CASE	:     Optional[int] =						json.loads(f.read()      )

       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

       __SCREAMING_SNAKE_CASE	:     int =						pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

       # encode them
       __SCREAMING_SNAKE_CASE	:     List[Any] =						ConditionalDetrImageProcessor(format='''coco_panoptic'''      )
       __SCREAMING_SNAKE_CASE	:     List[Any] =						image_processing(images=_SCREAMING_SNAKE_CASE			,		annotations=_SCREAMING_SNAKE_CASE			,		masks_path=_SCREAMING_SNAKE_CASE			,		return_tensors='''pt'''      )

       # verify pixel values
       __SCREAMING_SNAKE_CASE	:     str =						torch.Size([1, 3, 800, 1066]      )
       self.assertEqual(encoding['''pixel_values'''].shape			,		_SCREAMING_SNAKE_CASE      )

       __SCREAMING_SNAKE_CASE	:     List[Any] =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,		_SCREAMING_SNAKE_CASE			,		atol=1e-4      )      )

       # verify area
       __SCREAMING_SNAKE_CASE	:     Dict =						torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 58_79.65_62, 76_34.11_47]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,		_SCREAMING_SNAKE_CASE      )      )
       # verify boxes
       __SCREAMING_SNAKE_CASE	:     List[Any] =						torch.Size([6, 4]      )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,		_SCREAMING_SNAKE_CASE      )
       __SCREAMING_SNAKE_CASE	:     Any =						torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,		_SCREAMING_SNAKE_CASE			,		atol=1e-3      )      )
       # verify image_id
       __SCREAMING_SNAKE_CASE	:     int =						torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,		_SCREAMING_SNAKE_CASE      )      )
       # verify is_crowd
       __SCREAMING_SNAKE_CASE	:     Optional[int] =						torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,		_SCREAMING_SNAKE_CASE      )      )
       # verify class_labels
       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						torch.tensor([17, 17, 63, 75, 75, 93]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,		_SCREAMING_SNAKE_CASE      )      )
       # verify masks
       __SCREAMING_SNAKE_CASE	:     Any =						82_2873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()			,		_SCREAMING_SNAKE_CASE      )
       # verify orig_size
       __SCREAMING_SNAKE_CASE	:     Tuple =						torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,		_SCREAMING_SNAKE_CASE      )      )
       # verify size
       __SCREAMING_SNAKE_CASE	:     Dict =						torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,		_SCREAMING_SNAKE_CASE      )      )





"""simple docstring"""






from __future__ import annotations


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   list:
			__lowerCAmelCase      :  Dict												=       []
			__lowerCAmelCase						,	__lowerCAmelCase      :  Any												=       input_list[low:mid], input_list[mid : high + 1]
			while left and right:
						result.append((left if left[0] <= right[0] else right).pop(0	)	)
			__lowerCAmelCase      :  int												=       result + left + right
			return input_list


def 			_lowercase				(  __snake_case	)						->   list:
			if len(__snake_case	) <= 1:
						return input_list
			__lowerCAmelCase      :  int												=       list(__snake_case	)

			# iteration for two-way merging
			__lowerCAmelCase      :  Optional[int]												=       2
			while p <= len(__snake_case	):
						# getting low, high and middle value for merge-sort of single list
						for i in range(0					,len(__snake_case	)					,__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       i
									__lowerCAmelCase      :  Tuple												=       i + p - 1
									__lowerCAmelCase      :  Optional[Any]												=       (low + high + 1) // 2
									__lowerCAmelCase      :  Any												=       merge(__snake_case					,__snake_case					,__snake_case					,__snake_case	)
						# final merge of last two parts
						if p * 2 >= len(__snake_case	):
									__lowerCAmelCase      :  Optional[Any]												=       i
									__lowerCAmelCase      :  Union[str, Any]												=       merge(__snake_case					,0					,__snake_case					,len(__snake_case	) - 1	)
									break
						p *= 2

			return input_list


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