


'''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 DetaImageProcessor

class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase=7      ,      __lowercase=3      ,      __lowercase=30      ,      __lowercase=400      ,      __lowercase=True      ,      __lowercase=None      ,      __lowercase=True      ,      __lowercase=[0.5, 0.5, 0.5]      ,      __lowercase=[0.5, 0.5, 0.5]      ,      __lowercase=True      ,      __lowercase=1 / 255      ,      __lowercase=True      ,      ):

    """simple docstring"""







    __A  :		str       =			size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
    __A  :		List[Any]       =			parent
    __A  :		Any       =			batch_size
    __A  :		Dict       =			num_channels
    __A  :		Optional[int]       =			min_resolution
    __A  :		Any       =			max_resolution
    __A  :		str       =			do_resize
    __A  :		Any       =			size
    __A  :		List[str]       =			do_normalize
    __A  :		Dict       =			image_mean
    __A  :		Optional[int]       =			image_std
    __A  :		Tuple       =			do_rescale
    __A  :		str       =			rescale_factor
    __A  :		int       =			do_pad


 def  snake_case__   (      self						):

    """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  snake_case__   (      self      ,      __lowercase      ,      __lowercase=False						):

    """simple docstring"""







    if not batched:
       __A  :		Tuple       =			image_inputs[0]
       if isinstance(__lowercase      ,      Image.Image						):
          __A  :		str       =			image.size
       else:
          __A  :		Tuple       =			image.shape[1], image.shape[2]
       if w < h:
          __A  :		int       =			int(self.size['shortest_edge'] * h / w						)
          __A  :		Dict       =			self.size['shortest_edge']
       elif w > h:
          __A  :		List[str]       =			self.size['shortest_edge']
          __A  :		int       =			int(self.size['shortest_edge'] * w / h						)
       else:
          __A  :		Tuple       =			self.size['shortest_edge']
          __A  :		List[str]       =			self.size['shortest_edge']

    else:
       __A  :		List[str]       =			[]
       for image in image_inputs:
          __A  :		List[str]       =			self.get_expected_values([image]						)
          expected_values.append((expected_height, expected_width)						)
       __A  :		Optional[int]       =			max(__lowercase      ,      key=lambda __lowercase						: item[0]						)[0]
       __A  :		Union[str, Any]       =			max(__lowercase      ,      key=lambda __lowercase						: item[1]						)[1]

    return expected_height, expected_width


@require_torch
@require_vision
class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Union[str, Any]    							=						DetaImageProcessor if is_vision_available() else None


 def  snake_case__   (      self						):

    """simple docstring"""







    __A  :		Optional[int]       =			DetaImageProcessingTester(self						)


 @property
 def  snake_case__   (      self						):

    """simple docstring"""







    return self.image_processor_tester.prepare_image_processor_dict()


 def  snake_case__   (      self						):

    """simple docstring"""







    __A  :		Any       =			self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(__lowercase      ,      'image_mean'						)						)
    self.assertTrue(hasattr(__lowercase      ,      'image_std'						)						)
    self.assertTrue(hasattr(__lowercase      ,      'do_normalize'						)						)
    self.assertTrue(hasattr(__lowercase      ,      'do_resize'						)						)
    self.assertTrue(hasattr(__lowercase      ,      'do_rescale'						)						)
    self.assertTrue(hasattr(__lowercase      ,      'do_pad'						)						)
    self.assertTrue(hasattr(__lowercase      ,      'size'						)						)


 def  snake_case__   (      self						):

    """simple docstring"""







    __A  :		str       =			self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size      ,      {'shortest_edge': 18, 'longest_edge': 1_333}						)
    self.assertEqual(image_processor.do_pad      ,      __lowercase						)


 def  snake_case__   (      self						):

    """simple docstring"""







    pass


 def  snake_case__   (      self						):

    """simple docstring"""







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

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

    __A  :		Any       =			self.image_processor_tester.get_expected_values(__lowercase						)

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

    # Test batched
    __A  :		Union[str, Any]       =			self.image_processor_tester.get_expected_values(__lowercase      ,      batched=__lowercase						)

    __A  :		Optional[int]       =			image_processing(__lowercase      ,      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						):

    """simple docstring"""







    __A  :		Any       =			self.image_processing_class(**self.image_processor_dict						)
    # create random numpy tensors
    __A  :		int       =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=__lowercase      ,      numpify=__lowercase						)
    for image in image_inputs:
       self.assertIsInstance(__lowercase      ,      np.ndarray						)

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

    __A  :		Tuple       =			self.image_processor_tester.get_expected_values(__lowercase						)

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

    # Test batched
    __A  :		Any       =			image_processing(__lowercase      ,      return_tensors='pt'						).pixel_values

    __A  :		Optional[int]       =			self.image_processor_tester.get_expected_values(__lowercase      ,      batched=__lowercase						)

    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						):

    """simple docstring"""







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

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

    __A  :		List[str]       =			self.image_processor_tester.get_expected_values(__lowercase						)

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

    # Test batched
    __A  :		List[str]       =			image_processing(__lowercase      ,      return_tensors='pt'						).pixel_values

    __A  :		Any       =			self.image_processor_tester.get_expected_values(__lowercase      ,      batched=__lowercase						)

    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						):

    """simple docstring"""







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

    __A  :		int       =			{'image_id': 39_769, 'annotations': target}

    # encode them
    __A  :		Optional[int]       =			DetaImageProcessor()
    __A  :		Union[str, Any]       =			image_processing(images=__lowercase      ,      annotations=__lowercase      ,      return_tensors='pt'						)

    # verify pixel values
    __A  :		Union[str, Any]       =			torch.Size([1, 3, 800, 1_066]						)
    self.assertEqual(encoding['pixel_values'].shape      ,      __lowercase						)

    __A  :		Tuple       =			torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
    self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,      __lowercase      ,      atol=1E-4						)						)

    # verify area
    __A  :		int       =			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']      ,      __lowercase						)						)
    # verify boxes
    __A  :		Union[str, Any]       =			torch.Size([6, 4]						)
    self.assertEqual(encoding['labels'][0]['boxes'].shape      ,      __lowercase						)
    __A  :		Tuple       =			torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,      __lowercase      ,      atol=1E-3						)						)
    # verify image_id
    __A  :		Union[str, Any]       =			torch.tensor([39_769]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,      __lowercase						)						)
    # verify is_crowd
    __A  :		List[Any]       =			torch.tensor([0, 0, 0, 0, 0, 0]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,      __lowercase						)						)
    # verify class_labels
    __A  :		List[str]       =			torch.tensor([75, 75, 63, 65, 17, 17]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,      __lowercase						)						)
    # verify orig_size
    __A  :		Optional[Any]       =			torch.tensor([480, 640]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,      __lowercase						)						)
    # verify size
    __A  :		List[str]       =			torch.tensor([800, 1_066]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,      __lowercase						)						)







 @slow
 def  snake_case__   (      self						):

    """simple docstring"""







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

    __A  :		Dict       =			{'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

    __A  :		str       =			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'						)

    # encode them
    __A  :		int       =			DetaImageProcessor(format='coco_panoptic'						)
    __A  :		List[Any]       =			image_processing(images=__lowercase      ,      annotations=__lowercase      ,      masks_path=__lowercase      ,      return_tensors='pt'						)

    # verify pixel values
    __A  :		Tuple       =			torch.Size([1, 3, 800, 1_066]						)
    self.assertEqual(encoding['pixel_values'].shape      ,      __lowercase						)

    __A  :		str       =			torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
    self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,      __lowercase      ,      atol=1E-4						)						)

    # verify area
    __A  :		List[str]       =			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']      ,      __lowercase						)						)
    # verify boxes
    __A  :		List[Any]       =			torch.Size([6, 4]						)
    self.assertEqual(encoding['labels'][0]['boxes'].shape      ,      __lowercase						)
    __A  :		Union[str, Any]       =			torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,      __lowercase      ,      atol=1E-3						)						)
    # verify image_id
    __A  :		List[str]       =			torch.tensor([39_769]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,      __lowercase						)						)
    # verify is_crowd
    __A  :		Union[str, Any]       =			torch.tensor([0, 0, 0, 0, 0, 0]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,      __lowercase						)						)
    # verify class_labels
    __A  :		Optional[Any]       =			torch.tensor([17, 17, 63, 75, 75, 93]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,      __lowercase						)						)
    # verify masks
    __A  :		Dict       =			822_873
    self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,      __lowercase						)
    # verify orig_size
    __A  :		Union[str, Any]       =			torch.tensor([480, 640]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,      __lowercase						)						)
    # verify size
    __A  :		Any       =			torch.tensor([800, 1_066]						)
    self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,      __lowercase						)						)










'''simple docstring'''
from __future__ import annotations


def 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 __A  :		Dict       =			[]
 __A     ,__A  :		Dict       =			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			)			)
 __A  :		Optional[Any]       =			result + left + right
 return input_list


def 			_lowercase       (				UpperCamelCase__       :					list			):
 if len(UpperCamelCase__			) <= 1:
  return input_list
 __A  :		Any       =			list(UpperCamelCase__			)

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

 return input_list


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






