
"""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 DetrImageProcessor

class       lowerCAmelCase__				(						unittest.TestCase	):







     def __init__(							self			:			List[Any]    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Union[str, Any]=7    ,      _lowerCamelCase			:			Union[str, Any]=3    ,      _lowerCamelCase			:			Dict=30    ,      _lowerCamelCase			:			int=400    ,      _lowerCamelCase			:			int=True    ,      _lowerCamelCase			:			int=None    ,      _lowerCamelCase			:			Optional[Any]=True    ,      _lowerCamelCase			:			List[str]=1 / 255    ,      _lowerCamelCase			:			List[str]=True    ,      _lowerCamelCase			:			List[Any]=[0.5, 0.5, 0.5]    ,      _lowerCamelCase			:			List[Any]=[0.5, 0.5, 0.5]    ,      _lowerCamelCase			:			Tuple=True    ,      ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       _snake_case					     =       size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
       _snake_case					     =       parent
       _snake_case					     =       batch_size
       _snake_case					     =       num_channels
       _snake_case					     =       min_resolution
       _snake_case					     =       max_resolution
       _snake_case					     =       do_resize
       _snake_case					     =       size
       _snake_case					     =       do_rescale
       _snake_case					     =       rescale_factor
       _snake_case					     =       do_normalize
       _snake_case					     =       image_mean
       _snake_case					     =       image_std
       _snake_case					     =       do_pad







     def      lowercase	(							self			:			List[str]			):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_pad": self.do_pad,
       }







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

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

       return expected_height, expected_width






@require_torch
@require_vision
class       lowerCAmelCase__				(						A_ ,	unittest.TestCase	):
     __a						=					DetrImageProcessor if is_vision_available() else None







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       DetrImageProcessingTester(self			)







     @property
     def      lowercase	(							self			:			Any			):
       return self.image_processor_tester.prepare_image_processor_dict()







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       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_rescale'''			)			)
       self.assertTrue(hasattr(_lowerCamelCase    ,      '''rescale_factor'''			)			)
       self.assertTrue(hasattr(_lowerCamelCase    ,      '''do_resize'''			)			)
       self.assertTrue(hasattr(_lowerCamelCase    ,      '''size'''			)			)
       self.assertTrue(hasattr(_lowerCamelCase    ,      '''do_pad'''			)			)







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       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			)

       _snake_case					     =       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      lowercase	(							self			:			Optional[int]			):
       pass







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

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

       _snake_case     ,					_snake_case					     =       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
       _snake_case     ,					_snake_case					     =       self.image_processor_tester.get_expected_values(_lowerCamelCase    ,      batched=_lowerCamelCase			)

       _snake_case					     =       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      lowercase	(							self			:			str			):
       # Initialize image_processing
       _snake_case					     =       self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       _snake_case					     =       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
       _snake_case					     =       image_processing(image_inputs[0]    ,      return_tensors='''pt'''			).pixel_values

       _snake_case     ,					_snake_case					     =       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
       _snake_case					     =       image_processing(_lowerCamelCase    ,      return_tensors='''pt'''			).pixel_values

       _snake_case     ,					_snake_case					     =       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      lowercase	(							self			:			Dict			):
       # Initialize image_processing
       _snake_case					     =       self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       _snake_case					     =       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
       _snake_case					     =       image_processing(image_inputs[0]    ,      return_tensors='''pt'''			).pixel_values

       _snake_case     ,					_snake_case					     =       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
       _snake_case					     =       image_processing(_lowerCamelCase    ,      return_tensors='''pt'''			).pixel_values

       _snake_case     ,					_snake_case					     =       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      lowercase	(							self			:			Optional[int]			):
       # prepare image and target
       _snake_case					     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,      '''r'''			) as f:
         _snake_case					     =       json.loads(f.read()			)

       _snake_case					     =       {'''image_id''': 39769, '''annotations''': target}

       # encode them
       _snake_case					     =       DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''			)
       _snake_case					     =       image_processing(images=_lowerCamelCase    ,      annotations=_lowerCamelCase    ,      return_tensors='''pt'''			)

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

       _snake_case					     =       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]    ,      _lowerCamelCase    ,      atol=1e-4			)			)

       # verify area
       _snake_case					     =       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,      _lowerCamelCase			)			)
       # verify boxes
       _snake_case					     =       torch.Size([6, 4]			)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,      _lowerCamelCase			)
       _snake_case					     =       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]    ,      _lowerCamelCase    ,      atol=1e-3			)			)
       # verify image_id
       _snake_case					     =       torch.tensor([39769]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,      _lowerCamelCase			)			)
       # verify is_crowd
       _snake_case					     =       torch.tensor([0, 0, 0, 0, 0, 0]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,      _lowerCamelCase			)			)
       # verify class_labels
       _snake_case					     =       torch.tensor([75, 75, 63, 65, 17, 17]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,      _lowerCamelCase			)			)
       # verify orig_size
       _snake_case					     =       torch.tensor([480, 640]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,      _lowerCamelCase			)			)
       # verify size
       _snake_case					     =       torch.tensor([800, 1066]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,      _lowerCamelCase			)			)







     @slow
     def      lowercase	(							self			:			Tuple			):
       # prepare image, target and masks_path
       _snake_case					     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,      '''r'''			) as f:
         _snake_case					     =       json.loads(f.read()			)

       _snake_case					     =       {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

       _snake_case					     =       pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''			)

       # encode them
       _snake_case					     =       DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''			)
       _snake_case					     =       image_processing(images=_lowerCamelCase    ,      annotations=_lowerCamelCase    ,      masks_path=_lowerCamelCase    ,      return_tensors='''pt'''			)

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

       _snake_case					     =       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]    ,      _lowerCamelCase    ,      atol=1e-4			)			)

       # verify area
       _snake_case					     =       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,      _lowerCamelCase			)			)
       # verify boxes
       _snake_case					     =       torch.Size([6, 4]			)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,      _lowerCamelCase			)
       _snake_case					     =       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]    ,      _lowerCamelCase    ,      atol=1e-3			)			)
       # verify image_id
       _snake_case					     =       torch.tensor([39769]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,      _lowerCamelCase			)			)
       # verify is_crowd
       _snake_case					     =       torch.tensor([0, 0, 0, 0, 0, 0]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,      _lowerCamelCase			)			)
       # verify class_labels
       _snake_case					     =       torch.tensor([17, 17, 63, 75, 75, 93]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,      _lowerCamelCase			)			)
       # verify masks
       _snake_case					     =       822873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,      _lowerCamelCase			)
       # verify orig_size
       _snake_case					     =       torch.tensor([480, 640]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,      _lowerCamelCase			)			)
       # verify size
       _snake_case					     =       torch.tensor([800, 1066]			)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,      _lowerCamelCase			)			)





"""simple docstring"""



from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class       lowerCAmelCase__				(						A_	):







     def __lt__(							self			:			Any    ,      _lowerCamelCase			:			int			):
       return self[-1] < other[-1]







     def __eq__(							self			:			int    ,      _lowerCamelCase			:			Optional[Any]			):
       return self[-1] == other[-1]






def 					_UpperCAmelCase      ( __lowerCamelCase   :       list		)     ->					list:
  _snake_case					     =       []
  # sort into stacks
  for element in collection:
    _snake_case					     =       Stack([element]		)
    _snake_case					     =       bisect_left(__lowerCamelCase ,					__lowerCamelCase		)
    if i != len(__lowerCamelCase		):
      stacks[i].append(__lowerCamelCase		)
    else:
      stacks.append(__lowerCamelCase		)

    # use a heap-based merge to merge stack efficiently
  _snake_case					     =       merge(*(reversed(__lowerCamelCase		) for stack in stacks)		)
  return collection


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



