'''simple docstring'''

import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
    from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
    from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
  from PIL import Image
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase="shi-labs/oneformer_demo"					):
 with open(hf_hub_download(UpperCAmelCase		,					UpperCAmelCase		,					repo_type='''dataset'''					)		,					'''r'''					) as f:
  lowercase__    :   Union[str, Any]						= json.load(UpperCAmelCase					)
 lowercase__    :   Tuple						= {}
 lowercase__    :   List[str]						= []
 lowercase__    :   int						= []
 for key, info in class_info.items():
  lowercase__    :   Optional[Any]						= info['''name''']
  class_names.append(info['''name''']					)
  if info["isthing"]:
   thing_ids.append(int(UpperCAmelCase					)					)
 lowercase__    :   Tuple						= thing_ids
 lowercase__    :   Optional[int]						= class_names
 return metadata



class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=7     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=30     ,       __lowerCAmelCase=400     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=[0.5, 0.5, 0.5]     ,       __lowerCAmelCase=[0.5, 0.5, 0.5]     ,       __lowerCAmelCase=10     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=255     ,       __lowerCAmelCase="shi-labs/oneformer_demo"     ,       __lowerCAmelCase="ade20k_panoptic.json"     ,       __lowerCAmelCase=10     ,       )  ->  int:
        lowercase__    :   Optional[Any]						= parent
        lowercase__    :   Optional[int]						= batch_size
        lowercase__    :   List[Any]						= num_channels
        lowercase__    :   Union[str, Any]						= min_resolution
        lowercase__    :   List[Any]						= max_resolution
        lowercase__    :   Any						= do_resize
        lowercase__    :   str						= {'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
        lowercase__    :   Any						= do_normalize
        lowercase__    :   Optional[Any]						= image_mean
        lowercase__    :   Tuple						= image_std
        lowercase__    :   Tuple						= class_info_file
        lowercase__    :   str						= prepare_metadata(__lowerCAmelCase     ,       __lowerCAmelCase			)
        lowercase__    :   List[str]						= num_text
        lowercase__    :   Tuple						= repo_path

        # for the post_process_functions
        lowercase__    :   List[Any]						= 2
        lowercase__    :   Union[str, Any]						= 10
        lowercase__    :   int						= 10
        lowercase__    :   Dict						= 3
        lowercase__    :   List[str]						= 4
        lowercase__    :   int						= num_labels
        lowercase__    :   str						= do_reduce_labels
        lowercase__    :   Union[str, Any]						= ignore_index

       def _lowerCAmelCase(						self			)  ->  Any:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "num_labels": self.num_labels,
            "do_reduce_labels": self.do_reduce_labels,
            "ignore_index": self.ignore_index,
            "class_info_file": self.class_info_file,
            "metadata": self.metadata,
            "num_text": self.num_text,
        }

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

        else:
         lowercase__    :   Dict						= []
         for image in image_inputs:
          lowercase__			,							lowercase__    :   Optional[Any]						= self.get_expected_values([image]			)
          expected_values.append((expected_height, expected_width)			)
         lowercase__    :   Tuple						= max(__lowerCAmelCase     ,       key=lambda __lowerCAmelCase			: item[0]			)[0]
         lowercase__    :   List[Any]						= max(__lowerCAmelCase     ,       key=lambda __lowerCAmelCase			: item[1]			)[1]

        return expected_height, expected_width


       def _lowerCAmelCase(						self			)  ->  str:
        return OneFormerForUniversalSegmentationOutput(
            # +1 for null class
            class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)			)     ,       masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)			)     ,       )



@require_torch
@require_vision
class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
       # only for test_image_processing_common.test_image_proc_to_json_string
       SCREAMING_SNAKE_CASE 		=   image_processing_class

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   str						= OneFormerImageProcessorTester(self			)

       @property
       def _lowerCAmelCase(						self			)  ->  List[str]:
        return self.image_processing_tester.prepare_image_processor_dict()

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   int						= 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'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''ignore_index'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''class_info_file'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''num_text'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''repo_path'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''metadata'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''do_reduce_labels'''			)			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        pass

       def _lowerCAmelCase(						self			)  ->  Dict:
        # Initialize image_processor
        lowercase__    :   Any						= self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowercase__    :   List[str]						= prepare_image_inputs(self.image_processing_tester     ,       equal_resolution=__lowerCAmelCase			)
        for image in image_inputs:
         self.assertIsInstance(__lowerCAmelCase     ,       Image.Image			)

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

        lowercase__			,							lowercase__    :   str						= self.image_processing_tester.get_expected_values(__lowerCAmelCase			)

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

        # Test batched
        lowercase__			,							lowercase__    :   Optional[Any]						= self.image_processing_tester.get_expected_values(__lowerCAmelCase     ,       batched=__lowerCAmelCase			)

        lowercase__    :   int						= image_processor(
            __lowerCAmelCase     ,       ['''semantic'''] * len(__lowerCAmelCase			)     ,       return_tensors='''pt'''			).pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                self.image_processing_tester.batch_size,
                self.image_processing_tester.num_channels,
                expected_height,
                expected_width,
            )     ,       )

       def _lowerCAmelCase(						self			)  ->  Any:
        # Initialize image_processor
        lowercase__    :   Tuple						= self.image_processing_class(**self.image_processor_dict			)
        # create random numpy tensors
        lowercase__    :   Union[str, Any]						= prepare_image_inputs(self.image_processing_tester     ,       equal_resolution=__lowerCAmelCase     ,       numpify=__lowerCAmelCase			)
        for image in image_inputs:
         self.assertIsInstance(__lowerCAmelCase     ,       np.ndarray			)

        # Test not batched input
        lowercase__    :   Optional[int]						= image_processor(image_inputs[0]     ,       ['''semantic''']     ,       return_tensors='''pt'''			).pixel_values

        lowercase__			,							lowercase__    :   Optional[int]						= self.image_processing_tester.get_expected_values(__lowerCAmelCase			)

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

        # Test batched
        lowercase__			,							lowercase__    :   int						= self.image_processing_tester.get_expected_values(__lowerCAmelCase     ,       batched=__lowerCAmelCase			)

        lowercase__    :   List[Any]						= image_processor(
            __lowerCAmelCase     ,       ['''semantic'''] * len(__lowerCAmelCase			)     ,       return_tensors='''pt'''			).pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                self.image_processing_tester.batch_size,
                self.image_processing_tester.num_channels,
                expected_height,
                expected_width,
            )     ,       )

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        # Initialize image_processor
        lowercase__    :   str						= self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        lowercase__    :   Dict						= prepare_image_inputs(self.image_processing_tester     ,       equal_resolution=__lowerCAmelCase     ,       torchify=__lowerCAmelCase			)
        for image in image_inputs:
         self.assertIsInstance(__lowerCAmelCase     ,       torch.Tensor			)

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

        lowercase__			,							lowercase__    :   int						= self.image_processing_tester.get_expected_values(__lowerCAmelCase			)

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

        # Test batched
        lowercase__			,							lowercase__    :   Any						= self.image_processing_tester.get_expected_values(__lowerCAmelCase     ,       batched=__lowerCAmelCase			)

        lowercase__    :   Optional[int]						= image_processor(
            __lowerCAmelCase     ,       ['''semantic'''] * len(__lowerCAmelCase			)     ,       return_tensors='''pt'''			).pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                self.image_processing_tester.batch_size,
                self.image_processing_tester.num_channels,
                expected_height,
                expected_width,
            )     ,       )

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase="np"			)  ->  str:
        lowercase__    :   Any						= self.image_processing_class(**self.image_processor_dict			)
        # prepare image and target
        lowercase__    :   Union[str, Any]						= self.image_processing_tester.num_labels
        lowercase__    :   Optional[int]						= None
        lowercase__    :   Optional[Any]						= None
        lowercase__    :   Tuple						= prepare_image_inputs(self.image_processing_tester     ,       equal_resolution=__lowerCAmelCase			)
        if with_segmentation_maps:
         lowercase__    :   int						= num_labels
         if is_instance_map:
          lowercase__    :   List[str]						= list(range(__lowerCAmelCase			)			) * 2
          lowercase__    :   List[str]						= dict(enumerate(__lowerCAmelCase			)			)
         lowercase__    :   Optional[Any]						= [
             np.random.randint(0     ,       high * 2     ,       (img.size[1], img.size[0])			).astype(np.uinta			) for img in image_inputs
         ]
         if segmentation_type == "pil":
          lowercase__    :   Dict						= [Image.fromarray(__lowerCAmelCase			) for annotation in annotations]

        lowercase__    :   Optional[Any]						= image_processor(
            __lowerCAmelCase     ,       ['''semantic'''] * len(__lowerCAmelCase			)     ,       __lowerCAmelCase     ,       return_tensors='''pt'''     ,       instance_id_to_semantic_id=__lowerCAmelCase     ,       pad_and_return_pixel_mask=__lowerCAmelCase     ,       )

        return inputs

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        pass

       def _lowerCAmelCase(						self			)  ->  Tuple:
        def common(__lowerCAmelCase=False     ,       __lowerCAmelCase=None			):
         lowercase__    :   List[str]						= self.comm_get_image_processor_inputs(
             with_segmentation_maps=__lowerCAmelCase     ,       is_instance_map=__lowerCAmelCase     ,       segmentation_type=__lowerCAmelCase			)

         lowercase__    :   Optional[Any]						= inputs['''mask_labels''']
         lowercase__    :   List[str]						= inputs['''class_labels''']
         lowercase__    :   Union[str, Any]						= inputs['''pixel_values''']
         lowercase__    :   Union[str, Any]						= inputs['''text_inputs''']

         # check the batch_size
         for mask_label, class_label, text_input in zip(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			):
          self.assertEqual(mask_label.shape[0]     ,       class_label.shape[0]			)
          # this ensure padding has happened
          self.assertEqual(mask_label.shape[1:]     ,       pixel_values.shape[2:]			)
          self.assertEqual(len(__lowerCAmelCase			)     ,       self.image_processing_tester.num_text			)

        common()
        common(is_instance_map=__lowerCAmelCase			)
        common(is_instance_map=__lowerCAmelCase     ,       segmentation_type='''pil'''			)
        common(is_instance_map=__lowerCAmelCase     ,       segmentation_type='''pil'''			)

       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   Tuple						= np.zeros((20, 50)			)
        lowercase__    :   int						= 1
        lowercase__    :   Dict						= 1
        lowercase__    :   str						= 1

        lowercase__    :   int						= binary_mask_to_rle(__lowerCAmelCase			)
        self.assertEqual(len(__lowerCAmelCase			)     ,       4			)
        self.assertEqual(rle[0]     ,       21			)
        self.assertEqual(rle[1]     ,       45			)

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   str						= self.image_processing_class(
            num_labels=self.image_processing_tester.num_classes     ,       max_seq_length=77     ,       task_seq_length=77     ,       class_info_file='''ade20k_panoptic.json'''     ,       num_text=self.image_processing_tester.num_text     ,       repo_path='''shi-labs/oneformer_demo'''     ,       )
        lowercase__    :   List[Any]						= self.image_processing_tester.get_fake_oneformer_outputs()

        lowercase__    :   Optional[int]						= fature_extractor.post_process_semantic_segmentation(__lowerCAmelCase			)

        self.assertEqual(len(__lowerCAmelCase			)     ,       self.image_processing_tester.batch_size			)
        self.assertEqual(
            segmentation[0].shape     ,       (
                self.image_processing_tester.height,
                self.image_processing_tester.width,
            )     ,       )

        lowercase__    :   int						= [(1, 4) for i in range(self.image_processing_tester.batch_size			)]
        lowercase__    :   Dict						= fature_extractor.post_process_semantic_segmentation(__lowerCAmelCase     ,       target_sizes=__lowerCAmelCase			)

        self.assertEqual(segmentation[0].shape     ,       target_sizes[0]			)

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   Optional[int]						= self.image_processing_class(
            num_labels=self.image_processing_tester.num_classes     ,       max_seq_length=77     ,       task_seq_length=77     ,       class_info_file='''ade20k_panoptic.json'''     ,       num_text=self.image_processing_tester.num_text     ,       repo_path='''shi-labs/oneformer_demo'''     ,       )
        lowercase__    :   Optional[int]						= self.image_processing_tester.get_fake_oneformer_outputs()
        lowercase__    :   List[str]						= image_processor.post_process_instance_segmentation(__lowerCAmelCase     ,       threshold=0			)

        self.assertTrue(len(__lowerCAmelCase			) == self.image_processing_tester.batch_size			)
        for el in segmentation:
         self.assertTrue('''segmentation''' in el			)
         self.assertTrue('''segments_info''' in el			)
         self.assertEqual(type(el['''segments_info''']			)     ,       __lowerCAmelCase			)
         self.assertEqual(
             el['''segmentation'''].shape     ,       (self.image_processing_tester.height, self.image_processing_tester.width)			)


       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Optional[int]						= self.image_processing_class(
            num_labels=self.image_processing_tester.num_classes     ,       max_seq_length=77     ,       task_seq_length=77     ,       class_info_file='''ade20k_panoptic.json'''     ,       num_text=self.image_processing_tester.num_text     ,       repo_path='''shi-labs/oneformer_demo'''     ,       )
        lowercase__    :   Dict						= self.image_processing_tester.get_fake_oneformer_outputs()
        lowercase__    :   int						= image_processor.post_process_panoptic_segmentation(__lowerCAmelCase     ,       threshold=0			)

        self.assertTrue(len(__lowerCAmelCase			) == self.image_processing_tester.batch_size			)
        for el in segmentation:
         self.assertTrue('''segmentation''' in el			)
         self.assertTrue('''segments_info''' in el			)
         self.assertEqual(type(el['''segments_info''']			)     ,       __lowerCAmelCase			)
         self.assertEqual(
             el['''segmentation'''].shape     ,       (self.image_processing_tester.height, self.image_processing_tester.width)			)


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase					):

 def merge(UpperCAmelCase		,					UpperCAmelCase					) -> list:

  def _merge():
   while left and right:
    yield (left if left[0] <= right[0] else right).pop(0					)
   yield from left
   yield from right

  return list(_merge()					)

 if len(UpperCAmelCase					) <= 1:
  return collection
 lowercase__    :   int						= len(UpperCAmelCase					) // 2
 return merge(merge_sort(collection[:mid]					)		,					merge_sort(collection[mid:]					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __a:			str	       =       input("""Enter numbers separated by a comma:\n""").strip()
  __a:			Optional[int]	       =       [int(item) for item in user_input.split(""",""")]
  print(*merge_sort(unsorted), sep=""",""")

