
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					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Tuple="shi-labs/oneformer_demo"  ):
	with open(hf_hub_download(lowerCAmelCase__  ,       lowerCAmelCase__  ,       repo_type='''dataset'''  )  ,       '''r'''  ) as f:
		__a			:  Optional[Any]		    =  json.load(lowerCAmelCase__  )
	__a			:  Any		    =  {}
	__a			:  Dict		    =  []
	__a			:  Tuple		    =  []
	for key, info in class_info.items():
		__a			:  Optional[Any]		    =  info['''name''']
		class_names.append(info['''name''']  )
		if info["isthing"]:
			thing_ids.append(int(lowerCAmelCase__  )  )
	__a			:  Optional[int]		    =  thing_ids
	__a			:  Optional[Any]		    =  class_names
	return metadata



class 		UpperCamelCase__  (	unittest.TestCase   ):
			def __init__(self			: Optional[Any] ,    snake_case_			: Optional[int] ,    snake_case_			: List[Any]=7 ,    snake_case_			: int=3 ,    snake_case_			: List[Any]=3_0 ,    snake_case_			: List[Any]=4_0_0 ,    snake_case_			: Union[str, Any]=None ,    snake_case_			: Optional[Any]=True ,    snake_case_			: Tuple=True ,    snake_case_			: Dict=[0.5, 0.5, 0.5] ,    snake_case_			: Tuple=[0.5, 0.5, 0.5] ,    snake_case_			: List[Any]=1_0 ,    snake_case_			: List[Any]=False ,    snake_case_			: Dict=2_5_5 ,    snake_case_			: List[Any]="shi-labs/oneformer_demo" ,    snake_case_			: int="ade20k_panoptic.json" ,    snake_case_			: List[Any]=1_0 ,    ):
				__a			:  List[Any]		    =  parent
				__a			:  List[Any]		    =  batch_size
				__a			:  Any		    =  num_channels
				__a			:  Tuple		    =  min_resolution
				__a			:  int		    =  max_resolution
				__a			:  Optional[Any]		    =  do_resize
				__a			:  Tuple		    =  {'''shortest_edge''': 3_2, '''longest_edge''': 1_3_3_3} if size is None else size
				__a			:  Dict		    =  do_normalize
				__a			:  Optional[int]		    =  image_mean
				__a			:  Dict		    =  image_std
				__a			:  Tuple		    =  class_info_file
				__a			:  int		    =  prepare_metadata(snake_case_ ,    snake_case_       )
				__a			:  Optional[Any]		    =  num_text
				__a			:  Any		    =  repo_path

				# for the post_process_functions
				__a			:  Any		    =  2
				__a			:  Union[str, Any]		    =  1_0
				__a			:  Optional[Any]		    =  1_0
				__a			:  str		    =  3
				__a			:  str		    =  4
				__a			:  Optional[Any]		    =  num_labels
				__a			:  Union[str, Any]		    =  do_reduce_labels
				__a			:  Optional[Any]		    =  ignore_index
			def 				lowerCAmelCase      (self			: List[str]       ):
				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			: Any ,    snake_case_			: Optional[Any] ,    snake_case_			: int=False       ):
				if not batched:
					__a			:  Optional[int]		    =  image_inputs[0]
					if isinstance(snake_case_ ,    Image.Image       ):
						__a			:  Dict		    =  image.size
					else:
						__a			:  int		    =  image.shape[1], image.shape[2]
					if w < h:
						__a			:  Dict		    =  int(self.size['''shortest_edge'''] * h / w       )
						__a			:  int		    =  self.size['''shortest_edge''']
					elif w > h:
						__a			:  int		    =  self.size['''shortest_edge''']
						__a			:  str		    =  int(self.size['''shortest_edge'''] * w / h       )
					else:
						__a			:  List[Any]		    =  self.size['''shortest_edge''']
						__a			:  str		    =  self.size['''shortest_edge''']

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

				return expected_height, expected_width







			def 				lowerCAmelCase      (self			: Union[str, Any]       ):
				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__  (	__lowercase   ,unittest.TestCase   ):
			_SCREAMING_SNAKE_CASE				:							Dict     	=				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				:							Optional[int]     	=				image_processing_class
			def 				lowerCAmelCase      (self			: Optional[Any]       ):
				__a			:  Dict		    =  OneFormerImageProcessorTester(self       )
			@property
			def 				lowerCAmelCase      (self			: List[str]       ):
				return self.image_processing_tester.prepare_image_processor_dict()
			def 				lowerCAmelCase      (self			: Optional[int]       ):
				__a			:  Dict		    =  self.image_processing_class(**self.image_processor_dict       )
				self.assertTrue(hasattr(snake_case_ ,    '''image_mean'''       )       )
				self.assertTrue(hasattr(snake_case_ ,    '''image_std'''       )       )
				self.assertTrue(hasattr(snake_case_ ,    '''do_normalize'''       )       )
				self.assertTrue(hasattr(snake_case_ ,    '''do_resize'''       )       )
				self.assertTrue(hasattr(snake_case_ ,    '''size'''       )       )
				self.assertTrue(hasattr(snake_case_ ,    '''ignore_index'''       )       )
				self.assertTrue(hasattr(snake_case_ ,    '''class_info_file'''       )       )
				self.assertTrue(hasattr(snake_case_ ,    '''num_text'''       )       )
				self.assertTrue(hasattr(snake_case_ ,    '''repo_path'''       )       )
				self.assertTrue(hasattr(snake_case_ ,    '''metadata'''       )       )
				self.assertTrue(hasattr(snake_case_ ,    '''do_reduce_labels'''       )       )
			def 				lowerCAmelCase      (self			: Optional[int]       ):
				pass
			def 				lowerCAmelCase      (self			: str       ):
				# Initialize image_processor
				__a			:  Optional[Any]		    =  self.image_processing_class(**self.image_processor_dict       )
				# create random PIL images
				__a			:  List[str]		    =  prepare_image_inputs(self.image_processing_tester ,    equal_resolution=snake_case_       )
				for image in image_inputs:
					self.assertIsInstance(snake_case_ ,    Image.Image       )

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

				__a			:  int		    =  self.image_processing_tester.get_expected_values(snake_case_       )

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

				# Test batched
				__a			:  Union[str, Any]		    =  self.image_processing_tester.get_expected_values(snake_case_ ,    batched=snake_case_       )

				__a			:  str		    =  image_processor(
				    snake_case_ ,    ['''semantic'''] * len(snake_case_       ) ,    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			: List[Any]       ):
				# Initialize image_processor
				__a			:  List[Any]		    =  self.image_processing_class(**self.image_processor_dict       )
				# create random numpy tensors
				__a			:  int		    =  prepare_image_inputs(self.image_processing_tester ,    equal_resolution=snake_case_ ,    numpify=snake_case_       )
				for image in image_inputs:
					self.assertIsInstance(snake_case_ ,    np.ndarray       )

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

				__a			:  str		    =  self.image_processing_tester.get_expected_values(snake_case_       )

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

				# Test batched
				__a			:  Optional[int]		    =  self.image_processing_tester.get_expected_values(snake_case_ ,    batched=snake_case_       )

				__a			:  List[Any]		    =  image_processor(
				    snake_case_ ,    ['''semantic'''] * len(snake_case_       ) ,    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			: List[Any]       ):
				# Initialize image_processor
				__a			:  List[str]		    =  self.image_processing_class(**self.image_processor_dict       )
				# create random PyTorch tensors
				__a			:  Optional[Any]		    =  prepare_image_inputs(self.image_processing_tester ,    equal_resolution=snake_case_ ,    torchify=snake_case_       )
				for image in image_inputs:
					self.assertIsInstance(snake_case_ ,    torch.Tensor       )

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

				__a			:  int		    =  self.image_processing_tester.get_expected_values(snake_case_       )

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

				# Test batched
				__a			:  str		    =  self.image_processing_tester.get_expected_values(snake_case_ ,    batched=snake_case_       )

				__a			:  int		    =  image_processor(
				    snake_case_ ,    ['''semantic'''] * len(snake_case_       ) ,    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			: List[str] ,    snake_case_			: Any=False ,    snake_case_			: List[str]=False ,    snake_case_			: int="np"       ):
				__a			:  int		    =  self.image_processing_class(**self.image_processor_dict       )
				# prepare image and target
				__a			:  Union[str, Any]		    =  self.image_processing_tester.num_labels
				__a			:  List[Any]		    =  None
				__a			:  Optional[Any]		    =  None
				__a			:  int		    =  prepare_image_inputs(self.image_processing_tester ,    equal_resolution=snake_case_       )
				if with_segmentation_maps:
					__a			:  Tuple		    =  num_labels
					if is_instance_map:
						__a			:  List[str]		    =  list(range(snake_case_       )       ) * 2
						__a			:  Dict		    =  dict(enumerate(snake_case_       )       )
					__a			:  List[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":
						__a			:  List[Any]		    =  [Image.fromarray(snake_case_       ) for annotation in annotations]

				__a			:  Tuple		    =  image_processor(
				    snake_case_ ,    ['''semantic'''] * len(snake_case_       ) ,    snake_case_ ,    return_tensors='''pt''' ,    instance_id_to_semantic_id=snake_case_ ,    pad_and_return_pixel_mask=snake_case_ ,    )

				return inputs
			def 				lowerCAmelCase      (self			: str       ):
				pass
			def 				lowerCAmelCase      (self			: int       ):
				def common(snake_case_			: Any=False ,    snake_case_			: Union[str, Any]=None       ):
					__a			:  int		    =  self.comm_get_image_processor_inputs(
					    with_segmentation_maps=snake_case_ ,    is_instance_map=snake_case_ ,    segmentation_type=snake_case_       )

					__a			:  int		    =  inputs['''mask_labels''']
					__a			:  Optional[Any]		    =  inputs['''class_labels''']
					__a			:  List[Any]		    =  inputs['''pixel_values''']
					__a			:  Optional[Any]		    =  inputs['''text_inputs''']

					# check the batch_size
					for mask_label, class_label, text_input in zip(snake_case_ ,    snake_case_ ,    snake_case_       ):
						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(snake_case_       ) ,    self.image_processing_tester.num_text       )

				common()
				common(is_instance_map=snake_case_       )
				common(is_instance_map=snake_case_ ,    segmentation_type='''pil'''       )
				common(is_instance_map=snake_case_ ,    segmentation_type='''pil'''       )
			def 				lowerCAmelCase      (self			: Dict       ):
				__a			:  Dict		    =  np.zeros((2_0, 5_0)       )
				__a			:  Tuple		    =  1
				__a			:  int		    =  1
				__a			:  List[Any]		    =  1

				__a			:  Dict		    =  binary_mask_to_rle(snake_case_       )
				self.assertEqual(len(snake_case_       ) ,    4       )
				self.assertEqual(rle[0] ,    2_1       )
				self.assertEqual(rle[1] ,    4_5       )
			def 				lowerCAmelCase      (self			: Dict       ):
				__a			:  Dict		    =  self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes ,    max_seq_length=7_7 ,    task_seq_length=7_7 ,    class_info_file='''ade20k_panoptic.json''' ,    num_text=self.image_processing_tester.num_text ,    repo_path='''shi-labs/oneformer_demo''' ,    )
				__a			:  str		    =  self.image_processing_tester.get_fake_oneformer_outputs()

				__a			:  Optional[int]		    =  fature_extractor.post_process_semantic_segmentation(snake_case_       )

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

				__a			:  List[str]		    =  [(1, 4) for i in range(self.image_processing_tester.batch_size       )]
				__a			:  str		    =  fature_extractor.post_process_semantic_segmentation(snake_case_ ,    target_sizes=snake_case_       )

				self.assertEqual(segmentation[0].shape ,    target_sizes[0]       )
			def 				lowerCAmelCase      (self			: Any       ):
				__a			:  List[Any]		    =  self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes ,    max_seq_length=7_7 ,    task_seq_length=7_7 ,    class_info_file='''ade20k_panoptic.json''' ,    num_text=self.image_processing_tester.num_text ,    repo_path='''shi-labs/oneformer_demo''' ,    )
				__a			:  Optional[Any]		    =  self.image_processing_tester.get_fake_oneformer_outputs()
				__a			:  List[Any]		    =  image_processor.post_process_instance_segmentation(snake_case_ ,    threshold=0       )

				self.assertTrue(len(snake_case_       ) == 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''']       ) ,    snake_case_       )
					self.assertEqual(
					    el['''segmentation'''].shape ,    (self.image_processing_tester.height, self.image_processing_tester.width)       )







			def 				lowerCAmelCase      (self			: Any       ):
				__a			:  List[str]		    =  self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes ,    max_seq_length=7_7 ,    task_seq_length=7_7 ,    class_info_file='''ade20k_panoptic.json''' ,    num_text=self.image_processing_tester.num_text ,    repo_path='''shi-labs/oneformer_demo''' ,    )
				__a			:  List[str]		    =  self.image_processing_tester.get_fake_oneformer_outputs()
				__a			:  List[Any]		    =  image_processor.post_process_panoptic_segmentation(snake_case_ ,    threshold=0       )

				self.assertTrue(len(snake_case_       ) == 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''']       ) ,    snake_case_       )
					self.assertEqual(
					    el['''segmentation'''].shape ,    (self.image_processing_tester.height, self.image_processing_tester.width)       )


def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ):

 def merge(lowerCAmelCase__							:    list  ,       lowerCAmelCase__							:    list  ) -> 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(lowerCAmelCase__  ) <= 1:
  return collection
 __a			:  str		    =  len(lowerCAmelCase__  ) // 2
 return merge(merge_sort(collection[:mid]  )  ,       merge_sort(collection[mid:]  )  )


if __name__ == "__main__":
      import doctest

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