






def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						float:
       if mass < 0:
              raise ValueError("""The mass of a body cannot be negative""" )
       return 0.5 * mass * abs(__snake_case ) * abs(__snake_case )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)







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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case="shi-labs/oneformer_demo" )				->						Any:
       with open(hf_hub_download(__snake_case   ,      __snake_case   ,      repo_type="""dataset""" )   ,      """r""" ) as f:
              _UpperCAmelCase							=    json.load(__snake_case )
       _UpperCAmelCase							=    {}
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for key, info in class_info.items():
              _UpperCAmelCase							=    info["""name"""]
              class_names.append(info["""name"""] )
              if info["isthing"]:
                     thing_ids.append(int(__snake_case ) )
       _UpperCAmelCase							=    thing_ids
       _UpperCAmelCase							=    class_names
       return metadata
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			List[Any]=7       ,				lowerCamelCase		:			str=3       ,				lowerCamelCase		:			Union[str, Any]=30       ,				lowerCamelCase		:			Optional[int]=400       ,				lowerCamelCase		:			Union[str, Any]=None       ,				lowerCamelCase		:			int=True       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			Tuple=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			Tuple=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			Tuple=10       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Union[str, Any]=255       ,				lowerCamelCase		:			Tuple="shi-labs/oneformer_demo"       ,				lowerCamelCase		:			Tuple="ade20k_panoptic.json"       ,				lowerCamelCase		:			Optional[Any]=10       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    min_resolution
         _UpperCAmelCase							=    max_resolution
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean
         _UpperCAmelCase							=    image_std
         _UpperCAmelCase							=    class_info_file
         _UpperCAmelCase							=    prepare_metadata(lowerCamelCase       ,				lowerCamelCase							)
         _UpperCAmelCase							=    num_text
         _UpperCAmelCase							=    repo_path

         # for the post_process_functions
         _UpperCAmelCase							=    2
         _UpperCAmelCase							=    10
         _UpperCAmelCase							=    10
         _UpperCAmelCase							=    3
         _UpperCAmelCase							=    4
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    do_reduce_labels
         _UpperCAmelCase							=    ignore_index


  def  lowerCamelCase       (      self		:			int							)						->	Union[str, Any]:


         """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,
             "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       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Tuple=False							)						->	Any:


         """simple docstring"""
         if not batched:
                _UpperCAmelCase							=    image_inputs[0]
                if isinstance(lowerCamelCase       ,				Image.Image							):
                       _UpperCAmelCase	,			_UpperCAmelCase							=    image.size
                else:
                       _UpperCAmelCase	,			_UpperCAmelCase							=    image.shape[1], image.shape[2]
                if w < h:
                       _UpperCAmelCase							=    int(self.size["""shortest_edge"""] * h / w							)
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                elif w > h:
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                       _UpperCAmelCase							=    int(self.size["""shortest_edge"""] * w / h							)
                else:
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]
                       _UpperCAmelCase							=    self.size["""shortest_edge"""]

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

         return expected_height, expected_width





  def  lowerCamelCase       (      self		:			str							)						->	Optional[int]:


         """simple docstring"""
         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 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







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


  def  lowerCamelCase       (      self		:			Dict							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    OneFormerImageProcessorTester(self							)


  @property
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Dict:


         """simple docstring"""
         return self.image_processing_tester.prepare_image_processor_dict()


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    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		:			Tuple							)						->	Any:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[int]:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processing_tester       ,				equal_resolution=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				Image.Image							)

         # Test not batched input
         _UpperCAmelCase							=    image_processor(image_inputs[0]       ,				["""semantic"""]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    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
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    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		:			List[Any]							)						->	List[Any]:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    image_processor(image_inputs[0]       ,				["""semantic"""]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    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
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    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		:			List[str]							)						->	Tuple:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    image_processor(image_inputs[0]       ,				["""semantic"""]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    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
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    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		:			Tuple       ,				lowerCamelCase		:			Tuple=False       ,				lowerCamelCase		:			Union[str, Any]=False       ,				lowerCamelCase		:			Union[str, Any]="np"							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # prepare image and target
         _UpperCAmelCase							=    self.image_processing_tester.num_labels
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processing_tester       ,				equal_resolution=lowerCamelCase							)
         if with_segmentation_maps:
                _UpperCAmelCase							=    num_labels
                if is_instance_map:
                       _UpperCAmelCase							=    list(range(lowerCamelCase							)							) * 2
                       _UpperCAmelCase							=    dict(enumerate(lowerCamelCase							)							)
                _UpperCAmelCase							=    [
                    np.random.randint(0       ,				high * 2       ,				(img.size[1], img.size[0])							).astype(np.uinta							) for img in image_inputs
                ]
                if segmentation_type == "pil":
                       _UpperCAmelCase							=    [Image.fromarray(lowerCamelCase							) for annotation in annotations]

         _UpperCAmelCase							=    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		:			Any							)						->	Dict:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Dict							)						->	List[Any]:


         """simple docstring"""
         def common(lowerCamelCase		:			List[Any]=False       ,				lowerCamelCase		:			List[Any]=None							):
                _UpperCAmelCase							=    self.comm_get_image_processor_inputs(
                    with_segmentation_maps=lowerCamelCase       ,				is_instance_map=lowerCamelCase       ,				segmentation_type=lowerCamelCase							)

                _UpperCAmelCase							=    inputs["""mask_labels"""]
                _UpperCAmelCase							=    inputs["""class_labels"""]
                _UpperCAmelCase							=    inputs["""pixel_values"""]
                _UpperCAmelCase							=    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		:			int							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    np.zeros((20, 50)							)
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    1

         _UpperCAmelCase							=    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]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    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"""       ,				)
         _UpperCAmelCase							=    self.image_processing_tester.get_fake_oneformer_outputs()

         _UpperCAmelCase							=    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,
             )       ,				)

         _UpperCAmelCase							=    [(1, 4) for i in range(self.image_processing_tester.batch_size							)]
         _UpperCAmelCase							=    fature_extractor.post_process_semantic_segmentation(lowerCamelCase       ,				target_sizes=lowerCamelCase							)

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


  def  lowerCamelCase       (      self		:			Dict							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    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"""       ,				)
         _UpperCAmelCase							=    self.image_processing_tester.get_fake_oneformer_outputs()
         _UpperCAmelCase							=    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		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    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"""       ,				)
         _UpperCAmelCase							=    self.image_processing_tester.get_fake_oneformer_outputs()
         _UpperCAmelCase							=    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)							)