


'''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 			_a			(		_lowercase      :			Optional[int]    ,      _lowercase      :			Union[str, Any]="shi-labs/oneformer_demo"						):





     '''simple docstring'''






     with open(hf_hub_download(_lowercase    ,      _lowercase    ,      repo_type='''dataset'''						)    ,      '''r'''						) as f:
          __UpperCAmelCase			:				List[str]													=    json.load(_lowercase						)
     __UpperCAmelCase			:				Optional[int]													=    {}
     __UpperCAmelCase			:				Optional[Any]													=    []
     __UpperCAmelCase			:				List[str]													=    []
     for key, info in class_info.items():
          __UpperCAmelCase			:				int													=    info['''name''']
          class_names.append(info['''name''']						)
          if info["isthing"]:
               thing_ids.append(int(_lowercase						)						)
     __UpperCAmelCase			:				Dict													=    thing_ids
     __UpperCAmelCase			:				Dict													=    class_names
     return metadata



class 			a       (	unittest.TestCase ):


     """simple docstring"""






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

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

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

          else:
               __UpperCAmelCase			:				Dict													=    []
               for image in image_inputs:
                    __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    self.get_expected_values([image]    )
                    expected_values.append((expected_height, expected_width)    )
               __UpperCAmelCase			:				str													=    max(snake_case							,				key=lambda snake_case    : item[0]    )[0]
               __UpperCAmelCase			:				Union[str, Any]													=    max(snake_case							,				key=lambda snake_case    : item[1]    )[1]

          return expected_height, expected_width






     def 				lowerCamelCase__				(       self  :	Tuple    )		->							List[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 			a       (	_a			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Tuple							     =     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  :	Any    )		->							Dict:
          __UpperCAmelCase			:				Dict													=    OneFormerImageProcessorTester(self    )

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

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							List[Any]:
          __UpperCAmelCase			:				List[Any]													=    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[Any]    )		->							Dict:
          pass

     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Dict:
          # Initialize image_processor
          __UpperCAmelCase			:				List[Any]													=    self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          __UpperCAmelCase			:				Tuple													=    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
          __UpperCAmelCase			:				Optional[int]													=    image_processor(image_inputs[0]							,				['''semantic''']							,				return_tensors='''pt'''    ).pixel_values

          __UpperCAmelCase							,     __UpperCAmelCase			:				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
          __UpperCAmelCase							,     __UpperCAmelCase			:				str													=    self.image_processing_tester.get_expected_values(snake_case							,				batched=snake_case    )

          __UpperCAmelCase			:				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  :	int    )		->							str:
          # Initialize image_processor
          __UpperCAmelCase			:				List[Any]													=    self.image_processing_class(**self.image_processor_dict    )
          # create random numpy tensors
          __UpperCAmelCase			:				str													=    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
          __UpperCAmelCase			:				Any													=    image_processor(image_inputs[0]							,				['''semantic''']							,				return_tensors='''pt'''    ).pixel_values

          __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[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
          __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    self.image_processing_tester.get_expected_values(snake_case							,				batched=snake_case    )

          __UpperCAmelCase			:				Optional[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[Any]    )		->							List[Any]:
          # Initialize image_processor
          __UpperCAmelCase			:				Union[str, Any]													=    self.image_processing_class(**self.image_processor_dict    )
          # create random PyTorch tensors
          __UpperCAmelCase			:				List[str]													=    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
          __UpperCAmelCase			:				Optional[int]													=    image_processor(image_inputs[0]							,				['''semantic''']							,				return_tensors='''pt'''    ).pixel_values

          __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[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
          __UpperCAmelCase							,     __UpperCAmelCase			:				List[str]													=    self.image_processing_tester.get_expected_values(snake_case							,				batched=snake_case    )

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

          __UpperCAmelCase			:				Any													=    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  :	Optional[Any]    )		->							List[str]:
          pass

     def 				lowerCamelCase__				(       self  :	Any    )		->							Any:
          def common(snake_case  :	Dict=False							,				snake_case  :	Optional[int]=None    ):
               __UpperCAmelCase			:				Any													=    self.comm_get_image_processor_inputs(
                   with_segmentation_maps=snake_case							,				is_instance_map=snake_case							,				segmentation_type=snake_case    )

               __UpperCAmelCase			:				List[str]													=    inputs['''mask_labels''']
               __UpperCAmelCase			:				Tuple													=    inputs['''class_labels''']
               __UpperCAmelCase			:				Union[str, Any]													=    inputs['''pixel_values''']
               __UpperCAmelCase			:				Optional[int]													=    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  :	Optional[Any]    )		->							Dict:
          __UpperCAmelCase			:				Tuple													=    np.zeros((20, 50)    )
          __UpperCAmelCase			:				str													=    1
          __UpperCAmelCase			:				Tuple													=    1
          __UpperCAmelCase			:				int													=    1

          __UpperCAmelCase			:				Optional[int]													=    binary_mask_to_rle(snake_case    )
          self.assertEqual(len(snake_case    )							,				4    )
          self.assertEqual(rle[0]							,				21    )
          self.assertEqual(rle[1]							,				45    )

     def 				lowerCamelCase__				(       self  :	str    )		->							Union[str, Any]:
          __UpperCAmelCase			:				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'''							,				)
          __UpperCAmelCase			:				Optional[Any]													=    self.image_processing_tester.get_fake_oneformer_outputs()

          __UpperCAmelCase			:				List[str]													=    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,
              )							,				)

          __UpperCAmelCase			:				Tuple													=    [(1, 4) for i in range(self.image_processing_tester.batch_size    )]
          __UpperCAmelCase			:				Optional[Any]													=    fature_extractor.post_process_semantic_segmentation(snake_case							,				target_sizes=snake_case    )

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

     def 				lowerCamelCase__				(       self  :	str    )		->							Optional[int]:
          __UpperCAmelCase			:				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'''							,				)
          __UpperCAmelCase			:				Optional[int]													=    self.image_processing_tester.get_fake_oneformer_outputs()
          __UpperCAmelCase			:				Dict													=    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  :	Optional[Any]    )		->							Dict:
          __UpperCAmelCase			:				List[Any]													=    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			:				str													=    self.image_processing_tester.get_fake_oneformer_outputs()
          __UpperCAmelCase			:				Tuple													=    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)    )



'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Union[str, Any]							,				snake_case  :	List[Any]							,				snake_case  :	int							,				snake_case  :	int    )		->							List[Any]:
          if dst_width < 0 or dst_height < 0:
               raise ValueError('''Destination width/height should be > 0'''    )

          __UpperCAmelCase			:				str													=    img
          __UpperCAmelCase			:				List[Any]													=    img.shape[1]
          __UpperCAmelCase			:				Optional[Any]													=    img.shape[0]
          __UpperCAmelCase			:				Dict													=    dst_width
          __UpperCAmelCase			:				List[str]													=    dst_height

          __UpperCAmelCase			:				Union[str, Any]													=    self.src_w / self.dst_w
          __UpperCAmelCase			:				List[str]													=    self.src_h / self.dst_h

          __UpperCAmelCase			:				Optional[int]													=    (
              np.ones((self.dst_h, self.dst_w, 3)							,				np.uinta    ) * 255
          )

     def 				lowerCamelCase__				(       self  :	Any    )		->							str:
          for i in range(self.dst_h    ):
               for j in range(self.dst_w    ):
                    __UpperCAmelCase			:				Any													=    self.img[self.get_y(snake_case    )][self.get_x(snake_case    )]

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	int    )		->							int:
          return int(self.ratio_x * x    )






     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	int    )		->							int:
          return int(self.ratio_y * y    )


if __name__ == "__main__":
      __UpperCAmelCase       ,					__UpperCAmelCase    :int				       =     8_0_0, 6_0_0
      __UpperCAmelCase    :Dict				       =     imread("image_data/lena.jpg", 1)
      __UpperCAmelCase    :int				       =     NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
      )
      waitKey(0)
      destroyAllWindows()