






'''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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE="shi-labs/oneformer_demo"		)				->					str:
     """simple docstring"""




     with open(hf_hub_download(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  repo_type="""dataset"""		)				,  """r"""		) as f:
          __a       =   json.load(__SCREAMING_SNAKE_CASE		)
     __a       =   {}
     __a       =   []
     __a       =   []
     for key, info in class_info.items():
          __a       =   info["""name"""]
          class_names.append(info["""name"""]		)
          if info["isthing"]:
               thing_ids.append(int(__SCREAMING_SNAKE_CASE		)		)
     __a       =   thing_ids
     __a       =   class_names
     return metadata


class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  def __init__(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							List[str]=7				,							SCREAMING_SNAKE_CASE__     :							Dict=3				,							SCREAMING_SNAKE_CASE__     :							List[str]=3_0				,							SCREAMING_SNAKE_CASE__     :							List[str]=4_0_0				,							SCREAMING_SNAKE_CASE__     :							List[str]=None				,							SCREAMING_SNAKE_CASE__     :							Tuple=True				,							SCREAMING_SNAKE_CASE__     :							int=True				,							SCREAMING_SNAKE_CASE__     :							Dict=[0.5, 0.5, 0.5]				,							SCREAMING_SNAKE_CASE__     :							Tuple=[0.5, 0.5, 0.5]				,							SCREAMING_SNAKE_CASE__     :							int=1_0				,							SCREAMING_SNAKE_CASE__     :							int=False				,							SCREAMING_SNAKE_CASE__     :							Tuple=2_5_5				,							SCREAMING_SNAKE_CASE__     :							int="shi-labs/oneformer_demo"				,							SCREAMING_SNAKE_CASE__     :							Any="ade20k_panoptic.json"				,							SCREAMING_SNAKE_CASE__     :							Tuple=1_0				,							):





       '''simple docstring'''

       __a       =   parent
       __a       =   batch_size
       __a       =   num_channels
       __a       =   min_resolution
       __a       =   max_resolution
       __a       =   do_resize
       __a       =   {"""shortest_edge""": 3_2, """longest_edge""": 1_3_3_3} if size is None else size
       __a       =   do_normalize
       __a       =   image_mean
       __a       =   image_std
       __a       =   class_info_file
       __a       =   prepare_metadata(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)
       __a       =   num_text
       __a       =   repo_path

       # for the post_process_functions
       __a       =   2
       __a       =   1_0
       __a       =   1_0
       __a       =   3
       __a       =   4
       __a       =   num_labels
       __a       =   do_reduce_labels
       __a       =   ignore_index

  def __a						(					self     :							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 __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Dict=False						):





       '''simple docstring'''

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

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

       return expected_height, expected_width


  def __a						(					self     :							Any						):





       '''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  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



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

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   OneFormerImageProcessorTester(self						)

  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       return self.image_processing_tester.prepare_image_processor_dict()

  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""image_mean"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""image_std"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""do_resize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""size"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""ignore_index"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""class_info_file"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""num_text"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""repo_path"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""metadata"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""do_reduce_labels"""						)						)

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       pass

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       __a       =   prepare_image_inputs(self.image_processing_tester				,							equal_resolution=SCREAMING_SNAKE_CASE__						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE__				,							Image.Image						)

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

       __a ,						__a       =   self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__						)

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

       # Test batched
       __a ,						__a       =   self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__				,							batched=SCREAMING_SNAKE_CASE__						)

       __a       =   image_processor(
           SCREAMING_SNAKE_CASE__				,							["""semantic"""] * len(SCREAMING_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 __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       __a       =   prepare_image_inputs(self.image_processing_tester				,							equal_resolution=SCREAMING_SNAKE_CASE__				,							numpify=SCREAMING_SNAKE_CASE__						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE__				,							np.ndarray						)

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

       __a ,						__a       =   self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__						)

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

       # Test batched
       __a ,						__a       =   self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__				,							batched=SCREAMING_SNAKE_CASE__						)

       __a       =   image_processor(
           SCREAMING_SNAKE_CASE__				,							["""semantic"""] * len(SCREAMING_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 __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       __a       =   prepare_image_inputs(self.image_processing_tester				,							equal_resolution=SCREAMING_SNAKE_CASE__				,							torchify=SCREAMING_SNAKE_CASE__						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE__				,							torch.Tensor						)

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

       __a ,						__a       =   self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__						)

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

       # Test batched
       __a ,						__a       =   self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__				,							batched=SCREAMING_SNAKE_CASE__						)

       __a       =   image_processor(
           SCREAMING_SNAKE_CASE__				,							["""semantic"""] * len(SCREAMING_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 __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							List[Any]=False				,							SCREAMING_SNAKE_CASE__     :							int=False				,							SCREAMING_SNAKE_CASE__     :							Any="np"						):





       '''simple docstring'''

       __a       =   self.image_processing_class(**self.image_processor_dict						)
       # prepare image and target
       __a       =   self.image_processing_tester.num_labels
       __a       =   None
       __a       =   None
       __a       =   prepare_image_inputs(self.image_processing_tester				,							equal_resolution=SCREAMING_SNAKE_CASE__						)
       if with_segmentation_maps:
            __a       =   num_labels
            if is_instance_map:
                 __a       =   list(range(SCREAMING_SNAKE_CASE__						)						) * 2
                 __a       =   dict(enumerate(SCREAMING_SNAKE_CASE__						)						)
            __a       =   [
                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       =   [Image.fromarray(SCREAMING_SNAKE_CASE__						) for annotation in annotations]

       __a       =   image_processor(
           SCREAMING_SNAKE_CASE__				,							["""semantic"""] * len(SCREAMING_SNAKE_CASE__						)				,							SCREAMING_SNAKE_CASE__				,							return_tensors="""pt"""				,							instance_id_to_semantic_id=SCREAMING_SNAKE_CASE__				,							pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE__				,							)

       return inputs

  def __a						(					self     :							int						):





       '''simple docstring'''

       pass

  def __a						(					self     :							str						):





       '''simple docstring'''

       def common(SCREAMING_SNAKE_CASE__     :							Optional[int]=False				,							SCREAMING_SNAKE_CASE__     :							List[str]=None						):
            __a       =   self.comm_get_image_processor_inputs(
                with_segmentation_maps=SCREAMING_SNAKE_CASE__				,							is_instance_map=SCREAMING_SNAKE_CASE__				,							segmentation_type=SCREAMING_SNAKE_CASE__						)

            __a       =   inputs["""mask_labels"""]
            __a       =   inputs["""class_labels"""]
            __a       =   inputs["""pixel_values"""]
            __a       =   inputs["""text_inputs"""]

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

       common()
       common(is_instance_map=SCREAMING_SNAKE_CASE__						)
       common(is_instance_map=SCREAMING_SNAKE_CASE__				,							segmentation_type="""pil"""						)
       common(is_instance_map=SCREAMING_SNAKE_CASE__				,							segmentation_type="""pil"""						)

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   np.zeros((2_0, 5_0)						)
       __a       =   1
       __a       =   1
       __a       =   1

       __a       =   binary_mask_to_rle(SCREAMING_SNAKE_CASE__						)
       self.assertEqual(len(SCREAMING_SNAKE_CASE__						)				,							4						)
       self.assertEqual(rle[0]				,							2_1						)
       self.assertEqual(rle[1]				,							4_5						)

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   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       =   self.image_processing_tester.get_fake_oneformer_outputs()

       __a       =   fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE__						)

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

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

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

  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   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       =   self.image_processing_tester.get_fake_oneformer_outputs()
       __a       =   image_processor.post_process_instance_segmentation(SCREAMING_SNAKE_CASE__				,							threshold=0						)

       self.assertTrue(len(SCREAMING_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"""]						)				,							SCREAMING_SNAKE_CASE__						)
            self.assertEqual(
                el["""segmentation"""].shape				,							(self.image_processing_tester.height, self.image_processing_tester.width)						)


  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   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       =   self.image_processing_tester.get_fake_oneformer_outputs()
       __a       =   image_processor.post_process_panoptic_segmentation(SCREAMING_SNAKE_CASE__				,							threshold=0						)

       self.assertTrue(len(SCREAMING_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"""]						)				,							SCREAMING_SNAKE_CASE__						)
            self.assertEqual(
                el["""segmentation"""].shape				,							(self.image_processing_tester.height, self.image_processing_tester.width)						)










'''simple docstring'''







from ...processing_utils import ProcessorMixin


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Dict												=["""image_processor""", """feature_extractor"""]
  a_       :str												="""TvltImageProcessor"""
  a_       :str												="""TvltFeatureExtractor"""

  def __init__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       super().__init__(image_processor=SCREAMING_SNAKE_CASE__				,							feature_extractor=SCREAMING_SNAKE_CASE__						)

       __a       =   image_processor
       __a       =   feature_extractor

  def __call__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str=None				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=None				,							SCREAMING_SNAKE_CASE__     :							Tuple=None				,							SCREAMING_SNAKE_CASE__     :							List[Any]=None				,							SCREAMING_SNAKE_CASE__     :							Any=False				,							SCREAMING_SNAKE_CASE__     :							List[str]=False				,							*SCREAMING_SNAKE_CASE__     :							str				,							**SCREAMING_SNAKE_CASE__     :							Optional[int]				,							):





       '''simple docstring'''

       if images is None and audio is None:
            raise ValueError("""You need to specify either an `images` or `audio` input to process."""						)

       __a       =   None
       if images is not None:
            __a       =   self.image_processor(SCREAMING_SNAKE_CASE__				,							mask_pixel=SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
       if images_mixed is not None:
            __a       =   self.image_processor(SCREAMING_SNAKE_CASE__				,							is_mixed=SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
       if audio is not None:
            __a       =   self.feature_extractor(
                SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							sampling_rate=SCREAMING_SNAKE_CASE__				,							mask_audio=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

       __a       =   {}
       if audio is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       if images is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       if images_mixed_dict is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       return output_dict


  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   self.image_processor.model_input_names
       __a       =   self.feature_extractor.model_input_names
       return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names						)						)






