






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  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase="shi-labs/oneformer_demo"       ) ->			str:

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







class 				UpperCAmelCase_  (		unittest.TestCase		):




     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	,						):
          UpperCAmelCase__      :  Any				      =			parent
          UpperCAmelCase__      :  Tuple				      =			batch_size
          UpperCAmelCase__      :  List[Any]				      =			num_channels
          UpperCAmelCase__      :  str				      =			min_resolution
          UpperCAmelCase__      :  Dict				      =			max_resolution
          UpperCAmelCase__      :  List[str]				      =			do_resize
          UpperCAmelCase__      :  Union[str, Any]				      =			{"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
          UpperCAmelCase__      :  str				      =			do_normalize
          UpperCAmelCase__      :  Optional[Any]				      =			image_mean
          UpperCAmelCase__      :  Any				      =			image_std
          UpperCAmelCase__      :  str				      =			class_info_file
          UpperCAmelCase__      :  Tuple				      =			prepare_metadata(_lowerCAmelCase	,						_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			num_text
          UpperCAmelCase__      :  Dict				      =			repo_path

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




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

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

          return expected_height, expected_width




     def   __UpperCAmelCase  (				self		):
          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_  (		__lowerCamelCase							,      unittest.TestCase		):
     __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   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[int]				      =			OneFormerImageProcessorTester(self		)




     @property
     def   __UpperCAmelCase  (				self		):
          return self.image_processing_tester.prepare_image_processor_dict()




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Tuple				      =			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   __UpperCAmelCase  (				self		):
          pass




     def   __UpperCAmelCase  (				self		):
          # Initialize image_processor
          UpperCAmelCase__      :  Union[str, Any]				      =			self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          UpperCAmelCase__      :  Optional[Any]				      =			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__      :  Optional[int]				      =			image_processor(image_inputs[0]	,						["""semantic"""]	,						return_tensors="""pt"""		).pixel_values

          UpperCAmelCase__      ,		UpperCAmelCase__      :  Optional[Any]				      =			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__      :  Optional[Any]				      =			self.image_processing_tester.get_expected_values(_lowerCAmelCase	,						batched=_lowerCAmelCase		)

          UpperCAmelCase__      :  str				      =			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   __UpperCAmelCase  (				self		):
          # Initialize image_processor
          UpperCAmelCase__      :  Any				      =			self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          UpperCAmelCase__      :  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
          UpperCAmelCase__      :  Optional[Any]				      =			image_processor(image_inputs[0]	,						["""semantic"""]	,						return_tensors="""pt"""		).pixel_values

          UpperCAmelCase__      ,		UpperCAmelCase__      :  List[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
          UpperCAmelCase__      ,		UpperCAmelCase__      :  Union[str, Any]				      =			self.image_processing_tester.get_expected_values(_lowerCAmelCase	,						batched=_lowerCAmelCase		)

          UpperCAmelCase__      :  Optional[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   __UpperCAmelCase  (				self		):
          # Initialize image_processor
          UpperCAmelCase__      :  str				      =			self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          UpperCAmelCase__      :  Union[str, Any]				      =			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__      :  Optional[Any]				      =			image_processor(image_inputs[0]	,						["""semantic"""]	,						return_tensors="""pt"""		).pixel_values

          UpperCAmelCase__      ,		UpperCAmelCase__      :  List[Any]				      =			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__      :  List[str]				      =			self.image_processing_tester.get_expected_values(_lowerCAmelCase	,						batched=_lowerCAmelCase		)

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

          UpperCAmelCase__      :  List[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   __UpperCAmelCase  (				self		):
          pass




     def   __UpperCAmelCase  (				self		):
          def common(_lowerCAmelCase=False	,						_lowerCAmelCase=None		):
               UpperCAmelCase__      :  List[str]				      =			self.comm_get_image_processor_inputs(
                   with_segmentation_maps=_lowerCAmelCase	,						is_instance_map=_lowerCAmelCase	,						segmentation_type=_lowerCAmelCase		)

               UpperCAmelCase__      :  Any				      =			inputs["""mask_labels"""]
               UpperCAmelCase__      :  Optional[Any]				      =			inputs["""class_labels"""]
               UpperCAmelCase__      :  Any				      =			inputs["""pixel_values"""]
               UpperCAmelCase__      :  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   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  int				      =			np.zeros((20, 50)		)
          UpperCAmelCase__      :  List[Any]				      =			1
          UpperCAmelCase__      :  List[Any]				      =			1
          UpperCAmelCase__      :  int				      =			1

          UpperCAmelCase__      :  str				      =			binary_mask_to_rle(_lowerCAmelCase		)
          self.assertEqual(len(_lowerCAmelCase		)	,						4		)
          self.assertEqual(rle[0]	,						21		)
          self.assertEqual(rle[1]	,						45		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  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__      :  Tuple				      =			self.image_processing_tester.get_fake_oneformer_outputs()

          UpperCAmelCase__      :  str				      =			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__      :  Tuple				      =			[(1, 4) for i in range(self.image_processing_tester.batch_size		)]
          UpperCAmelCase__      :  Union[str, Any]				      =			fature_extractor.post_process_semantic_segmentation(_lowerCAmelCase	,						target_sizes=_lowerCAmelCase		)

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




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  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__      :  Optional[int]				      =			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   __UpperCAmelCase  (				self		):
          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__      :  List[Any]				      =			self.image_processing_tester.get_fake_oneformer_outputs()
          UpperCAmelCase__      :  List[Any]				      =			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)		)









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''AutoImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''AutoTokenizer'''




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__(snake_case ,  snake_case			)
											snake_case_     							=       self.image_processor




				def __call__(   self ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  **snake_case			):

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'			)

											if text is not None:
																		snake_case_     							=       self.tokenizer(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if images is not None:
																		snake_case_     							=       self.image_processor(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if text is not None and images is not None:
																		snake_case_     							=       image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**snake_case			) ,  tensor_type=snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




				@property
				def        a				(   self			):
											return ["input_ids", "attention_mask", "pixel_values"]




