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_  (		lowercase:				List[Any]	,       lowercase:				str="shi-labs/oneformer_demo"		)     ->					Optional[int]:







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




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




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



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			parent
    _UpperCamelCase:  Any    						=			batch_size
    _UpperCamelCase:  List[str]    						=			num_channels
    _UpperCamelCase:  Union[str, Any]    						=			min_resolution
    _UpperCamelCase:  str    						=			max_resolution
    _UpperCamelCase:  Tuple    						=			do_resize
    _UpperCamelCase:  Union[str, Any]    						=			{'''shortest_edge''': 32, '''longest_edge''': 1_333} if size is None else size
    _UpperCamelCase:  str    						=			do_normalize
    _UpperCamelCase:  List[str]    						=			image_mean
    _UpperCamelCase:  Dict    						=			image_std
    _UpperCamelCase:  Optional[int]    						=			class_info_file
    _UpperCamelCase:  Optional[int]    						=			prepare_metadata(_lowercase		,	_lowercase				)
    _UpperCamelCase:  str    						=			num_text
    _UpperCamelCase:  Optional[Any]    						=			repo_path

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



   def 	lowerCAmelCase    (						self       :	str				):



    """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       :	Optional[int]		,	_lowercase       :	str		,	_lowercase       :	int=False				):



    """simple docstring"""



    if not batched:
     _UpperCamelCase:  Optional[int]    						=			image_inputs[0]
     if isinstance(_lowercase		,	Image.Image				):
      _UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			image.size
     else:
      _UpperCamelCase						,				_UpperCamelCase:  Union[str, Any]    						=			image.shape[1], image.shape[2]
     if w < h:
      _UpperCamelCase:  Tuple    						=			int(self.size['''shortest_edge'''] * h / w				)
      _UpperCamelCase:  Optional[Any]    						=			self.size['''shortest_edge''']
     elif w > h:
      _UpperCamelCase:  List[Any]    						=			self.size['''shortest_edge''']
      _UpperCamelCase:  Any    						=			int(self.size['''shortest_edge'''] * w / h				)
     else:
      _UpperCamelCase:  Optional[int]    						=			self.size['''shortest_edge''']
      _UpperCamelCase:  Tuple    						=			self.size['''shortest_edge''']

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

    return expected_height, expected_width



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """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 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Tuple	      = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
   # only for test_image_processing_common.test_image_proc_to_json_string
   lowerCAmelCase   :     str	      = image_processing_class



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			OneFormerImageProcessorTester(self				)



   @property
   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    return self.image_processing_tester.prepare_image_processor_dict()



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			self.image_processing_class(**self.image_processor_dict				)
    self.assertTrue(hasattr(_lowercase		,	'''image_mean'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''image_std'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''do_normalize'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''do_resize'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''size'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''ignore_index'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''class_info_file'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''num_text'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''repo_path'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''metadata'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''do_reduce_labels'''				)				)



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			self.image_processing_class(**self.image_processor_dict				)
    # create random PIL images
    _UpperCamelCase:  int    						=			prepare_image_inputs(self.image_processing_tester		,	equal_resolution=_lowercase				)
    for image in image_inputs:
     self.assertIsInstance(_lowercase		,	Image.Image				)

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

    _UpperCamelCase						,				_UpperCamelCase:  Dict    						=			self.image_processing_tester.get_expected_values(_lowercase				)

    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(_lowercase		,	batched=_lowercase				)

    _UpperCamelCase:  int    						=			image_processor(
        _lowercase		,	['''semantic'''] * len(_lowercase				)		,	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       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			self.image_processing_class(**self.image_processor_dict				)
    # create random numpy tensors
    _UpperCamelCase:  Tuple    						=			prepare_image_inputs(self.image_processing_tester		,	equal_resolution=_lowercase		,	numpify=_lowercase				)
    for image in image_inputs:
     self.assertIsInstance(_lowercase		,	np.ndarray				)

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

    _UpperCamelCase						,				_UpperCamelCase:  List[Any]    						=			self.image_processing_tester.get_expected_values(_lowercase				)

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

    # Test batched
    _UpperCamelCase						,				_UpperCamelCase:  Tuple    						=			self.image_processing_tester.get_expected_values(_lowercase		,	batched=_lowercase				)

    _UpperCamelCase:  Union[str, Any]    						=			image_processor(
        _lowercase		,	['''semantic'''] * len(_lowercase				)		,	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]				):



    """simple docstring"""



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

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

    _UpperCamelCase						,				_UpperCamelCase:  List[str]    						=			self.image_processing_tester.get_expected_values(_lowercase				)

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

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

    _UpperCamelCase:  Optional[Any]    						=			image_processor(
        _lowercase		,	['''semantic'''] * len(_lowercase				)		,	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       :	Union[str, Any]		,	_lowercase       :	str=False		,	_lowercase       :	Dict=False		,	_lowercase       :	Any="np"				):



    """simple docstring"""



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

    _UpperCamelCase:  int    						=			image_processor(
        _lowercase		,	['''semantic'''] * len(_lowercase				)		,	_lowercase		,	return_tensors='''pt'''		,	instance_id_to_semantic_id=_lowercase		,	pad_and_return_pixel_mask=_lowercase		,	)

    return inputs



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



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

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

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

    common()
    common(is_instance_map=_lowercase				)
    common(is_instance_map=_lowercase		,	segmentation_type='''pil'''				)
    common(is_instance_map=_lowercase		,	segmentation_type='''pil'''				)



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			np.zeros((20, 50)				)
    _UpperCamelCase:  List[Any]    						=			1
    _UpperCamelCase:  List[Any]    						=			1
    _UpperCamelCase:  Optional[Any]    						=			1

    _UpperCamelCase:  List[Any]    						=			binary_mask_to_rle(_lowercase				)
    self.assertEqual(len(_lowercase				)		,	4				)
    self.assertEqual(rle[0]		,	21				)
    self.assertEqual(rle[1]		,	45				)



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			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:  Optional[Any]    						=			fature_extractor.post_process_semantic_segmentation(_lowercase				)

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

    _UpperCamelCase:  Optional[Any]    						=			[(1, 4) for i in range(self.image_processing_tester.batch_size				)]
    _UpperCamelCase:  List[str]    						=			fature_extractor.post_process_semantic_segmentation(_lowercase		,	target_sizes=_lowercase				)

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



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _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:  int    						=			self.image_processing_tester.get_fake_oneformer_outputs()
    _UpperCamelCase:  Tuple    						=			image_processor.post_process_instance_segmentation(_lowercase		,	threshold=0				)

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



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _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:  Union[str, Any]    						=			self.image_processing_tester.get_fake_oneformer_outputs()
    _UpperCamelCase:  Optional[int]    						=			image_processor.post_process_panoptic_segmentation(_lowercase		,	threshold=0				)

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




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 if torch.cuda.is_available():
  _UpperCamelCase:  Any    						=			torch.cuda.device_count()
 else:
  _UpperCamelCase:  Union[str, Any]    						=			0
 print(F"""Successfully ran on {num_gpus} GPUs"""		)


if __name__ == "__main__":
  main()