






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				int									=							"roc_bert"







			def __init__(     self:  Dict				,     UpperCAmelCase_:  Optional[Any]=30_522				,     UpperCAmelCase_:  Tuple=768				,     UpperCAmelCase_:  List[str]=12				,     UpperCAmelCase_:  List[str]=12				,     UpperCAmelCase_:  Dict=3_072				,     UpperCAmelCase_:  Optional[int]="gelu"				,     UpperCAmelCase_:  Tuple=0.1				,     UpperCAmelCase_:  List[str]=0.1				,     UpperCAmelCase_:  List[str]=512				,     UpperCAmelCase_:  Tuple=2				,     UpperCAmelCase_:  Optional[Any]=0.02				,     UpperCAmelCase_:  str=1E-12				,     UpperCAmelCase_:  Optional[int]=True				,     UpperCAmelCase_:  Optional[Any]=0				,     UpperCAmelCase_:  List[Any]="absolute"				,     UpperCAmelCase_:  Optional[int]=None				,     UpperCAmelCase_:  Dict=True				,     UpperCAmelCase_:  Union[str, Any]=True				,     UpperCAmelCase_:  Any=768				,     UpperCAmelCase_:  Union[str, Any]=910				,     UpperCAmelCase_:  Any=512				,     UpperCAmelCase_:  int=24_858				,     UpperCAmelCase_:  str=True				,     **UpperCAmelCase_:  str				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  vocab_size
									_SCREAMING_SNAKE_CASE     				=  max_position_embeddings
									_SCREAMING_SNAKE_CASE     				=  hidden_size
									_SCREAMING_SNAKE_CASE     				=  num_hidden_layers
									_SCREAMING_SNAKE_CASE     				=  num_attention_heads
									_SCREAMING_SNAKE_CASE     				=  intermediate_size
									_SCREAMING_SNAKE_CASE     				=  hidden_act
									_SCREAMING_SNAKE_CASE     				=  hidden_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  attention_probs_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  initializer_range
									_SCREAMING_SNAKE_CASE     				=  type_vocab_size
									_SCREAMING_SNAKE_CASE     				=  layer_norm_eps
									_SCREAMING_SNAKE_CASE     				=  use_cache
									_SCREAMING_SNAKE_CASE     				=  enable_pronunciation
									_SCREAMING_SNAKE_CASE     				=  enable_shape
									_SCREAMING_SNAKE_CASE     				=  pronunciation_embed_dim
									_SCREAMING_SNAKE_CASE     				=  pronunciation_vocab_size
									_SCREAMING_SNAKE_CASE     				=  shape_embed_dim
									_SCREAMING_SNAKE_CASE     				=  shape_vocab_size
									_SCREAMING_SNAKE_CASE     				=  concat_input
									_SCREAMING_SNAKE_CASE     				=  position_embedding_type
									_SCREAMING_SNAKE_CASE     				=  classifier_dropout
									super().__init__(pad_token_id=UpperCAmelCase_				,     **UpperCAmelCase_					)









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      (		snake_case__	,snake_case__="shi-labs/oneformer_demo"		)							->  Union[str, Any]:






						"""simple docstring"""





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



class 				__UpperCAmelCase		(unittest.TestCase   ):







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







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  parent
									_SCREAMING_SNAKE_CASE     				=  batch_size
									_SCREAMING_SNAKE_CASE     				=  num_channels
									_SCREAMING_SNAKE_CASE     				=  min_resolution
									_SCREAMING_SNAKE_CASE     				=  max_resolution
									_SCREAMING_SNAKE_CASE     				=  do_resize
									_SCREAMING_SNAKE_CASE     				=  {"""shortest_edge""": 32, """longest_edge""": 1_333} if size is None else size
									_SCREAMING_SNAKE_CASE     				=  do_normalize
									_SCREAMING_SNAKE_CASE     				=  image_mean
									_SCREAMING_SNAKE_CASE     				=  image_std
									_SCREAMING_SNAKE_CASE     				=  class_info_file
									_SCREAMING_SNAKE_CASE     				=  prepare_metadata(UpperCAmelCase_				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  num_text
									_SCREAMING_SNAKE_CASE     				=  repo_path

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







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''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 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  Union[str, Any]				,     UpperCAmelCase_:  List[str]=False					):







									'''simple docstring'''




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

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

									return expected_height, expected_width







			def 							UpperCamelCase				(     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 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Union[str, Any]									=							OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
			# only for test_image_processing_common.test_image_proc_to_json_string
			__snake_case    :				int									=							image_processing_class







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OneFormerImageProcessorTester(self					)







			@property
			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									return self.image_processing_tester.prepare_image_processor_dict()







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




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

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

									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  self.image_processing_tester.get_expected_values(UpperCAmelCase_					)

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

									# Test batched
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  self.image_processing_tester.get_expected_values(UpperCAmelCase_				,     batched=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  image_processor(
									    UpperCAmelCase_				,     ["""semantic"""] * len(UpperCAmelCase_					)				,     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:  int					):







									'''simple docstring'''




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

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

									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  self.image_processing_tester.get_expected_values(UpperCAmelCase_					)

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

									# Test batched
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  self.image_processing_tester.get_expected_values(UpperCAmelCase_				,     batched=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  image_processor(
									    UpperCAmelCase_				,     ["""semantic"""] * len(UpperCAmelCase_					)				,     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:  Tuple					):







									'''simple docstring'''




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

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

									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  self.image_processing_tester.get_expected_values(UpperCAmelCase_					)

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

									# Test batched
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  self.image_processing_tester.get_expected_values(UpperCAmelCase_				,     batched=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  image_processor(
									    UpperCAmelCase_				,     ["""semantic"""] * len(UpperCAmelCase_					)				,     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:  Optional[Any]				,     UpperCAmelCase_:  Tuple=False				,     UpperCAmelCase_:  Any=False				,     UpperCAmelCase_:  str="np"					):







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE     				=  image_processor(
									    UpperCAmelCase_				,     ["""semantic"""] * len(UpperCAmelCase_					)				,     UpperCAmelCase_				,     return_tensors="""pt"""				,     instance_id_to_semantic_id=UpperCAmelCase_				,     pad_and_return_pixel_mask=UpperCAmelCase_				,     )

									return inputs







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







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




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

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

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

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







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  np.zeros((20, 50)					)
									_SCREAMING_SNAKE_CASE     				=  1
									_SCREAMING_SNAKE_CASE     				=  1
									_SCREAMING_SNAKE_CASE     				=  1

									_SCREAMING_SNAKE_CASE     				=  binary_mask_to_rle(UpperCAmelCase_					)
									self.assertEqual(len(UpperCAmelCase_					)				,     4					)
									self.assertEqual(rle[0]				,     21					)
									self.assertEqual(rle[1]				,     45					)







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  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"""				,     )
									_SCREAMING_SNAKE_CASE     				=  self.image_processing_tester.get_fake_oneformer_outputs()

									_SCREAMING_SNAKE_CASE     				=  fature_extractor.post_process_semantic_segmentation(UpperCAmelCase_					)

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

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

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







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







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  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"""				,     )
									_SCREAMING_SNAKE_CASE     				=  self.image_processing_tester.get_fake_oneformer_outputs()
									_SCREAMING_SNAKE_CASE     				=  image_processor.post_process_instance_segmentation(UpperCAmelCase_				,     threshold=0					)

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







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  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"""				,     )
									_SCREAMING_SNAKE_CASE     				=  self.image_processing_tester.get_fake_oneformer_outputs()
									_SCREAMING_SNAKE_CASE     				=  image_processor.post_process_panoptic_segmentation(UpperCAmelCase_				,     threshold=0					)

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

