






"""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       (							__snake_case							:					Any						,   __snake_case							:					Tuple="shi-labs/oneformer_demo"      ):
		with open(hf_hub_download(UpperCAmelCase_						,   UpperCAmelCase_						,   repo_type='''dataset'''      )						,   '''r'''      ) as f:
				lowercase_					:					Tuple							=  json.load(UpperCAmelCase_      )
		lowercase_					:					Optional[int]							=  {}
		lowercase_					:					Union[str, Any]							=  []
		lowercase_					:					Optional[Any]							=  []
		for key, info in class_info.items():
				lowercase_					:					List[str]							=  info['''name''']
				class_names.append(info['''name''']      )
				if info["isthing"]:
						thing_ids.append(int(UpperCAmelCase_      )      )
		lowercase_					:					List[Any]							=  thing_ids
		lowercase_					:					Optional[Any]							=  class_names
		return metadata





class    _UpperCAmelCase			(					unittest.TestCase				):




					def __init__(							self	:		List[Any]       ,		A	:		Optional[Any]       ,		A	:		int=7       ,		A	:		str=3       ,		A	:		Tuple=30       ,		A	:		Tuple=4_00       ,		A	:		Dict=None       ,		A	:		Tuple=True       ,		A	:		List[Any]=True       ,		A	:		Tuple=[0.5, 0.5, 0.5]       ,		A	:		Tuple=[0.5, 0.5, 0.5]       ,		A	:		int=10       ,		A	:		str=False       ,		A	:		Union[str, Any]=2_55       ,		A	:		Any="shi-labs/oneformer_demo"       ,		A	:		Union[str, Any]="ade20k_panoptic.json"       ,		A	:		List[Any]=10       ,		)    ->    List[Any]:
							lowercase_					:					Tuple							=  parent
							lowercase_					:					Optional[Any]							=  batch_size
							lowercase_					:					Any							=  num_channels
							lowercase_					:					List[Any]							=  min_resolution
							lowercase_					:					List[Any]							=  max_resolution
							lowercase_					:					str							=  do_resize
							lowercase_					:					Optional[int]							=  {'''shortest_edge''': 32, '''longest_edge''': 13_33} if size is None else size
							lowercase_					:					Any							=  do_normalize
							lowercase_					:					Optional[Any]							=  image_mean
							lowercase_					:					List[Any]							=  image_std
							lowercase_					:					List[str]							=  class_info_file
							lowercase_					:					Dict							=  prepare_metadata(UpperCamelCase_       ,		UpperCamelCase_  )
							lowercase_					:					Union[str, Any]							=  num_text
							lowercase_					:					int							=  repo_path

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




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

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

							return expected_height, expected_width






					def     A    (							self	:		Dict  )    ->    Tuple:
							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			(					_A  ,		unittest.TestCase				):
					SCREAMING_SNAKE_CASE_						:							List[str]				       =  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_						:							List[Any]				       =  image_processing_class




					def     A    (							self	:		Union[str, Any]  )    ->    Optional[Any]:
							lowercase_					:					Tuple							=  OneFormerImageProcessorTester(self  )




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




					def     A    (							self	:		Tuple  )    ->    List[Any]:
							lowercase_					:					int							=  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     A    (							self	:		Dict  )    ->    Any:
							pass




					def     A    (							self	:		Optional[Any]  )    ->    Optional[Any]:
							# Initialize image_processor
							lowercase_					:					Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowercase_					:					Dict							=  prepare_image_inputs(self.image_processing_tester       ,		equal_resolution=UpperCamelCase_  )
							for image in image_inputs:
									self.assertIsInstance(UpperCamelCase_       ,		Image.Image  )

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

							lowercase_			,   lowercase_					:					Union[str, Any]							=  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
							lowercase_			,   lowercase_					:					Any							=  self.image_processing_tester.get_expected_values(UpperCamelCase_       ,		batched=UpperCamelCase_  )

							lowercase_					:					Optional[Any]							=  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     A    (							self	:		Tuple  )    ->    Optional[Any]:
							# Initialize image_processor
							lowercase_					:					Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowercase_					:					Dict							=  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
							lowercase_					:					Any							=  image_processor(image_inputs[0]       ,		['''semantic''']       ,		return_tensors='''pt'''  ).pixel_values

							lowercase_			,   lowercase_					:					List[Any]							=  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
							lowercase_			,   lowercase_					:					List[Any]							=  self.image_processing_tester.get_expected_values(UpperCamelCase_       ,		batched=UpperCamelCase_  )

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

							lowercase_			,   lowercase_					:					Any							=  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
							lowercase_			,   lowercase_					:					Optional[Any]							=  self.image_processing_tester.get_expected_values(UpperCamelCase_       ,		batched=UpperCamelCase_  )

							lowercase_					:					Optional[int]							=  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     A    (							self	:		Any       ,		A	:		Tuple=False       ,		A	:		List[Any]=False       ,		A	:		List[str]="np"  )    ->    List[Any]:
							lowercase_					:					Optional[Any]							=  self.image_processing_class(**self.image_processor_dict  )
							# prepare image and target
							lowercase_					:					List[str]							=  self.image_processing_tester.num_labels
							lowercase_					:					Union[str, Any]							=  None
							lowercase_					:					List[Any]							=  None
							lowercase_					:					Tuple							=  prepare_image_inputs(self.image_processing_tester       ,		equal_resolution=UpperCamelCase_  )
							if with_segmentation_maps:
									lowercase_					:					Any							=  num_labels
									if is_instance_map:
											lowercase_					:					str							=  list(range(UpperCamelCase_  )  ) * 2
											lowercase_					:					Tuple							=  dict(enumerate(UpperCamelCase_  )  )
									lowercase_					:					Union[str, Any]							=  [
									    np.random.randint(0       ,		high * 2       ,		(img.size[1], img.size[0])  ).astype(np.uinta  ) for img in image_inputs
									]
									if segmentation_type == "pil":
											lowercase_					:					List[str]							=  [Image.fromarray(UpperCamelCase_  ) for annotation in annotations]

							lowercase_					:					Optional[int]							=  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     A    (							self	:		Tuple  )    ->    Union[str, Any]:
							pass




					def     A    (							self	:		Union[str, Any]  )    ->    List[Any]:
							def common(A	:		Optional[Any]=False       ,		A	:		Dict=None  ):
									lowercase_					:					str							=  self.comm_get_image_processor_inputs(
									    with_segmentation_maps=UpperCamelCase_       ,		is_instance_map=UpperCamelCase_       ,		segmentation_type=UpperCamelCase_  )

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

							lowercase_					:					List[str]							=  binary_mask_to_rle(UpperCamelCase_  )
							self.assertEqual(len(UpperCamelCase_  )       ,		4  )
							self.assertEqual(rle[0]       ,		21  )
							self.assertEqual(rle[1]       ,		45  )




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

							lowercase_					:					Tuple							=  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,
							    )       ,		)

							lowercase_					:					Tuple							=  [(1, 4) for i in range(self.image_processing_tester.batch_size  )]
							lowercase_					:					Tuple							=  fature_extractor.post_process_semantic_segmentation(UpperCamelCase_       ,		target_sizes=UpperCamelCase_  )

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




					def     A    (							self	:		List[str]  )    ->    str:
							lowercase_					:					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'''       ,		)
							lowercase_					:					int							=  self.image_processing_tester.get_fake_oneformer_outputs()
							lowercase_					:					List[Any]							=  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     A    (							self	:		Any  )    ->    Dict:
							lowercase_					:					List[str]							=  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'''       ,		)
							lowercase_					:					str							=  self.image_processing_tester.get_fake_oneformer_outputs()
							lowercase_					:					List[str]							=  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)  )










'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


a__					:			int          =     logging.get_logger(__name__)






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		None:
		warnings.warn(
		    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
		    ' Please use PerceiverImageProcessor instead.'      ,       UpperCamelCase_      ,       )
		super().__init__(*UpperCamelCase_      ,       **UpperCamelCase_		)



