

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			(				UpperCamelCase__     ,							UpperCamelCase__="shi-labs/oneformer_demo"   ):







							'''simple docstring'''






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







class 				lowercase       ( unittest.TestCase    ):




				def __init__(      self   ,     snake_case   ,     snake_case=7   ,     snake_case=3   ,     snake_case=30   ,     snake_case=400   ,     snake_case=None   ,     snake_case=True   ,     snake_case=True   ,     snake_case=[0.5, 0.5, 0.5]   ,     snake_case=[0.5, 0.5, 0.5]   ,     snake_case=10   ,     snake_case=False   ,     snake_case=255   ,     snake_case="shi-labs/oneformer_demo"   ,     snake_case="ade20k_panoptic.json"   ,     snake_case=10   ,     ):
											snake_case_           =					parent
											snake_case_           =					batch_size
											snake_case_           =					num_channels
											snake_case_           =					min_resolution
											snake_case_           =					max_resolution
											snake_case_           =					do_resize
											snake_case_           =					{'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
											snake_case_           =					do_normalize
											snake_case_           =					image_mean
											snake_case_           =					image_std
											snake_case_           =					class_info_file
											snake_case_           =					prepare_metadata(snake_case   ,     snake_case )
											snake_case_           =					num_text
											snake_case_           =					repo_path

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




				def 					a							(      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 					a							(      self   ,     snake_case   ,     snake_case=False ):
											if not batched:
																		snake_case_           =					image_inputs[0]
																		if isinstance(snake_case   ,     Image.Image ):
																									snake_case_       ,					snake_case_           =					image.size
																		else:
																									snake_case_       ,					snake_case_           =					image.shape[1], image.shape[2]
																		if w < h:
																									snake_case_           =					int(self.size['shortest_edge'] * h / w )
																									snake_case_           =					self.size['shortest_edge']
																		elif w > h:
																									snake_case_           =					self.size['shortest_edge']
																									snake_case_           =					int(self.size['shortest_edge'] * w / h )
																		else:
																									snake_case_           =					self.size['shortest_edge']
																									snake_case_           =					self.size['shortest_edge']

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

											return expected_height, expected_width




				def 					a							(      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 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_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
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    image_processing_class




				def 					a							(      self ):
											snake_case_           =					OneFormerImageProcessorTester(self )




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




				def 					a							(      self ):
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											self.assertTrue(hasattr(snake_case   ,     'image_mean' ) )
											self.assertTrue(hasattr(snake_case   ,     'image_std' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_normalize' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_resize' ) )
											self.assertTrue(hasattr(snake_case   ,     'size' ) )
											self.assertTrue(hasattr(snake_case   ,     'ignore_index' ) )
											self.assertTrue(hasattr(snake_case   ,     'class_info_file' ) )
											self.assertTrue(hasattr(snake_case   ,     'num_text' ) )
											self.assertTrue(hasattr(snake_case   ,     'repo_path' ) )
											self.assertTrue(hasattr(snake_case   ,     'metadata' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_reduce_labels' ) )




				def 					a							(      self ):
											pass




				def 					a							(      self ):
											# Initialize image_processor
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random PIL images
											snake_case_           =					prepare_image_inputs(self.image_processing_tester   ,     equal_resolution=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     Image.Image )

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

											snake_case_       ,					snake_case_           =					self.image_processing_tester.get_expected_values(snake_case )

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

											# Test batched
											snake_case_       ,					snake_case_           =					self.image_processing_tester.get_expected_values(snake_case   ,     batched=snake_case )

											snake_case_           =					image_processor(
											    snake_case   ,     ['semantic'] * len(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 ):
											# Initialize image_processor
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random numpy tensors
											snake_case_           =					prepare_image_inputs(self.image_processing_tester   ,     equal_resolution=snake_case   ,     numpify=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     np.ndarray )

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

											snake_case_       ,					snake_case_           =					self.image_processing_tester.get_expected_values(snake_case )

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

											# Test batched
											snake_case_       ,					snake_case_           =					self.image_processing_tester.get_expected_values(snake_case   ,     batched=snake_case )

											snake_case_           =					image_processor(
											    snake_case   ,     ['semantic'] * len(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 ):
											# Initialize image_processor
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random PyTorch tensors
											snake_case_           =					prepare_image_inputs(self.image_processing_tester   ,     equal_resolution=snake_case   ,     torchify=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     torch.Tensor )

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

											snake_case_       ,					snake_case_           =					self.image_processing_tester.get_expected_values(snake_case )

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

											# Test batched
											snake_case_       ,					snake_case_           =					self.image_processing_tester.get_expected_values(snake_case   ,     batched=snake_case )

											snake_case_           =					image_processor(
											    snake_case   ,     ['semantic'] * len(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   ,     snake_case=False   ,     snake_case=False   ,     snake_case="np" ):
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# prepare image and target
											snake_case_           =					self.image_processing_tester.num_labels
											snake_case_           =					None
											snake_case_           =					None
											snake_case_           =					prepare_image_inputs(self.image_processing_tester   ,     equal_resolution=snake_case )
											if with_segmentation_maps:
																		snake_case_           =					num_labels
																		if is_instance_map:
																									snake_case_           =					list(range(snake_case ) ) * 2
																									snake_case_           =					dict(enumerate(snake_case ) )
																		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":
																									snake_case_           =					[Image.fromarray(snake_case ) for annotation in annotations]

											snake_case_           =					image_processor(
											    snake_case   ,     ['semantic'] * len(snake_case )   ,     snake_case   ,     return_tensors='pt'   ,     instance_id_to_semantic_id=snake_case   ,     pad_and_return_pixel_mask=snake_case   ,     )

											return inputs




				def 					a							(      self ):
											pass




				def 					a							(      self ):
											def common(snake_case=False   ,     snake_case=None ):
																		snake_case_           =					self.comm_get_image_processor_inputs(
																		    with_segmentation_maps=snake_case   ,     is_instance_map=snake_case   ,     segmentation_type=snake_case )

																		snake_case_           =					inputs['mask_labels']
																		snake_case_           =					inputs['class_labels']
																		snake_case_           =					inputs['pixel_values']
																		snake_case_           =					inputs['text_inputs']

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

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




				def 					a							(      self ):
											snake_case_           =					np.zeros((20, 50) )
											snake_case_           =					1
											snake_case_           =					1
											snake_case_           =					1

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




				def 					a							(      self ):
											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'   ,     )
											snake_case_           =					self.image_processing_tester.get_fake_oneformer_outputs()

											snake_case_           =					fature_extractor.post_process_semantic_segmentation(snake_case )

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

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

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




				def 					a							(      self ):
											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'   ,     )
											snake_case_           =					self.image_processing_tester.get_fake_oneformer_outputs()
											snake_case_           =					image_processor.post_process_instance_segmentation(snake_case   ,     threshold=0 )

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




				def 					a							(      self ):
											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'   ,     )
											snake_case_           =					self.image_processing_tester.get_fake_oneformer_outputs()
											snake_case_           =					image_processor.post_process_panoptic_segmentation(snake_case   ,     threshold=0 )

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







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_UpperCAmelCase	:     Dict		       = logging.get_logger(__name__)







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Any     =    '''upernet'''




				def __init__(      self   ,     snake_case=None   ,     snake_case=512   ,     snake_case=0.02   ,     snake_case=[1, 2, 3, 6]   ,     snake_case=True   ,     snake_case=0.4   ,     snake_case=384   ,     snake_case=256   ,     snake_case=1   ,     snake_case=False   ,     snake_case=255   ,     **snake_case   ,     ):
											super().__init__(**snake_case )

											if backbone_config is None:
																		logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
																		snake_case_           =					CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
											elif isinstance(snake_case   ,     snake_case ):
																		snake_case_           =					backbone_config.get('model_type' )
																		snake_case_           =					CONFIG_MAPPING[backbone_model_type]
																		snake_case_           =					config_class.from_dict(snake_case )

											snake_case_           =					backbone_config
											snake_case_           =					hidden_size
											snake_case_           =					initializer_range
											snake_case_           =					pool_scales
											snake_case_           =					use_auxiliary_head
											snake_case_           =					auxiliary_loss_weight
											snake_case_           =					auxiliary_in_channels
											snake_case_           =					auxiliary_channels
											snake_case_           =					auxiliary_num_convs
											snake_case_           =					auxiliary_concat_input
											snake_case_           =					loss_ignore_index




				def 					a							(      self ):
											snake_case_           =					copy.deepcopy(self.__dict__ )
											snake_case_           =					self.backbone_config.to_dict()
											snake_case_           =					self.__class__.model_type
											return output




