





'''simple docstring'''



import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
 from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=13						,   lowerCamelCase=32						,   lowerCamelCase=3						,   lowerCamelCase=4						,   lowerCamelCase=[10, 20, 30, 40]						,   lowerCamelCase=[2, 2, 3, 2]						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=37						,   lowerCamelCase="gelu"						,   lowerCamelCase=10						,   lowerCamelCase=0.02						,   lowerCamelCase=["stage2", "stage3", "stage4"]						,   lowerCamelCase=[2, 3, 4]						,   lowerCamelCase=None						,   ):
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				image_size
       _snake_case     		=				num_channels
       _snake_case     		=				num_stages
       _snake_case     		=				hidden_sizes
       _snake_case     		=				depths
       _snake_case     		=				is_training
       _snake_case     		=				use_labels
       _snake_case     		=				intermediate_size
       _snake_case     		=				hidden_act
       _snake_case     		=				num_labels
       _snake_case     		=				initializer_range
       _snake_case     		=				out_features
       _snake_case     		=				out_indices
       _snake_case     		=				scope

     def 	UpperCamelCase(					self	):
       _snake_case     		=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

       _snake_case     		=				None
       if self.use_labels:
         _snake_case     		=				ids_tensor([self.batch_size]						,   self.num_labels	)

       _snake_case     		=				self.get_config()

       return config, pixel_values, labels

     def 	UpperCamelCase(					self	):
       return ConvNextVaConfig(
           num_channels=self.num_channels						,   hidden_sizes=self.hidden_sizes						,   depths=self.depths						,   num_stages=self.num_stages						,   hidden_act=self.hidden_act						,   is_decoder=lowerCamelCase						,   initializer_range=self.initializer_range						,   out_features=self.out_features						,   out_indices=self.out_indices						,   num_labels=self.num_labels						,   )

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				ConvNextVaModel(config=lowerCamelCase	)
       model.to(lowerCamelCase	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase	)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape						,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,   )

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				ConvNextVaForImageClassification(lowerCamelCase	)
       model.to(lowerCamelCase	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase						,   labels=lowerCamelCase	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				ConvNextVaBackbone(config=lowerCamelCase	)
       model.to(lowerCamelCase	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase	)

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps	)						,   len(config.out_features	)	)
       self.parent.assertListEqual(list(result.feature_maps[0].shape	)						,   [self.batch_size, self.hidden_sizes[1], 4, 4]	)

       # verify channels
       self.parent.assertEqual(len(model.channels	)						,   len(config.out_features	)	)
       self.parent.assertListEqual(model.channels						,   config.hidden_sizes[1:]	)

       # verify backbone works with out_features=None
       _snake_case     		=				None
       _snake_case     		=				ConvNextVaBackbone(config=lowerCamelCase	)
       model.to(lowerCamelCase	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase	)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps	)						,   1	)
       self.parent.assertListEqual(list(result.feature_maps[0].shape	)						,   [self.batch_size, self.hidden_sizes[-1], 1, 1]	)

       # verify channels
       self.parent.assertEqual(len(model.channels	)						,   1	)
       self.parent.assertListEqual(model.channels						,   [config.hidden_sizes[-1]]	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.prepare_config_and_inputs()
       _snake_case		,		_snake_case		,		_snake_case     		=				config_and_inputs
       _snake_case     		=				{"pixel_values": pixel_values}
       return config, inputs_dict






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.prepare_config_and_inputs()
       _snake_case		,		_snake_case		,		_snake_case     		=				config_and_inputs
       _snake_case     		=				{"pixel_values": pixel_values, "labels": labels}
       return config, inputs_dict







@require_torch
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    __UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   (
         (
             ConvNextVaModel,
             ConvNextVaForImageClassification,
             ConvNextVaBackbone,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__       :						Optional[int]      =   (
         {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCAmelCase__       :						Dict      =   False
     UpperCAmelCase__       :						str      =   False
     UpperCAmelCase__       :						Optional[int]      =   False
     UpperCAmelCase__       :						Any      =   False
     UpperCAmelCase__       :						Optional[int]      =   False

     def 	UpperCamelCase(					self	):
       _snake_case     		=				ConvNextVaModelTester(self	)
       _snake_case     		=				ConfigTester(self						,   config_class=lowerCamelCase						,   has_text_modality=lowerCamelCase						,   hidden_size=37	)

     def 	UpperCamelCase(					self	):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()

     def 	UpperCamelCase(					self	):
       return

     @unittest.skip(reason="ConvNextV2 does not use inputs_embeds"	)
     def 	UpperCamelCase(					self	):
       pass

     @unittest.skip(reason="ConvNextV2 does not support input and output embeddings"	)
     def 	UpperCamelCase(					self	):
       pass

     @unittest.skip(reason="ConvNextV2 does not use feedforward chunking"	)
     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       if not self.model_tester.is_training:
         return

       for model_class in self.all_model_classes:
         _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_with_labels()
         _snake_case     		=				True

         if model_class.__name__ in [
             *get_values(lowerCamelCase	),
             *get_values(lowerCamelCase	),
         ]:
           continue

         _snake_case     		=				model_class(lowerCamelCase	)
         model.to(lowerCamelCase	)
         model.train()
         _snake_case     		=				self._prepare_for_class(lowerCamelCase						,   lowerCamelCase						,   return_labels=lowerCamelCase	)
         _snake_case     		=				model(**lowerCamelCase	).loss
         loss.backward()

     def 	UpperCamelCase(					self	):
       if not self.model_tester.is_training:
         return

       for model_class in self.all_model_classes:
         _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_with_labels()
         _snake_case     		=				False
         _snake_case     		=				True

         if (
             model_class.__name__
             in [*get_values(lowerCamelCase	), *get_values(lowerCamelCase	)]
             or not model_class.supports_gradient_checkpointing
         ):
           continue

         _snake_case     		=				model_class(lowerCamelCase	)
         model.to(lowerCamelCase	)
         model.gradient_checkpointing_enable()
         model.train()
         _snake_case     		=				self._prepare_for_class(lowerCamelCase						,   lowerCamelCase						,   return_labels=lowerCamelCase	)
         _snake_case     		=				model(**lowerCamelCase	).loss
         loss.backward()

     def 	UpperCamelCase(					self	):
       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _snake_case     		=				model_class(lowerCamelCase	)
         _snake_case     		=				inspect.signature(model.forward	)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _snake_case     		=				[*signature.parameters.keys()]

         _snake_case     		=				["pixel_values"]
         self.assertListEqual(arg_names[:1]						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       def check_hidden_states_output(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
         _snake_case     		=				model_class(lowerCamelCase	)
         model.to(lowerCamelCase	)
         model.eval()

         with torch.no_grad():
           _snake_case     		=				model(**self._prepare_for_class(lowerCamelCase						,   lowerCamelCase	)	)

         _snake_case     		=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         _snake_case     		=				self.model_tester.num_stages
         self.assertEqual(len(lowerCamelCase	)						,   expected_num_stages + 1	)

         # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]	)						,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,   )

       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _snake_case     		=				True
         check_hidden_states_output(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _snake_case     		=				True

         check_hidden_states_output(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase	)






     @slow
     def 	UpperCamelCase(					self	):
       for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case     		=				ConvNextVaModel.from_pretrained(lowerCamelCase	)
         self.assertIsNotNone(lowerCamelCase	)







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
  return image




@require_torch
@require_vision
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     @cached_property
     def 	UpperCamelCase(					self	):
       return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224"	) if is_vision_available() else None






     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224"	).to(lowerCamelCase	)

       _snake_case     		=				self.default_image_processor
       _snake_case     		=				prepare_img()
       _snake_case     		=				preprocessor(images=lowerCamelCase						,   return_tensors="pt"	).to(lowerCamelCase	)

       # forward pass
       with torch.no_grad():
         _snake_case     		=				model(**lowerCamelCase	)

       # verify the logits
       _snake_case     		=				torch.Size((1, 1_000)	)
       self.assertEqual(outputs.logits.shape						,   lowerCamelCase	)

       _snake_case     		=				torch.tensor([0.9996, 0.1966, -0.4386]	).to(lowerCamelCase	)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]						,   lowerCamelCase						,   atol=1e-4	)	)









'''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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__="shi-labs/oneformer_demo"       ):







  '''simple docstring'''



  with open(hf_hub_download(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="dataset"       )					,	"r"       ) as f:
    _snake_case     		=				json.load(SCREAMING_SNAKE_CASE__       )
  _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(SCREAMING_SNAKE_CASE__       )       )
  _snake_case     		=				thing_ids
  _snake_case     		=				class_names
  return metadata


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     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						,   ):
       _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": 1_333} 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(lowerCamelCase						,   lowerCamelCase	)
       _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 	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:
         _snake_case     		=				image_inputs[0]
         if isinstance(lowerCamelCase						,   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(lowerCamelCase						,   key=lambda lowerCamelCase	: item[0]	)[0]
         _snake_case     		=				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       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						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
     UpperCAmelCase__       :						Tuple      =   image_processing_class

     def 	UpperCamelCase(					self	):
       _snake_case     		=				OneFormerImageProcessorTester(self	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				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
       _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=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   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(lowerCamelCase	)

       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(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				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
       _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=lowerCamelCase						,   numpify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   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(lowerCamelCase	)

       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(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				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
       _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=lowerCamelCase						,   torchify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   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(lowerCamelCase	)

       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(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				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"	):
       _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=lowerCamelCase	)
       if with_segmentation_maps:
         _snake_case     		=				num_labels
         if is_instance_map:
           _snake_case     		=				list(range(lowerCamelCase	)	) * 2
           _snake_case     		=				dict(enumerate(lowerCamelCase	)	)
         _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(lowerCamelCase	) for annotation in annotations]

       _snake_case     		=				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	):
         _snake_case     		=				self.comm_get_image_processor_inputs(
             with_segmentation_maps=lowerCamelCase						,   is_instance_map=lowerCamelCase						,   segmentation_type=lowerCamelCase	)

         _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(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	):
       _snake_case     		=				np.zeros((20, 50)	)
       _snake_case     		=				1
       _snake_case     		=				1
       _snake_case     		=				1

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

     def 	UpperCamelCase(					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(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,
           )						,   )

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

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

     def 	UpperCamelCase(					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(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	):
       _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(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)	)


