





import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
       from transformers.models.mobilevitva.modeling_mobilevitva import (
           MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
           make_divisible,
       )


if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->List[Any]:
          lowerCamelCase__       :						List[Any]        =							self.config_class(**self.inputs_dict      )
          self.parent.assertTrue(hasattr(A		, '''width_multiplier'''      )      )


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     Union[str, Any]		, A					:     Optional[Any]		, A					:     Any=1_3		, A					:     str=6_4		, A					:     List[Any]=2		, A					:     Tuple=3		, A					:     int="swish"		, A					:     str=3		, A					:     Dict=3_2		, A					:     int=0.1		, A					:     int=0.02		, A					:     Dict=True		, A					:     Tuple=True		, A					:     Optional[int]=1_0		, A					:     Tuple=None		, A					:     Dict=0.25		, A					:     List[Any]=0.0		, A					:     Optional[Any]=0.0		, )       ->Optional[Any]:
          lowerCamelCase__       :						Optional[int]        =							parent
          lowerCamelCase__       :						Dict        =							batch_size
          lowerCamelCase__       :						List[str]        =							image_size
          lowerCamelCase__       :						List[str]        =							patch_size
          lowerCamelCase__       :						List[Any]        =							num_channels
          lowerCamelCase__       :						List[str]        =							make_divisible(5_1_2 * width_multiplier		, divisor=8      )
          lowerCamelCase__       :						Dict        =							hidden_act
          lowerCamelCase__       :						int        =							conv_kernel_size
          lowerCamelCase__       :						str        =							output_stride
          lowerCamelCase__       :						Tuple        =							classifier_dropout_prob
          lowerCamelCase__       :						Dict        =							use_labels
          lowerCamelCase__       :						Optional[int]        =							is_training
          lowerCamelCase__       :						Any        =							num_labels
          lowerCamelCase__       :						List[str]        =							initializer_range
          lowerCamelCase__       :						Optional[Any]        =							scope
          lowerCamelCase__       :						Dict        =							width_multiplier
          lowerCamelCase__       :						int        =							ffn_dropout
          lowerCamelCase__       :						List[str]        =							attn_dropout







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->str:
          lowerCamelCase__       :						Optional[int]        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

          lowerCamelCase__       :						Optional[Any]        =							None
          lowerCamelCase__       :						List[Any]        =							None
          if self.use_labels:
             lowerCamelCase__       :						Any        =							ids_tensor([self.batch_size]		, self.num_labels      )
             lowerCamelCase__       :						Tuple        =							ids_tensor([self.batch_size, self.image_size, self.image_size]		, self.num_labels      )

          lowerCamelCase__       :						List[Any]        =							self.get_config()

          return config, pixel_values, labels, pixel_labels







       def 						__lowerCamelCase   (				self					:     Dict      )       ->str:
          return MobileViTVaConfig(
              image_size=self.image_size		, patch_size=self.patch_size		, num_channels=self.num_channels		, hidden_act=self.hidden_act		, conv_kernel_size=self.conv_kernel_size		, output_stride=self.output_stride		, classifier_dropout_prob=self.classifier_dropout_prob		, initializer_range=self.initializer_range		, width_multiplier=self.width_multiplier		, ffn_dropout=self.ffn_dropout_prob		, attn_dropout=self.attn_dropout_prob		, )







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     Optional[Any]		, A					:     Any		, A					:     int		, A					:     int      )       ->List[Any]:
          lowerCamelCase__       :						str        =							MobileViTVaModel(config=A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						Any        =							model(A      )
          self.parent.assertEqual(
              result.last_hidden_state.shape		, (
                  self.batch_size,
                  self.last_hidden_size,
                  self.image_size // self.output_stride,
                  self.image_size // self.output_stride,
              )		, )







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     List[Any]		, A					:     Optional[int]		, A					:     int		, A					:     Any      )       ->int:
          lowerCamelCase__       :						int        =							self.num_labels
          lowerCamelCase__       :						List[Any]        =							MobileViTVaForImageClassification(A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						List[str]        =							model(A		, labels=A      )
          self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     Optional[int]		, A					:     Optional[Any]		, A					:     int		, A					:     List[str]      )       ->Optional[Any]:
          lowerCamelCase__       :						Optional[Any]        =							self.num_labels
          lowerCamelCase__       :						Tuple        =							MobileViTVaForSemanticSegmentation(A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						Dict        =							model(A      )
          self.parent.assertEqual(
              result.logits.shape		, (
                  self.batch_size,
                  self.num_labels,
                  self.image_size // self.output_stride,
                  self.image_size // self.output_stride,
              )		, )
          lowerCamelCase__       :						Tuple        =							model(A		, labels=A      )
          self.parent.assertEqual(
              result.logits.shape		, (
                  self.batch_size,
                  self.num_labels,
                  self.image_size // self.output_stride,
                  self.image_size // self.output_stride,
              )		, )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Union[str, Any]:
          lowerCamelCase__       :						List[Any]        =							self.prepare_config_and_inputs()
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Optional[int]        =							config_and_inputs
          lowerCamelCase__       :						List[str]        =							{'''pixel_values''': pixel_values}
          return config, inputs_dict


@require_torch
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[Any]			   =   (
           (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
           if is_torch_available()
           else ()
       )

       _UpperCAmelCase				:  Tuple			   =   (
           {
               "feature-extraction": MobileViTVaModel,
               "image-classification": MobileViTVaForImageClassification,
               "image-segmentation": MobileViTVaForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       _UpperCAmelCase				:  Union[str, Any]			   =   False
       _UpperCAmelCase				:  List[Any]			   =   False
       _UpperCAmelCase				:  Union[str, Any]			   =   False
       _UpperCAmelCase				:  Optional[Any]			   =   False







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Any:
          lowerCamelCase__       :						Dict        =							MobileViTVaModelTester(self      )
          lowerCamelCase__       :						Optional[int]        =							MobileViTVaConfigTester(self		, config_class=A		, has_text_modality=A      )







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Tuple:
          self.config_tester.run_common_tests()







       @unittest.skip(reason='''MobileViTV2 does not use inputs_embeds'''      )
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[Any]:
          pass







       @unittest.skip(reason='''MobileViTV2 does not support input and output embeddings'''      )
       def 						__lowerCamelCase   (				self					:     Any      )       ->Optional[int]:
          pass







       @unittest.skip(reason='''MobileViTV2 does not output attentions'''      )
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Union[str, Any]:
          pass







       @require_torch_multi_gpu
       @unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.'''      )
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[int]:
          pass







       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Tuple:
          pass







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->str:
          lowerCamelCase__       ,							lowerCamelCase__       :						Any        =							self.model_tester.prepare_config_and_inputs_for_common()

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

             lowerCamelCase__       :						str        =							['''pixel_values''']
             self.assertListEqual(arg_names[:1]		, A      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Any:
          lowerCamelCase__       :						List[str]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Any:
          def check_hidden_states_output(A					:     Dict		, A					:     Tuple		, A					:     int      ):
             lowerCamelCase__       :						Dict        =							model_class(A      )
             model.to(A      )
             model.eval()

             with torch.no_grad():
                lowerCamelCase__       :						Union[str, Any]        =							model(**self._prepare_for_class(A		, A      )      )

             lowerCamelCase__       :						Optional[int]        =							outputs.hidden_states

             lowerCamelCase__       :						int        =							5
             self.assertEqual(len(A      )		, A      )

             # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
             # with the width and height being successively divided by 2.
             lowerCamelCase__       :						Optional[Any]        =							2
             for i in range(len(A      )      ):
                self.assertListEqual(
                    list(hidden_states[i].shape[-2:]      )		, [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]		, )
                divisor *= 2

             self.assertEqual(self.model_tester.output_stride		, divisor // 2      )

          lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             lowerCamelCase__       :						Any        =							True
             check_hidden_states_output(A		, A		, A      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowerCamelCase__       :						int        =							True

             check_hidden_states_output(A		, A		, A      )







       def 						__lowerCamelCase   (				self					:     str      )       ->Union[str, Any]:
          lowerCamelCase__       :						int        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[str]:
          lowerCamelCase__       :						Optional[int]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*A      )







       @slow
       def 						__lowerCamelCase   (				self					:     int      )       ->List[Any]:
          for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase__       :						Optional[int]        =							MobileViTVaModel.from_pretrained(A      )
             self.assertIsNotNone(A      )



def 	_a						(   )    ->    Union[str, Any]:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
   return image




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







       @cached_property
       def 						__lowerCamelCase   (				self					:     Any      )       ->Tuple:
          return (
              MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''      )
              if is_vision_available()
              else None
          )







       @slow
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->str:
          lowerCamelCase__       :						Dict        =							MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''      ).to(
              A      )

          lowerCamelCase__       :						str        =							self.default_image_processor
          lowerCamelCase__       :						List[Any]        =							prepare_img()
          lowerCamelCase__       :						Any        =							image_processor(images=A		, return_tensors='''pt'''      ).to(A      )

          # forward pass
          with torch.no_grad():
             lowerCamelCase__       :						int        =							model(**A      )

          # verify the logits
          lowerCamelCase__       :						str        =							torch.Size((1, 1_0_0_0)      )
          self.assertEqual(outputs.logits.shape		, A      )

          lowerCamelCase__       :						Dict        =							torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]      ).to(A      )

          self.assertTrue(torch.allclose(outputs.logits[0, :3]		, A		, atol=1e-4      )      )







       @slow
       def 						__lowerCamelCase   (				self					:     Any      )       ->Any:
          lowerCamelCase__       :						Optional[Any]        =							MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''      )
          lowerCamelCase__       :						List[str]        =							model.to(A      )

          lowerCamelCase__       :						Any        =							MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''      )

          lowerCamelCase__       :						Optional[int]        =							prepare_img()
          lowerCamelCase__       :						Any        =							image_processor(images=A		, return_tensors='''pt'''      ).to(A      )

          # forward pass
          with torch.no_grad():
             lowerCamelCase__       :						int        =							model(**A      )
          lowerCamelCase__       :						Tuple        =							outputs.logits

          # verify the logits
          lowerCamelCase__       :						Union[str, Any]        =							torch.Size((1, 2_1, 3_2, 3_2)      )
          self.assertEqual(logits.shape		, A      )

          lowerCamelCase__       :						str        =							torch.tensor(
              [
                  [[7.08_63, 7.15_25, 6.82_01], [6.69_31, 6.87_70, 6.89_33], [6.29_78, 7.03_66, 6.96_36]],
                  [[-3.71_34, -3.67_12, -3.66_75], [-3.58_25, -3.35_49, -3.47_77], [-3.34_35, -3.39_79, -3.28_57]],
                  [[-2.93_29, -2.80_03, -2.73_69], [-3.05_64, -2.47_80, -2.02_07], [-2.68_89, -1.92_98, -1.76_40]],
              ]		, device=A		, )

          self.assertTrue(torch.allclose(logits[0, :3, :3, :3]		, A		, atol=1e-4      )      )







       @slow
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Dict:
          lowerCamelCase__       :						Tuple        =							MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''      )
          lowerCamelCase__       :						List[str]        =							model.to(A      )

          lowerCamelCase__       :						Any        =							MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''      )

          lowerCamelCase__       :						int        =							prepare_img()
          lowerCamelCase__       :						Dict        =							image_processor(images=A		, return_tensors='''pt'''      ).to(A      )

          # forward pass
          with torch.no_grad():
             lowerCamelCase__       :						int        =							model(**A      )

          lowerCamelCase__       :						str        =							outputs.logits.detach().cpu()

          lowerCamelCase__       :						List[str]        =							image_processor.post_process_semantic_segmentation(outputs=A		, target_sizes=[(5_0, 6_0)]      )
          lowerCamelCase__       :						List[Any]        =							torch.Size((5_0, 6_0)      )
          self.assertEqual(segmentation[0].shape		, A      )

          lowerCamelCase__       :						Optional[int]        =							image_processor.post_process_semantic_segmentation(outputs=A      )
          lowerCamelCase__       :						List[Any]        =							torch.Size((3_2, 3_2)      )
          self.assertEqual(segmentation[0].shape		, A      )










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







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


class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def __init__(				self					:     Tuple		, A					:     Tuple		, A					:     List[Any]=7		, A					:     str=3		, A					:     List[str]=3_0		, A					:     Optional[int]=4_0_0		, A					:     int=None		, A					:     Tuple=True		, A					:     Dict=True		, A					:     Dict=[0.5, 0.5, 0.5]		, A					:     Tuple=[0.5, 0.5, 0.5]		, A					:     int=1_0		, A					:     List[str]=False		, A					:     Optional[Any]=2_5_5		, A					:     Union[str, Any]="shi-labs/oneformer_demo"		, A					:     Optional[Any]="ade20k_panoptic.json"		, A					:     str=1_0		, )       ->Dict:
          lowerCamelCase__       :						Dict        =							parent
          lowerCamelCase__       :						List[Any]        =							batch_size
          lowerCamelCase__       :						Dict        =							num_channels
          lowerCamelCase__       :						List[str]        =							min_resolution
          lowerCamelCase__       :						str        =							max_resolution
          lowerCamelCase__       :						Optional[Any]        =							do_resize
          lowerCamelCase__       :						Any        =							{'''shortest_edge''': 3_2, '''longest_edge''': 1_3_3_3} if size is None else size
          lowerCamelCase__       :						str        =							do_normalize
          lowerCamelCase__       :						List[str]        =							image_mean
          lowerCamelCase__       :						List[str]        =							image_std
          lowerCamelCase__       :						Optional[int]        =							class_info_file
          lowerCamelCase__       :						Any        =							prepare_metadata(A		, A      )
          lowerCamelCase__       :						str        =							num_text
          lowerCamelCase__       :						Dict        =							repo_path

          # for the post_process_functions
          lowerCamelCase__       :						str        =							2
          lowerCamelCase__       :						Union[str, Any]        =							1_0
          lowerCamelCase__       :						List[Any]        =							1_0
          lowerCamelCase__       :						List[Any]        =							3
          lowerCamelCase__       :						Tuple        =							4
          lowerCamelCase__       :						List[Any]        =							num_labels
          lowerCamelCase__       :						List[Any]        =							do_reduce_labels
          lowerCamelCase__       :						List[Any]        =							ignore_index







       def 						__lowerCamelCase   (				self					:     str      )       ->Dict:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "num_labels": self.num_labels,
              "do_reduce_labels": self.do_reduce_labels,
              "ignore_index": self.ignore_index,
              "class_info_file": self.class_info_file,
              "metadata": self.metadata,
              "num_text": self.num_text,
          }







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

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

          return expected_height, expected_width







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->List[str]:
          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      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[int]			   =   OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
       # only for test_image_processing_common.test_image_proc_to_json_string
       _UpperCAmelCase				:  Dict			   =   image_processing_class







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->str:
          lowerCamelCase__       :						Optional[int]        =							OneFormerImageProcessorTester(self      )







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







       def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
          lowerCamelCase__       :						List[str]        =							self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(A		, '''image_mean'''      )      )
          self.assertTrue(hasattr(A		, '''image_std'''      )      )
          self.assertTrue(hasattr(A		, '''do_normalize'''      )      )
          self.assertTrue(hasattr(A		, '''do_resize'''      )      )
          self.assertTrue(hasattr(A		, '''size'''      )      )
          self.assertTrue(hasattr(A		, '''ignore_index'''      )      )
          self.assertTrue(hasattr(A		, '''class_info_file'''      )      )
          self.assertTrue(hasattr(A		, '''num_text'''      )      )
          self.assertTrue(hasattr(A		, '''repo_path'''      )      )
          self.assertTrue(hasattr(A		, '''metadata'''      )      )
          self.assertTrue(hasattr(A		, '''do_reduce_labels'''      )      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Tuple:
          pass







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

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

          lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_expected_values(A		, batched=A      )

          lowerCamelCase__       :						List[str]        =							image_processor(
              A		, ['''semantic'''] * len(A      )		, return_tensors='''pt'''      ).pixel_values
          self.assertEqual(
              encoded_images.shape		, (
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )		, )







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Tuple:
          # Initialize image_processor
          lowerCamelCase__       :						Union[str, Any]        =							self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          lowerCamelCase__       :						List[str]        =							prepare_image_inputs(self.image_processing_tester		, equal_resolution=A		, numpify=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, np.ndarray      )

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

          lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							self.image_processing_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processing_tester.get_expected_values(A		, batched=A      )

          lowerCamelCase__       :						int        =							image_processor(
              A		, ['''semantic'''] * len(A      )		, return_tensors='''pt'''      ).pixel_values
          self.assertEqual(
              encoded_images.shape		, (
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )		, )







       def 						__lowerCamelCase   (				self					:     int      )       ->str:
          # Initialize image_processor
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          lowerCamelCase__       :						int        =							prepare_image_inputs(self.image_processing_tester		, equal_resolution=A		, torchify=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, torch.Tensor      )

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

          lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.image_processing_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_expected_values(A		, batched=A      )

          lowerCamelCase__       :						Any        =							image_processor(
              A		, ['''semantic'''] * len(A      )		, return_tensors='''pt'''      ).pixel_values
          self.assertEqual(
              encoded_images.shape		, (
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )		, )







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

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

          return inputs







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[Any]:
          pass







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Any:
          def common(A					:     Dict=False		, A					:     Tuple=None      ):
             lowerCamelCase__       :						str        =							self.comm_get_image_processor_inputs(
                 with_segmentation_maps=A		, is_instance_map=A		, segmentation_type=A      )

             lowerCamelCase__       :						Union[str, Any]        =							inputs['''mask_labels''']
             lowerCamelCase__       :						List[Any]        =							inputs['''class_labels''']
             lowerCamelCase__       :						List[str]        =							inputs['''pixel_values''']
             lowerCamelCase__       :						Union[str, Any]        =							inputs['''text_inputs''']

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

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







       def 						__lowerCamelCase   (				self					:     Any      )       ->Optional[int]:
          lowerCamelCase__       :						List[Any]        =							np.zeros((2_0, 5_0)      )
          lowerCamelCase__       :						Any        =							1
          lowerCamelCase__       :						Union[str, Any]        =							1
          lowerCamelCase__       :						List[str]        =							1

          lowerCamelCase__       :						str        =							binary_mask_to_rle(A      )
          self.assertEqual(len(A      )		, 4      )
          self.assertEqual(rle[0]		, 2_1      )
          self.assertEqual(rle[1]		, 4_5      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Dict:
          lowerCamelCase__       :						int        =							self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		, max_seq_length=7_7		, task_seq_length=7_7		, class_info_file='''ade20k_panoptic.json'''		, num_text=self.image_processing_tester.num_text		, repo_path='''shi-labs/oneformer_demo'''		, )
          lowerCamelCase__       :						str        =							self.image_processing_tester.get_fake_oneformer_outputs()

          lowerCamelCase__       :						List[Any]        =							fature_extractor.post_process_semantic_segmentation(A      )

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

          lowerCamelCase__       :						Any        =							[(1, 4) for i in range(self.image_processing_tester.batch_size      )]
          lowerCamelCase__       :						Optional[Any]        =							fature_extractor.post_process_semantic_segmentation(A		, target_sizes=A      )

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







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Tuple:
          lowerCamelCase__       :						Optional[Any]        =							self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		, max_seq_length=7_7		, task_seq_length=7_7		, class_info_file='''ade20k_panoptic.json'''		, num_text=self.image_processing_tester.num_text		, repo_path='''shi-labs/oneformer_demo'''		, )
          lowerCamelCase__       :						Optional[int]        =							self.image_processing_tester.get_fake_oneformer_outputs()
          lowerCamelCase__       :						Union[str, Any]        =							image_processor.post_process_instance_segmentation(A		, threshold=0      )

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







       def 						__lowerCamelCase   (				self					:     str      )       ->Dict:
          lowerCamelCase__       :						Union[str, Any]        =							self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		, max_seq_length=7_7		, task_seq_length=7_7		, class_info_file='''ade20k_panoptic.json'''		, num_text=self.image_processing_tester.num_text		, repo_path='''shi-labs/oneformer_demo'''		, )
          lowerCamelCase__       :						Tuple        =							self.image_processing_tester.get_fake_oneformer_outputs()
          lowerCamelCase__       :						List[str]        =							image_processor.post_process_panoptic_segmentation(A		, threshold=0      )

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



