






import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_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 torch import nn

  from transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      MODEL_MAPPING,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
  )
  from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import DeiTImageProcessor
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Tuple       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			str=13       ,				lowerCamelCase		:			Union[str, Any]=30       ,				lowerCamelCase		:			Optional[Any]=2       ,				lowerCamelCase		:			Optional[int]=3       ,				lowerCamelCase		:			Optional[Any]=True       ,				lowerCamelCase		:			Any=True       ,				lowerCamelCase		:			List[str]=32       ,				lowerCamelCase		:			Optional[int]=5       ,				lowerCamelCase		:			Any=4       ,				lowerCamelCase		:			Optional[int]=37       ,				lowerCamelCase		:			Optional[Any]="gelu"       ,				lowerCamelCase		:			Optional[int]=0.1       ,				lowerCamelCase		:			List[str]=0.1       ,				lowerCamelCase		:			List[str]=10       ,				lowerCamelCase		:			Any=0.02       ,				lowerCamelCase		:			List[Any]=3       ,				lowerCamelCase		:			Dict=None       ,				lowerCamelCase		:			Tuple=2       ,				)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    patch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    type_sequence_label_size
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    scope
         _UpperCAmelCase							=    encoder_stride

         # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
         _UpperCAmelCase							=    (image_size // patch_size) ** 2
         _UpperCAmelCase							=    num_patches + 2


  def  lowerCamelCase       (      self		:			Any							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _UpperCAmelCase							=    None
         if self.use_labels:
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.type_sequence_label_size							)

         _UpperCAmelCase							=    self.get_config()

         return config, pixel_values, labels


  def  lowerCamelCase       (      self		:			int							)						->	Optional[Any]:


         """simple docstring"""
         return DeiTConfig(
             image_size=self.image_size       ,				patch_size=self.patch_size       ,				num_channels=self.num_channels       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				is_decoder=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				encoder_stride=self.encoder_stride       ,				)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Tuple							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    DeiTModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)							)


  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			List[Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    DeiTForMaskedImageModeling(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(
             result.reconstruction.shape       ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

         # test greyscale images
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    DeiTForMaskedImageModeling(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         _UpperCAmelCase							=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.reconstruction.shape       ,				(self.batch_size, 1, self.image_size, self.image_size)							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Union[str, Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.type_sequence_label_size
         _UpperCAmelCase							=    DeiTForImageClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)							)

         # test greyscale images
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    DeiTForImageClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         _UpperCAmelCase							=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)							)





  def  lowerCamelCase       (      self		:			int							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         (
             (
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			
         )							=    config_and_inputs
         _UpperCAmelCase							=    {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (
          DeiTModel,
          DeiTForImageClassification,
          DeiTForImageClassificationWithTeacher,
          DeiTForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  _lowerCamelCase    =  (
      {
          '''feature-extraction''': DeiTModel,
          '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
      }
      if is_torch_available()
      else {}
  )

  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    DeiTModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				has_text_modality=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Tuple:


         """simple docstring"""
         self.config_tester.run_common_tests()


  @unittest.skip(reason="""DeiT does not use inputs_embeds"""							)
  def  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         pass


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


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                self.assertIsInstance(model.get_input_embeddings()       ,				(nn.Module)							)
                _UpperCAmelCase							=    model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(lowerCamelCase       ,				nn.Linear							)							)


  def  lowerCamelCase       (      self		:			Any							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

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

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


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[str]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[Any]=False							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    super()._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)

         if return_labels:
                if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                       del inputs_dict["labels"]

         return inputs_dict


  def  lowerCamelCase       (      self		:			List[str]							)						->	Dict:


         """simple docstring"""
         if not self.model_tester.is_training:
                return

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase							=    True

         for model_class in self.all_model_classes:
                # DeiTForImageClassificationWithTeacher supports inference-only
                if (
                    model_class in get_values(lowerCamelCase							)
                    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
                ):
                       continue
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.train()
                _UpperCAmelCase							=    self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)
                _UpperCAmelCase							=    model(**lowerCamelCase							).loss
                loss.backward()


  def  lowerCamelCase       (      self		:			int							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
                return

         _UpperCAmelCase							=    False
         _UpperCAmelCase							=    True

         for model_class in self.all_model_classes:
                if model_class in get_values(lowerCamelCase							) or not model_class.supports_gradient_checkpointing:
                       continue
                # DeiTForImageClassificationWithTeacher supports inference-only
                if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                       continue
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.gradient_checkpointing_enable()
                model.to(lowerCamelCase							)
                model.train()
                _UpperCAmelCase							=    self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)
                _UpperCAmelCase							=    model(**lowerCamelCase							).loss
                loss.backward()


  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         _UpperCAmelCase							=    [
             {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
             {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
             {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
         ]

         for model_class in self.all_model_classes:
                if (
                    model_class
                    not in [
                        *get_values(lowerCamelCase							),
                        *get_values(lowerCamelCase							),
                    ]
                    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
                ):
                       continue

                for problem_type in problem_types:
                       with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}"""							):
                              _UpperCAmelCase							=    problem_type["""title"""]
                              _UpperCAmelCase							=    problem_type["""num_labels"""]

                              _UpperCAmelCase							=    model_class(lowerCamelCase							)
                              model.to(lowerCamelCase							)
                              model.train()

                              _UpperCAmelCase							=    self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)

                              if problem_type["num_labels"] > 1:
                                     _UpperCAmelCase							=    inputs["""labels"""].unsqueeze(1							).repeat(1       ,				problem_type["""num_labels"""]							)

                              _UpperCAmelCase							=    inputs["""labels"""].to(problem_type["""dtype"""]							)

                              # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                              # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                              # they have the same size." which is a symptom something in wrong for the regression problem.
                              # See https://github.com/huggingface/transformers/issues/11780
                              with warnings.catch_warnings(record=lowerCamelCase							) as warning_list:
                                     _UpperCAmelCase							=    model(**lowerCamelCase							).loss
                              for w in warning_list:
                                     if "Using a target size that is different to the input size" in str(w.message							):
                                            raise ValueError(
                                                f"""Something is going wrong in the regression problem: intercepted {w.message}"""							)

                              loss.backward()





  @slow
  def  lowerCamelCase       (      self		:			Tuple							)						->	int:


         """simple docstring"""
         for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    DeiTModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							)				->						Union[str, Any]:
       _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Any							)						->	Any:


         """simple docstring"""
         return (
             DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""							)
             if is_vision_available()
             else None
         )


  @slow
  def  lowerCamelCase       (      self		:			str							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""							).to(
             lowerCamelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

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

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 1000)							)
         self.assertEqual(outputs.logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor([-1.0266, 0.1912, -1.2861]							).to(lowerCamelCase							)

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





  @slow
  @require_accelerate
  @require_torch_gpu
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    DeiTModel.from_pretrained(
             """facebook/deit-base-distilled-patch16-224"""       ,				torch_dtype=torch.floataa       ,				device_map="""auto"""							)
         _UpperCAmelCase							=    self.default_image_processor

         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							)
         _UpperCAmelCase							=    inputs.pixel_values.to(lowerCamelCase							)

         # forward pass to make sure inference works in fp16
         with torch.no_grad():
                _UpperCAmelCase							=    model(lowerCamelCase							)







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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case="shi-labs/oneformer_demo" )				->						Any:
       with open(hf_hub_download(__snake_case   ,      __snake_case   ,      repo_type="""dataset""" )   ,      """r""" ) as f:
              _UpperCAmelCase							=    json.load(__snake_case )
       _UpperCAmelCase							=    {}
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for key, info in class_info.items():
              _UpperCAmelCase							=    info["""name"""]
              class_names.append(info["""name"""] )
              if info["isthing"]:
                     thing_ids.append(int(__snake_case ) )
       _UpperCAmelCase							=    thing_ids
       _UpperCAmelCase							=    class_names
       return metadata
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







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


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    min_resolution
         _UpperCAmelCase							=    max_resolution
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean
         _UpperCAmelCase							=    image_std
         _UpperCAmelCase							=    class_info_file
         _UpperCAmelCase							=    prepare_metadata(lowerCamelCase       ,				lowerCamelCase							)
         _UpperCAmelCase							=    num_text
         _UpperCAmelCase							=    repo_path

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


  def  lowerCamelCase       (      self		:			int							)						->	Union[str, Any]:


         """simple docstring"""
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "num_labels": self.num_labels,
             "do_reduce_labels": self.do_reduce_labels,
             "ignore_index": self.ignore_index,
             "class_info_file": self.class_info_file,
             "metadata": self.metadata,
             "num_text": self.num_text,
         }


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Tuple=False							)						->	Any:


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

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

         return expected_height, expected_width





  def  lowerCamelCase       (      self		:			str							)						->	Optional[int]:


         """simple docstring"""
         return OneFormerForUniversalSegmentationOutput(
             # +1 for null class
             class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)							)       ,				masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)							)       ,				)



@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  _lowerCamelCase    =  image_processing_class


  def  lowerCamelCase       (      self		:			Dict							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    OneFormerImageProcessorTester(self							)


  @property
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Dict:


         """simple docstring"""
         return self.image_processing_tester.prepare_image_processor_dict()


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[int]:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processing_tester       ,				equal_resolution=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				Image.Image							)

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

         _UpperCAmelCase	,			_UpperCAmelCase							=    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
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			List[Any]							)						->	List[Any]:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    image_processor(image_inputs[0]       ,				["""semantic"""]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    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
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			List[str]							)						->	Tuple:


         """simple docstring"""
         # Initialize image_processor
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    image_processor(image_inputs[0]       ,				["""semantic"""]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase	,			_UpperCAmelCase							=    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
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.image_processing_tester.get_expected_values(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Tuple=False       ,				lowerCamelCase		:			Union[str, Any]=False       ,				lowerCamelCase		:			Union[str, Any]="np"							)						->	str:


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

         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Any							)						->	Dict:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Dict							)						->	List[Any]:


         """simple docstring"""
         def common(lowerCamelCase		:			List[Any]=False       ,				lowerCamelCase		:			List[Any]=None							):
                _UpperCAmelCase							=    self.comm_get_image_processor_inputs(
                    with_segmentation_maps=lowerCamelCase       ,				is_instance_map=lowerCamelCase       ,				segmentation_type=lowerCamelCase							)

                _UpperCAmelCase							=    inputs["""mask_labels"""]
                _UpperCAmelCase							=    inputs["""class_labels"""]
                _UpperCAmelCase							=    inputs["""pixel_values"""]
                _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			int							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    np.zeros((20, 50)							)
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    1

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


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    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"""       ,				)
         _UpperCAmelCase							=    self.image_processing_tester.get_fake_oneformer_outputs()

         _UpperCAmelCase							=    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,
             )       ,				)

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

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


  def  lowerCamelCase       (      self		:			Dict							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    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"""       ,				)
         _UpperCAmelCase							=    self.image_processing_tester.get_fake_oneformer_outputs()
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    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"""       ,				)
         _UpperCAmelCase							=    self.image_processing_tester.get_fake_oneformer_outputs()
         _UpperCAmelCase							=    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)							)