





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowerCAmelCase_						   =    r"""
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `\" / \"`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `\" // \"`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `\"train\"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `\"compressed\"`)
            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and
            `\"compressed\"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
"""



@add_start_docstrings(UpperCAmelCase_							)
class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Tuple      					="""rag"""
  a_							:       str      					=True







  def __init__(      self      :					Union[str, Any]      ,				UpperCamelCase      :					int=None      ,				UpperCamelCase      :					str=True      ,				UpperCamelCase      :					List[str]=None      ,				UpperCamelCase      :					List[str]=None      ,				UpperCamelCase      :					List[Any]=None      ,				UpperCamelCase      :					Tuple=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					int=" / "      ,				UpperCamelCase      :					List[str]=" // "      ,				UpperCamelCase      :					Any=5      ,				UpperCamelCase      :					Optional[int]=3_00      ,				UpperCamelCase      :					Dict=7_68      ,				UpperCamelCase      :					List[Any]=8      ,				UpperCamelCase      :					Tuple="wiki_dpr"      ,				UpperCamelCase      :					List[Any]="train"      ,				UpperCamelCase      :					Optional[int]="compressed"      ,				UpperCamelCase      :					List[Any]=None      ,				UpperCamelCase      :					Optional[int]=None      ,				UpperCamelCase      :					Union[str, Any]=False      ,				UpperCamelCase      :					Tuple=False      ,				UpperCamelCase      :					Tuple=0.0      ,				UpperCamelCase      :					str=True      ,				UpperCamelCase      :					str=False      ,				UpperCamelCase      :					int=False      ,				UpperCamelCase      :					Union[str, Any]=False      ,				UpperCamelCase      :					Union[str, Any]=True      ,				UpperCamelCase      :					Optional[Any]=None      ,				**UpperCamelCase      :					str      ,				):


    '''simple docstring'''





    super().__init__(
        bos_token_id=UpperCamelCase      ,				pad_token_id=UpperCamelCase      ,				eos_token_id=UpperCamelCase      ,				decoder_start_token_id=UpperCamelCase      ,				forced_eos_token_id=UpperCamelCase      ,				is_encoder_decoder=UpperCamelCase      ,				prefix=UpperCamelCase      ,				vocab_size=UpperCamelCase      ,				**UpperCamelCase      ,				)
    assert (
        "question_encoder" in kwargs and "generator" in kwargs
    ), "Config has to be initialized with question_encoder and generator config"
    _snake_case   :				Tuple														=	kwargs.pop('question_encoder'							)
    _snake_case   :				int														=	question_encoder_config.pop('model_type'							)
    _snake_case   :				Dict														=	kwargs.pop('generator'							)
    _snake_case   :				Optional[Any]														=	decoder_config.pop('model_type'							)

    from ..auto.configuration_auto import AutoConfig

    _snake_case   :				Union[str, Any]														=	AutoConfig.for_model(UpperCamelCase      ,				**UpperCamelCase							)
    _snake_case   :				Dict														=	AutoConfig.for_model(UpperCamelCase      ,				**UpperCamelCase							)

    _snake_case   :				Tuple														=	reduce_loss
    _snake_case   :				Optional[Any]														=	label_smoothing
    _snake_case   :				List[str]														=	exclude_bos_score
    _snake_case   :				int														=	do_marginalize

    _snake_case   :				Any														=	title_sep
    _snake_case   :				Optional[Any]														=	doc_sep
    _snake_case   :				Union[str, Any]														=	n_docs
    _snake_case   :				Dict														=	max_combined_length

    _snake_case   :				List[str]														=	dataset
    _snake_case   :				Union[str, Any]														=	dataset_split
    _snake_case   :				Dict														=	index_name

    _snake_case   :				Any														=	retrieval_vector_size
    _snake_case   :				List[str]														=	retrieval_batch_size
    _snake_case   :				Dict														=	passages_path
    _snake_case   :				List[Any]														=	index_path
    _snake_case   :				Tuple														=	use_dummy_dataset

    _snake_case   :				int														=	output_retrieved

    _snake_case   :				str														=	do_deduplication

    _snake_case   :				Union[str, Any]														=	use_cache

    if self.forced_eos_token_id is None:
      _snake_case   :				Dict														=	getattr(self.generator      ,				'forced_eos_token_id'      ,				UpperCamelCase							)







  @classmethod
  def UpperCamelCase_ (      cls      :					Any      ,				UpperCamelCase      :					PretrainedConfig      ,				UpperCamelCase      :					PretrainedConfig      ,				**UpperCamelCase      :					Optional[int]							):


    '''simple docstring'''





    return cls(question_encoder=question_encoder_config.to_dict()      ,				generator=generator_config.to_dict()      ,				**UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	copy.deepcopy(self.__dict__							)
    _snake_case   :				Optional[int]														=	self.question_encoder.to_dict()
    _snake_case   :				int														=	self.generator.to_dict()
    _snake_case   :				str														=	self.__class__.model_type
    return output











import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

      if is_vision_available():
            from transformers import OneFormerImageProcessor
            from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
            from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
      from PIL import Image


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



class        _lowerCAmelCase    (			unittest.TestCase							):


  '''simple docstring'''







  def __init__(      self      :					Union[str, Any]      ,				UpperCamelCase      :					Tuple      ,				UpperCamelCase      :					Any=7      ,				UpperCamelCase      :					Optional[Any]=3      ,				UpperCamelCase      :					Dict=30      ,				UpperCamelCase      :					int=4_00      ,				UpperCamelCase      :					List[str]=None      ,				UpperCamelCase      :					Optional[Any]=True      ,				UpperCamelCase      :					str=True      ,				UpperCamelCase      :					Any=[0.5, 0.5, 0.5]      ,				UpperCamelCase      :					int=[0.5, 0.5, 0.5]      ,				UpperCamelCase      :					Dict=10      ,				UpperCamelCase      :					Dict=False      ,				UpperCamelCase      :					Dict=2_55      ,				UpperCamelCase      :					Dict="shi-labs/oneformer_demo"      ,				UpperCamelCase      :					Optional[int]="ade20k_panoptic.json"      ,				UpperCamelCase      :					Tuple=10      ,				):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	parent
    _snake_case   :				Union[str, Any]														=	batch_size
    _snake_case   :				Tuple														=	num_channels
    _snake_case   :				List[str]														=	min_resolution
    _snake_case   :				List[str]														=	max_resolution
    _snake_case   :				Optional[Any]														=	do_resize
    _snake_case   :				Optional[Any]														=	{'shortest_edge': 32, 'longest_edge': 13_33} if size is None else size
    _snake_case   :				Optional[int]														=	do_normalize
    _snake_case   :				Any														=	image_mean
    _snake_case   :				List[Any]														=	image_std
    _snake_case   :				Any														=	class_info_file
    _snake_case   :				List[str]														=	prepare_metadata(UpperCamelCase      ,				UpperCamelCase							)
    _snake_case   :				Any														=	num_text
    _snake_case   :				str														=	repo_path

    # for the post_process_functions
    _snake_case   :				Optional[Any]														=	2
    _snake_case   :				str														=	10
    _snake_case   :				Union[str, Any]														=	10
    _snake_case   :				List[Any]														=	3
    _snake_case   :				str														=	4
    _snake_case   :				List[Any]														=	num_labels
    _snake_case   :				str														=	do_reduce_labels
    _snake_case   :				List[str]														=	ignore_index







  def UpperCamelCase_ (      self      :					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 UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					Optional[int]      ,				UpperCamelCase      :					List[Any]=False							):


    '''simple docstring'''





    if not batched:
      _snake_case   :				Any														=	image_inputs[0]
      if isinstance(UpperCamelCase      ,				Image.Image							):
        _snake_case							,					_snake_case   :				Any														=	image.size
      else:
        _snake_case							,					_snake_case   :				Any														=	image.shape[1], image.shape[2]
      if w < h:
        _snake_case   :				Union[str, Any]														=	int(self.size['shortest_edge'] * h / w							)
        _snake_case   :				Any														=	self.size['shortest_edge']
      elif w > h:
        _snake_case   :				int														=	self.size['shortest_edge']
        _snake_case   :				Union[str, Any]														=	int(self.size['shortest_edge'] * w / h							)
      else:
        _snake_case   :				Dict														=	self.size['shortest_edge']
        _snake_case   :				Dict														=	self.size['shortest_edge']

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

    return expected_height, expected_width







  def UpperCamelCase_ (      self      :					Tuple							):


    '''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        _lowerCAmelCase    (			UpperCAmelCase_							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       Tuple      					=OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  a_							:       Any      					=image_processing_class







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				Dict														=	OneFormerImageProcessorTester(self							)







  @property
  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    return self.image_processing_tester.prepare_image_processor_dict()







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				int														=	self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'image_mean'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'image_std'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_normalize'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_resize'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'size'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'ignore_index'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'class_info_file'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'num_text'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'repo_path'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'metadata'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_reduce_labels'							)							)







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    pass







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	self.image_processing_class(**self.image_processor_dict							)
    # create random PIL images
    _snake_case   :				int														=	prepare_image_inputs(self.image_processing_tester      ,				equal_resolution=UpperCamelCase							)
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase      ,				Image.Image							)

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

    _snake_case							,					_snake_case   :				List[Any]														=	self.image_processing_tester.get_expected_values(UpperCamelCase							)

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

    # Test batched
    _snake_case							,					_snake_case   :				Optional[int]														=	self.image_processing_tester.get_expected_values(UpperCamelCase      ,				batched=UpperCamelCase							)

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







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	self.image_processing_class(**self.image_processor_dict							)
    # create random numpy tensors
    _snake_case   :				int														=	prepare_image_inputs(self.image_processing_tester      ,				equal_resolution=UpperCamelCase      ,				numpify=UpperCamelCase							)
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase      ,				np.ndarray							)

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

    _snake_case							,					_snake_case   :				Optional[int]														=	self.image_processing_tester.get_expected_values(UpperCamelCase							)

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

    # Test batched
    _snake_case							,					_snake_case   :				Union[str, Any]														=	self.image_processing_tester.get_expected_values(UpperCamelCase      ,				batched=UpperCamelCase							)

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







  def UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	self.image_processing_class(**self.image_processor_dict							)
    # create random PyTorch tensors
    _snake_case   :				Union[str, Any]														=	prepare_image_inputs(self.image_processing_tester      ,				equal_resolution=UpperCamelCase      ,				torchify=UpperCamelCase							)
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase      ,				torch.Tensor							)

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

    _snake_case							,					_snake_case   :				int														=	self.image_processing_tester.get_expected_values(UpperCamelCase							)

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

    # Test batched
    _snake_case							,					_snake_case   :				int														=	self.image_processing_tester.get_expected_values(UpperCamelCase      ,				batched=UpperCamelCase							)

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







  def UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					Tuple=False      ,				UpperCamelCase      :					str=False      ,				UpperCamelCase      :					Dict="np"							):


    '''simple docstring'''





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

    _snake_case   :				List[Any]														=	image_processor(
        UpperCamelCase      ,				['semantic'] * len(UpperCamelCase							)      ,				UpperCamelCase      ,				return_tensors='pt'      ,				instance_id_to_semantic_id=UpperCamelCase      ,				pad_and_return_pixel_mask=UpperCamelCase      ,				)

    return inputs







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    pass







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    def common(UpperCamelCase      :					Any=False      ,				UpperCamelCase      :					int=None							):
      _snake_case   :				Any														=	self.comm_get_image_processor_inputs(
          with_segmentation_maps=UpperCamelCase      ,				is_instance_map=UpperCamelCase      ,				segmentation_type=UpperCamelCase							)

      _snake_case   :				Union[str, Any]														=	inputs['mask_labels']
      _snake_case   :				Optional[int]														=	inputs['class_labels']
      _snake_case   :				Optional[int]														=	inputs['pixel_values']
      _snake_case   :				Optional[Any]														=	inputs['text_inputs']

      # check the batch_size
      for mask_label, class_label, text_input in zip(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							):
        self.assertEqual(mask_label.shape[0]      ,				class_label.shape[0]							)
        # this ensure padding has happened
        self.assertEqual(mask_label.shape[1:]      ,				pixel_values.shape[2:]							)
        self.assertEqual(len(UpperCamelCase							)      ,				self.image_processing_tester.num_text							)

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







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	np.zeros((20, 50)							)
    _snake_case   :				int														=	1
    _snake_case   :				int														=	1
    _snake_case   :				Optional[Any]														=	1

    _snake_case   :				List[Any]														=	binary_mask_to_rle(UpperCamelCase							)
    self.assertEqual(len(UpperCamelCase							)      ,				4							)
    self.assertEqual(rle[0]      ,				21							)
    self.assertEqual(rle[1]      ,				45							)







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes      ,				max_seq_length=77      ,				task_seq_length=77      ,				class_info_file='ade20k_panoptic.json'      ,				num_text=self.image_processing_tester.num_text      ,				repo_path='shi-labs/oneformer_demo'      ,				)
    _snake_case   :				Optional[int]														=	self.image_processing_tester.get_fake_oneformer_outputs()

    _snake_case   :				Any														=	fature_extractor.post_process_semantic_segmentation(UpperCamelCase							)

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

    _snake_case   :				Optional[Any]														=	[(1, 4) for i in range(self.image_processing_tester.batch_size							)]
    _snake_case   :				Union[str, Any]														=	fature_extractor.post_process_semantic_segmentation(UpperCamelCase      ,				target_sizes=UpperCamelCase							)

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







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				Any														=	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   :				Optional[int]														=	self.image_processing_tester.get_fake_oneformer_outputs()
    _snake_case   :				int														=	image_processor.post_process_instance_segmentation(UpperCamelCase      ,				threshold=0							)

    self.assertTrue(len(UpperCamelCase							) == self.image_processing_tester.batch_size							)
    for el in segmentation:
      self.assertTrue('segmentation' in el							)
      self.assertTrue('segments_info' in el							)
      self.assertEqual(type(el['segments_info']							)      ,				UpperCamelCase							)
      self.assertEqual(
          el['segmentation'].shape      ,				(self.image_processing_tester.height, self.image_processing_tester.width)							)







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    _snake_case   :				str														=	self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes      ,				max_seq_length=77      ,				task_seq_length=77      ,				class_info_file='ade20k_panoptic.json'      ,				num_text=self.image_processing_tester.num_text      ,				repo_path='shi-labs/oneformer_demo'      ,				)
    _snake_case   :				Optional[Any]														=	self.image_processing_tester.get_fake_oneformer_outputs()
    _snake_case   :				Any														=	image_processor.post_process_panoptic_segmentation(UpperCamelCase      ,				threshold=0							)

    self.assertTrue(len(UpperCamelCase							) == self.image_processing_tester.batch_size							)
    for el in segmentation:
      self.assertTrue('segmentation' in el							)
      self.assertTrue('segments_info' in el							)
      self.assertEqual(type(el['segments_info']							)      ,				UpperCamelCase							)
      self.assertEqual(
          el['segmentation'].shape      ,				(self.image_processing_tester.height, self.image_processing_tester.width)							)




