





'''simple docstring'''

import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


a   	=    "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

a   	=    "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

a   	=    "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION      )
class __a			( datasets.Metric      ):





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

      '''simple docstring'''

      if version.parse(scb.__version__     ) < version.parse("""1.4.12"""     ):
         raise ImportWarning(
             """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
             """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""     )
      return datasets.MetricInfo(
          description=_DESCRIPTION					,citation=_CITATION					,homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf"""					,inputs_description=_KWARGS_DESCRIPTION					,features=datasets.Features(
              {
                  """predictions""": datasets.Value("""string"""					,id="""sequence"""     ),
                  """references""": datasets.Sequence(datasets.Value("""string"""					,id="""sequence"""     )					,id="""references"""     ),
              }     )					,codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""]					,reference_urls=[
              """https://github.com/m-popovic/chrF""",
          ]					,)





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Any					,lowerCamelCase					:      int = CHRF.CHAR_ORDER					,lowerCamelCase					:      int = CHRF.WORD_ORDER					,lowerCamelCase					:      int = CHRF.BETA					,lowerCamelCase					:      bool = False					,lowerCamelCase					:      bool = False					,lowerCamelCase					:      bool = False					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							len(references[0]     )
      if any(len(lowerCamelCase     ) != references_per_prediction for refs in references     ):
         raise ValueError("""Sacrebleu requires the same number of references for each prediction"""     )
      __SCREAMING_SNAKE_CASE   				=							[[refs[i] for refs in references] for i in range(lowerCamelCase     )]

      __SCREAMING_SNAKE_CASE   				=							CHRF(lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sb_chrf.corpus_score(lowerCamelCase					,lowerCamelCase     )

      return {
          "score": output.score,
          "char_order": output.char_order,
          "word_order": output.word_order,
          "beta": output.beta,
      }










"""simple docstring"""




import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

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

if is_vision_available():
 from PIL import Image

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		List[Any]="shi-labs/oneformer_demo"							)    ->  Tuple:





     '''simple docstring'''

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






class       UpperCAmelCase_    (							unittest.TestCase		):



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

           # for the post_process_functions
           _UpperCAmelCase     :      str														=       2
           _UpperCAmelCase     :      Any														=       1_0
           _UpperCAmelCase     :      Optional[int]														=       1_0
           _UpperCAmelCase     :      Tuple														=       3
           _UpperCAmelCase     :      List[str]														=       4
           _UpperCAmelCase     :      int														=       num_labels
           _UpperCAmelCase     :      Optional[Any]														=       do_reduce_labels
           _UpperCAmelCase     :      Any														=       ignore_index



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

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

           return expected_height, expected_width



      def 		snake_case_   (	self	:      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       UpperCAmelCase_    (							_UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  str						=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      __SCREAMING_SNAKE_CASE		:  Tuple						=					image_processing_class



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      Optional[Any]														=       OneFormerImageProcessorTester(self		)



      @property
      def 		snake_case_   (	self	:      Optional[Any]		):
           return self.image_processing_tester.prepare_image_processor_dict()



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      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 		snake_case_   (	self	:      List[Any]		):
           pass



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

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

           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       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
           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       self.image_processing_tester.get_expected_values(A	,      batched=A		)

           _UpperCAmelCase     :      Optional[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 		snake_case_   (	self	:      str		):
           # Initialize image_processor
           _UpperCAmelCase     :      Optional[Any]														=       self.image_processing_class(**self.image_processor_dict		)
           # create random numpy tensors
           _UpperCAmelCase     :      Any														=       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
           _UpperCAmelCase     :      int														=       image_processor(image_inputs[0]	,      ["semantic"]	,      return_tensors="pt"		).pixel_values

           _UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       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
           _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       self.image_processing_tester.get_expected_values(A	,      batched=A		)

           _UpperCAmelCase     :      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 		snake_case_   (	self	:      Optional[int]		):
           # Initialize image_processor
           _UpperCAmelCase     :      Dict														=       self.image_processing_class(**self.image_processor_dict		)
           # create random PyTorch tensors
           _UpperCAmelCase     :      str														=       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
           _UpperCAmelCase     :      Tuple														=       image_processor(image_inputs[0]	,      ["semantic"]	,      return_tensors="pt"		).pixel_values

           _UpperCAmelCase							,			_UpperCAmelCase     :      Any														=       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
           _UpperCAmelCase							,			_UpperCAmelCase     :      List[str]														=       self.image_processing_tester.get_expected_values(A	,      batched=A		)

           _UpperCAmelCase     :      Optional[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 		snake_case_   (	self	:      Optional[int]	,      A	:      Tuple=False	,      A	:      Optional[Any]=False	,      A	:      int="np"		):
           _UpperCAmelCase     :      List[Any]														=       self.image_processing_class(**self.image_processor_dict		)
           # prepare image and target
           _UpperCAmelCase     :      List[str]														=       self.image_processing_tester.num_labels
           _UpperCAmelCase     :      Any														=       None
           _UpperCAmelCase     :      List[Any]														=       None
           _UpperCAmelCase     :      Dict														=       prepare_image_inputs(self.image_processing_tester	,      equal_resolution=A		)
           if with_segmentation_maps:
                _UpperCAmelCase     :      Union[str, Any]														=       num_labels
                if is_instance_map:
                     _UpperCAmelCase     :      Optional[int]														=       list(range(A		)		) * 2
                     _UpperCAmelCase     :      Union[str, Any]														=       dict(enumerate(A		)		)
                _UpperCAmelCase     :      Tuple														=       [
                    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     :      Optional[int]														=       [Image.fromarray(A		) for annotation in annotations]

           _UpperCAmelCase     :      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 		snake_case_   (	self	:      Any		):
           pass



      def 		snake_case_   (	self	:      Dict		):
           def common(A	:      List[Any]=False	,      A	:      List[str]=None		):
                _UpperCAmelCase     :      str														=       self.comm_get_image_processor_inputs(
                    with_segmentation_maps=A	,      is_instance_map=A	,      segmentation_type=A		)

                _UpperCAmelCase     :      Optional[int]														=       inputs["mask_labels"]
                _UpperCAmelCase     :      str														=       inputs["class_labels"]
                _UpperCAmelCase     :      List[str]														=       inputs["pixel_values"]
                _UpperCAmelCase     :      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 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Optional[Any]														=       np.zeros((2_0, 5_0)		)
           _UpperCAmelCase     :      Dict														=       1
           _UpperCAmelCase     :      Optional[int]														=       1
           _UpperCAmelCase     :      Optional[int]														=       1

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      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"	,      )
           _UpperCAmelCase     :      int														=       self.image_processing_tester.get_fake_oneformer_outputs()

           _UpperCAmelCase     :      List[str]														=       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,
               )	,      )

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

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



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Tuple														=       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"	,      )
           _UpperCAmelCase     :      Union[str, Any]														=       self.image_processing_tester.get_fake_oneformer_outputs()
           _UpperCAmelCase     :      Tuple														=       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 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Optional[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"	,      )
           _UpperCAmelCase     :      Optional[Any]														=       self.image_processing_tester.get_fake_oneformer_outputs()
           _UpperCAmelCase     :      Tuple														=       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)		)
