code: 
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
         import torch
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       ["image_processor", "tokenizer"]
      a__     							=       "OneFormerImageProcessor"
      a__     							=       ("CLIPTokenizer", "CLIPTokenizerFast")
  
  
      def __init__(self     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__ = 77     ,      lowercase__ = 77     ,      **lowercase__  )     ->      Optional[Any]:
          if image_processor is None:
              raise ValueError('''You need to specify an `image_processor`.'''  )
          if tokenizer is None:
              raise ValueError('''You need to specify a `tokenizer`.'''  )
  
          __UpperCAmelCase									=			max_seq_length
          __UpperCAmelCase									=			task_seq_length
  
          super().__init__(lowercase__     ,      lowercase__  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__=None     ,      lowercase__=77  )     ->      str:
          if text_list is None:
              raise ValueError('''tokens cannot be None.'''  )
  
          __UpperCAmelCase									=			self.tokenizer(lowercase__     ,      padding='''max_length'''     ,      max_length=lowercase__     ,      truncation=lowercase__  )
  
          __UpperCAmelCase					,     __UpperCAmelCase									=			tokens['''attention_mask'''], tokens['''input_ids''']
  
          __UpperCAmelCase									=			[]
          for attn_mask, input_id in zip(lowercase__     ,      lowercase__  ):
              __UpperCAmelCase									=			torch.tensor(lowercase__  ) * torch.tensor(lowercase__  )
              token_inputs.append(token.unsqueeze(0  )  )
  
          __UpperCAmelCase									=			torch.cat(lowercase__     ,      dim=0  )
          return token_inputs
  
  
      def __call__(self     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      **lowercase__  )     ->      List[str]:
  
          if task_inputs is None:
              raise ValueError('''You have to specify the task_input. Found None.'''  )
          elif images is None:
              raise ValueError('''You have to specify the image. Found None.'''  )
  
          if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs  ):
              raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''  )
  
          __UpperCAmelCase									=			self.image_processor(lowercase__     ,      lowercase__     ,      lowercase__     ,      **lowercase__  )
  
          if isinstance(lowercase__     ,      lowercase__  ):
              __UpperCAmelCase									=			[task_inputs]
  
          if isinstance(lowercase__     ,      lowercase__  ) and all(isinstance(lowercase__     ,      lowercase__  ) for task_input in task_inputs  ):
              __UpperCAmelCase									=			[]
              for task in task_inputs:
                  __UpperCAmelCase									=			F'''the task is {task}'''
                  task_token_inputs.append(lowercase__  )
              __UpperCAmelCase									=			self._preprocess_text(lowercase__     ,      max_length=self.task_seq_length  )
          else:
              raise TypeError('''Task Inputs should be a string or a list of strings.'''  )
  
          if hasattr(lowercase__     ,      '''text_inputs'''  ):
              __UpperCAmelCase									=			encoded_inputs.text_inputs
  
              __UpperCAmelCase									=			[]
              for texts in texts_list:
                  __UpperCAmelCase									=			self._preprocess_text(lowercase__     ,      max_length=self.max_seq_length  )
                  text_inputs.append(text_input_list.unsqueeze(0  )  )
  
              __UpperCAmelCase									=			torch.cat(lowercase__     ,      dim=0  )
  
          return encoded_inputs
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      **lowercase__  )     ->      Dict:
  
          if task_inputs is None:
              raise ValueError('''You have to specify the task_input. Found None.'''  )
          elif images is None:
              raise ValueError('''You have to specify the image. Found None.'''  )
  
          if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs  ):
              raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''  )
  
          __UpperCAmelCase									=			self.image_processor.encode_inputs(lowercase__     ,      lowercase__     ,      lowercase__     ,      **lowercase__  )
  
          if isinstance(lowercase__     ,      lowercase__  ):
              __UpperCAmelCase									=			[task_inputs]
  
          if isinstance(lowercase__     ,      lowercase__  ) and all(isinstance(lowercase__     ,      lowercase__  ) for task_input in task_inputs  ):
              __UpperCAmelCase									=			[]
              for task in task_inputs:
                  __UpperCAmelCase									=			F'''the task is {task}'''
                  task_token_inputs.append(lowercase__  )
              __UpperCAmelCase									=			self._preprocess_text(lowercase__     ,      max_length=self.task_seq_length  )
          else:
              raise TypeError('''Task Inputs should be a string or a list of strings.'''  )
  
          if hasattr(lowercase__     ,      '''text_inputs'''  ):
              __UpperCAmelCase									=			encoded_inputs.text_inputs
  
              __UpperCAmelCase									=			[]
              for texts in texts_list:
                  __UpperCAmelCase									=			self._preprocess_text(lowercase__     ,      max_length=self.max_seq_length  )
                  text_inputs.append(text_input_list.unsqueeze(0  )  )
  
              __UpperCAmelCase									=			torch.cat(lowercase__     ,      dim=0  )
  
          return encoded_inputs
  
  
      def      lowerCAmelCase_						(self     ,      *lowercase__     ,      **lowercase__  )     ->      Tuple:
          return self.image_processor.post_process_semantic_segmentation(*lowercase__     ,      **lowercase__  )
  
  
      def      lowerCAmelCase_						(self     ,      *lowercase__     ,      **lowercase__  )     ->      Tuple:
          return self.image_processor.post_process_instance_segmentation(*lowercase__     ,      **lowercase__  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      *lowercase__     ,      **lowercase__  )     ->      Optional[int]:
          return self.image_processor.post_process_panoptic_segmentation(*lowercase__     ,      **lowercase__  )
  
  
  
  
  
style_context: 
  
  import csv
  import json
  import os
  
  import datasets
  
  
  # TODO: Add BibTeX citation
  # Find for instance the citation on arxiv or on the dataset repo/website
  A_		:			Any										=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'
  
  # TODO: Add description of the dataset here
  # You can copy an official description
  A_		:			Optional[Any]										=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'
  
  # TODO: Add a link to an official homepage for the dataset here
  A_		:			Dict										=   ''
  
  # TODO: Add the licence for the dataset here if you can find it
  A_		:			Dict										=   ''
  
  # TODO: Add link to the official dataset URLs here
  # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
  # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
  A_		:			Union[str, Any]										=   {
      'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
      'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
  }
  class 					A_   (      datasets.GeneratorBasedBuilder						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       datasets.Version("1.1.0"						)
  
      # This is an example of a dataset with multiple configurations.
      # If you don't want/need to define several sub-sets in your dataset,
      # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
  
      # If you need to make complex sub-parts in the datasets with configurable options
      # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
      # BUILDER_CONFIG_CLASS = MyBuilderConfig
  
      # You will be able to load one or the other configurations in the following list with
      # data = datasets.load_dataset('my_dataset', 'first_domain')
      # data = datasets.load_dataset('my_dataset', 'second_domain')
      a__     							=       [
          datasets.BuilderConfig(name="first_domain" ,     version=_a ,     description="This part of my dataset covers a first domain"						),
          datasets.BuilderConfig(name="second_domain" ,     version=_a ,     description="This part of my dataset covers a second domain"						),
      ]
  
      a__     							=       "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
          if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option1''': datasets.Value('''string'''  ),
                      '''answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option2''': datasets.Value('''string'''  ),
                      '''second_domain_answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          return datasets.DatasetInfo(
              # This is the description that will appear on the datasets page.
              description=_DESCRIPTION     ,      features=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
          # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
  
          # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
          # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
          # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
          __UpperCAmelCase									=			_URLS[self.config.name]
          __UpperCAmelCase									=			dl_manager.download_and_extract(lowercase__  )
          return [
              datasets.SplitGenerator(
                  name=datasets.Split.TRAIN     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''train.jsonl'''  ),
                      '''split''': '''train''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.VALIDATION     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''dev.jsonl'''  ),
                      '''split''': '''dev''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.TEST     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''test.jsonl'''  ),
                      '''split''': '''test'''
                  }     ,      ),
          ]
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
          # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
          # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
          with open(lowercase__     ,      encoding='''utf-8'''  ) as f:
              for key, row in enumerate(lowercase__  ):
                  __UpperCAmelCase									=			json.loads(lowercase__  )
                  if self.config.name == "first_domain":
                      # Yields examples as (key, example) tuples
                      yield key, {
                          "sentence": data["sentence"],
                          "option1": data["option1"],
                          "answer": "" if split == "test" else data["answer"],
                      }
                  else:
                      yield key, {
                          "sentence": data["sentence"],
                          "option2": data["option2"],
                          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                      }
  
  
  
  
  