code: 
  
  
  from copy import deepcopy
  from unittest.case import TestCase
  
  import pytest
  
  from datasets.arrow_dataset import Dataset
  from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
  from datasets.info import DatasetInfo
  from datasets.tasks import (
      AudioClassification,
      AutomaticSpeechRecognition,
      ImageClassification,
      LanguageModeling,
      QuestionAnsweringExtractive,
      Summarization,
      TextClassification,
      task_template_from_dict,
  )
  from datasets.utils.py_utils import asdict
  
  
  _SCREAMING_SNAKE_CASE    :				List[Any]       =					{
      'id': '5733be284776f41900661182',
      'title': 'University_of_Notre_Dame',
      'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
      'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
      'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [5_15]},
  }
  @pytest.mark.parametrize(
      '''task_cls'''							,[
          AudioClassification,
          AutomaticSpeechRecognition,
          ImageClassification,
          LanguageModeling,
          QuestionAnsweringExtractive,
          Summarization,
          TextClassification,
      ]							,)
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       task_cls()
       snake_case					=       asdict(_SCREAMING_SNAKE_CASE       )
       snake_case					=       task_template_from_dict(_SCREAMING_SNAKE_CASE       )
       assert task == reloaded
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           snake_case					=       LanguageModeling(text_column='''input_text'''					)
           assert {"input_text": "text"} == task.column_mapping
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       Features({'''text''': Value('''string'''					)}					)
           snake_case					=       {'text_column': 'input_text'}
           snake_case					=       LanguageModeling.from_dict(__a					)
           assert "language-modeling" == task.task
           assert input_schema == task.input_schema
  
  
  
  
  
  
  class 						A__      (					lowerCAmelCase_			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           snake_case					=       sorted(['''pos''', '''neg''']					)
      def 	a_    ( self					):
           snake_case					=       TextClassification(text_column='''input_text'''    ,      label_column='''input_label'''					)
           self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}    ,      task.column_mapping					)
      def 	a_    ( self					):
           snake_case					=       Features({'''text''': Value('''string'''					)}					)
           # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
           snake_case					=       Features({'''labels''': ClassLabel}					)
           snake_case					=       {'text_column': 'input_text', 'label_column': 'input_labels'}
           snake_case					=       TextClassification.from_dict(__a					)
           self.assertEqual('''text-classification'''    ,      task.task					)
           self.assertEqual(__a    ,      task.input_schema					)
           self.assertEqual(__a    ,      task.label_schema					)
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       TextClassification(text_column='''input_text'''    ,      label_column='''input_label'''					)
           self.assertEqual(task.label_schema['''labels''']    ,      __a					)
           snake_case					=       task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels					)}					)					)
           self.assertEqual(task.label_schema['''labels''']    ,      ClassLabel(names=self.labels					)					)
  
  
  
  
  
  
  class 						A__      (					lowerCAmelCase_			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           snake_case					=       QuestionAnsweringExtractive(
               context_column='''input_context'''    ,      question_column='''input_question'''    ,      answers_column='''input_answers'''					)
           self.assertDictEqual(
               {'''input_context''': '''context''', '''input_question''': '''question''', '''input_answers''': '''answers'''}    ,      task.column_mapping					)
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       Features({'''question''': Value('''string'''					), '''context''': Value('''string'''					)}					)
           snake_case					=       Features(
               {
                   '''answers''': Sequence(
                       {
                           '''text''': Value('''string'''					),
                           '''answer_start''': Value('''int32'''					),
                       }					)
               }					)
           snake_case					=       {
               'context_column': 'input_input_context',
               'question_column': 'input_question',
               'answers_column': 'input_answers',
           }
           snake_case					=       QuestionAnsweringExtractive.from_dict(__a					)
           self.assertEqual('''question-answering-extractive'''    ,      task.task					)
           self.assertEqual(__a    ,      task.input_schema					)
           self.assertEqual(__a    ,      task.label_schema					)
  
  
  
  
  
  
  class 						A__      (					lowerCAmelCase_			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           snake_case					=       Summarization(text_column='''input_text'''    ,      summary_column='''input_summary'''					)
           self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}    ,      task.column_mapping					)
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       Features({'''text''': Value('''string'''					)}					)
           snake_case					=       Features({'''summary''': Value('''string'''					)}					)
           snake_case					=       {'text_column': 'input_text', 'summary_column': 'input_summary'}
           snake_case					=       Summarization.from_dict(__a					)
           self.assertEqual('''summarization'''    ,      task.task					)
           self.assertEqual(__a    ,      task.input_schema					)
           self.assertEqual(__a    ,      task.label_schema					)
  
  
  
  
  
  
  class 						A__      (					lowerCAmelCase_			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           snake_case					=       AutomaticSpeechRecognition(audio_column='''input_audio'''    ,      transcription_column='''input_transcription'''					)
           self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}    ,      task.column_mapping					)
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       Features({'''audio''': Audio()}					)
           snake_case					=       Features({'''transcription''': Value('''string'''					)}					)
           snake_case					=       {
               'audio_column': 'input_audio',
               'transcription_column': 'input_transcription',
           }
           snake_case					=       AutomaticSpeechRecognition.from_dict(__a					)
           self.assertEqual('''automatic-speech-recognition'''    ,      task.task					)
           self.assertEqual(__a    ,      task.input_schema					)
           self.assertEqual(__a    ,      task.label_schema					)
  
  
  
  
  
  
  class 						A__      (					lowerCAmelCase_			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           snake_case					=       sorted(['''pos''', '''neg''']					)
      def 	a_    ( self					):
           snake_case					=       AudioClassification(audio_column='''input_audio'''    ,      label_column='''input_label'''					)
           self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}    ,      task.column_mapping					)
      def 	a_    ( self					):
           snake_case					=       Features({'''audio''': Audio()}					)
           snake_case					=       Features({'''labels''': ClassLabel}					)
           snake_case					=       {
               'audio_column': 'input_image',
               'label_column': 'input_label',
           }
           snake_case					=       AudioClassification.from_dict(__a					)
           self.assertEqual('''audio-classification'''    ,      task.task					)
           self.assertEqual(__a    ,      task.input_schema					)
           self.assertEqual(__a    ,      task.label_schema					)
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       AudioClassification(audio_column='''input_audio'''    ,      label_column='''input_label'''					)
           self.assertEqual(task.label_schema['''labels''']    ,      __a					)
           snake_case					=       task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels					)}					)					)
           self.assertEqual(task.label_schema['''labels''']    ,      ClassLabel(names=self.labels					)					)
  
  
  
  
  
  
  class 						A__      (					lowerCAmelCase_			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           snake_case					=       sorted(['''pos''', '''neg''']					)
      def 	a_    ( self					):
           snake_case					=       ImageClassification(image_column='''input_image'''    ,      label_column='''input_label'''					)
           self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}    ,      task.column_mapping					)
      def 	a_    ( self					):
           snake_case					=       Features({'''image''': Image()}					)
           snake_case					=       Features({'''labels''': ClassLabel}					)
           snake_case					=       {
               'image_column': 'input_image',
               'label_column': 'input_label',
           }
           snake_case					=       ImageClassification.from_dict(__a					)
           self.assertEqual('''image-classification'''    ,      task.task					)
           self.assertEqual(__a    ,      task.input_schema					)
           self.assertEqual(__a    ,      task.label_schema					)
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       ImageClassification(image_column='''input_image'''    ,      label_column='''input_label'''					)
           self.assertEqual(task.label_schema['''labels''']    ,      __a					)
           snake_case					=       task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels					)}					)					)
           self.assertEqual(task.label_schema['''labels''']    ,      ClassLabel(names=self.labels					)					)
  
  
  
  
  
  
  class 						A__      (					lowerCAmelCase_			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           snake_case					=       DatasetInfo(task_templates=QuestionAnsweringExtractive()					)
           snake_case					=       Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}    ,      info=__a					)
           assert isinstance(dataset.info.task_templates    ,      __a					)
           assert len(dataset.info.task_templates					) == 1
  
           def keep_task(__snake_case					):
                return x
  
           def dont_keep_task(__snake_case					):
                snake_case					=       deepcopy(__a					)
                snake_case					=       0
                return out
  
           snake_case					=       dataset.map(__a					)
           assert mapped_dataset.info.task_templates == dataset.info.task_templates
           # reload from cache
           snake_case					=       dataset.map(__a					)
           assert mapped_dataset.info.task_templates == dataset.info.task_templates
  
           snake_case					=       dataset.map(__a					)
           assert mapped_dataset.info.task_templates == []
           # reload from cache
           snake_case					=       dataset.map(__a					)
           assert mapped_dataset.info.task_templates == []
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       Features({'''text''': Value('''string'''					), '''label''': ClassLabel(names=('''pos''', '''neg''')					)}					)
           snake_case					=       TextClassification(text_column='''text'''    ,      label_column='''label'''					)
           snake_case					=       DatasetInfo(features=__a    ,      task_templates=__a					)
           snake_case					=       Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}    ,      info=__a					)
  
           def process(__snake_case					):
                return example
  
           snake_case					=       dataset.remove_columns('''label'''					)
           snake_case					=       modified_dataset.map(__a					)
           assert mapped_dataset.info.task_templates == []
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				List[str]       =					[
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )
  
  else:
   import sys
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  