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
  
  
  _lowercase		:     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""": [515]},
  }
  
  
  @pytest.mark.parametrize(
      'task_cls'       ,[
          AudioClassification,
          AutomaticSpeechRecognition,
          ImageClassification,
          LanguageModeling,
          QuestionAnsweringExtractive,
          Summarization,
          TextClassification,
      ]       ,)
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		lowerCamelCase_  :    Tuple   	=			task_cls()
  		lowerCamelCase_  :    Dict   	=			asdict(lowerCAmelCase__	)
  		lowerCamelCase_  :    Any   	=			task_template_from_dict(lowerCAmelCase__	)
  		assert task == reloaded
  
  
  
  
  
  
  class 	UpperCamelCase_     :
  
  
  
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    Optional[int]   	=			LanguageModeling(text_column='input_text'	)
  							assert {"input_text": "text"} == task.column_mapping
  
  
  
  
  
  					def      __a    (		self    :    int	):
  							lowerCamelCase_  :    Union[str, Any]   	=			Features({'text': Value('string'	)}	)
  							lowerCamelCase_  :    List[Any]   	=			{'text_column': 'input_text'}
  							lowerCamelCase_  :    List[Any]   	=			LanguageModeling.from_dict(lowerCamelCase	)
  							assert "language-modeling" == task.task
  							assert input_schema == task.input_schema
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							lowerCamelCase_  :    List[str]   	=			sorted(['pos', 'neg']	)
  
  
  
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    int   	=			TextClassification(text_column='input_text'					,			label_column='input_label'	)
  							self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}					,			task.column_mapping	)
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							lowerCamelCase_  :    List[Any]   	=			Features({'text': Value('string'	)}	)
  							# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
  							lowerCamelCase_  :    int   	=			Features({'labels': ClassLabel}	)
  							lowerCamelCase_  :    int   	=			{'text_column': 'input_text', 'label_column': 'input_labels'}
  							lowerCamelCase_  :    List[str]   	=			TextClassification.from_dict(lowerCamelCase	)
  							self.assertEqual('text-classification'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  					def      __a    (		self    :    Tuple	):
  							lowerCamelCase_  :    Any   	=			TextClassification(text_column='input_text'					,			label_column='input_label'	)
  							self.assertEqual(task.label_schema['labels']					,			lowerCamelCase	)
  							lowerCamelCase_  :    Optional[int]   	=			task.align_with_features(Features({'input_label': ClassLabel(names=self.labels	)}	)	)
  							self.assertEqual(task.label_schema['labels']					,			ClassLabel(names=self.labels	)	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    Optional[Any]	):
  							lowerCamelCase_  :    Optional[int]   	=			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    :    Tuple	):
  							lowerCamelCase_  :    Union[str, Any]   	=			Features({'question': Value('string'	), 'context': Value('string'	)}	)
  							lowerCamelCase_  :    Union[str, Any]   	=			Features(
  							    {
  							        'answers': Sequence(
  							            {
  							                'text': Value('string'	),
  							                'answer_start': Value('int32'	),
  							            }	)
  							    }	)
  							lowerCamelCase_  :    List[Any]   	=			{
  							    'context_column': 'input_input_context',
  							    'question_column': 'input_question',
  							    'answers_column': 'input_answers',
  							}
  							lowerCamelCase_  :    Union[str, Any]   	=			QuestionAnsweringExtractive.from_dict(lowerCamelCase	)
  							self.assertEqual('question-answering-extractive'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    Any   	=			Summarization(text_column='input_text'					,			summary_column='input_summary'	)
  							self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'}					,			task.column_mapping	)
  
  
  
  
  
  					def      __a    (		self    :    Tuple	):
  							lowerCamelCase_  :    Optional[int]   	=			Features({'text': Value('string'	)}	)
  							lowerCamelCase_  :    Union[str, Any]   	=			Features({'summary': Value('string'	)}	)
  							lowerCamelCase_  :    str   	=			{'text_column': 'input_text', 'summary_column': 'input_summary'}
  							lowerCamelCase_  :    Optional[Any]   	=			Summarization.from_dict(lowerCamelCase	)
  							self.assertEqual('summarization'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    Any   	=			AutomaticSpeechRecognition(audio_column='input_audio'					,			transcription_column='input_transcription'	)
  							self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'}					,			task.column_mapping	)
  
  
  
  
  
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    List[Any]   	=			Features({'audio': Audio()}	)
  							lowerCamelCase_  :    Tuple   	=			Features({'transcription': Value('string'	)}	)
  							lowerCamelCase_  :    Optional[Any]   	=			{
  							    'audio_column': 'input_audio',
  							    'transcription_column': 'input_transcription',
  							}
  							lowerCamelCase_  :    Any   	=			AutomaticSpeechRecognition.from_dict(lowerCamelCase	)
  							self.assertEqual('automatic-speech-recognition'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							lowerCamelCase_  :    Optional[Any]   	=			sorted(['pos', 'neg']	)
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							lowerCamelCase_  :    List[str]   	=			AudioClassification(audio_column='input_audio'					,			label_column='input_label'	)
  							self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'}					,			task.column_mapping	)
  
  
  
  					def      __a    (		self    :    Tuple	):
  							lowerCamelCase_  :    Union[str, Any]   	=			Features({'audio': Audio()}	)
  							lowerCamelCase_  :    Any   	=			Features({'labels': ClassLabel}	)
  							lowerCamelCase_  :    Dict   	=			{
  							    'audio_column': 'input_image',
  							    'label_column': 'input_label',
  							}
  							lowerCamelCase_  :    Dict   	=			AudioClassification.from_dict(lowerCamelCase	)
  							self.assertEqual('audio-classification'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  					def      __a    (		self    :    Any	):
  							lowerCamelCase_  :    str   	=			AudioClassification(audio_column='input_audio'					,			label_column='input_label'	)
  							self.assertEqual(task.label_schema['labels']					,			lowerCamelCase	)
  							lowerCamelCase_  :    Dict   	=			task.align_with_features(Features({'input_label': ClassLabel(names=self.labels	)}	)	)
  							self.assertEqual(task.label_schema['labels']					,			ClassLabel(names=self.labels	)	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    int	):
  							lowerCamelCase_  :    Optional[Any]   	=			sorted(['pos', 'neg']	)
  
  
  
  					def      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    Union[str, Any]   	=			ImageClassification(image_column='input_image'					,			label_column='input_label'	)
  							self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'}					,			task.column_mapping	)
  
  
  
  					def      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    int   	=			Features({'image': Image()}	)
  							lowerCamelCase_  :    List[Any]   	=			Features({'labels': ClassLabel}	)
  							lowerCamelCase_  :    List[str]   	=			{
  							    'image_column': 'input_image',
  							    'label_column': 'input_label',
  							}
  							lowerCamelCase_  :    Dict   	=			ImageClassification.from_dict(lowerCamelCase	)
  							self.assertEqual('image-classification'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  					def      __a    (		self    :    Optional[Any]	):
  							lowerCamelCase_  :    Any   	=			ImageClassification(image_column='input_image'					,			label_column='input_label'	)
  							self.assertEqual(task.label_schema['labels']					,			lowerCamelCase	)
  							lowerCamelCase_  :    Dict   	=			task.align_with_features(Features({'input_label': ClassLabel(names=self.labels	)}	)	)
  							self.assertEqual(task.label_schema['labels']					,			ClassLabel(names=self.labels	)	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    int	):
  							lowerCamelCase_  :    Optional[Any]   	=			DatasetInfo(task_templates=QuestionAnsweringExtractive()	)
  							lowerCamelCase_  :    Any   	=			Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}					,			info=lowerCamelCase	)
  							assert isinstance(dataset.info.task_templates					,			lowerCamelCase	)
  							assert len(dataset.info.task_templates	) == 1
  
  							def keep_task(lowerCamelCase    :    List[Any]	):
  									return x
  
  							def dont_keep_task(lowerCamelCase    :    int	):
  									lowerCamelCase_  :    Dict   	=			deepcopy(lowerCamelCase	)
  									lowerCamelCase_  :    Dict   	=			0
  									return out
  
  							lowerCamelCase_  :    Tuple   	=			dataset.map(lowerCamelCase	)
  							assert mapped_dataset.info.task_templates == dataset.info.task_templates
  							# reload from cache
  							lowerCamelCase_  :    int   	=			dataset.map(lowerCamelCase	)
  							assert mapped_dataset.info.task_templates == dataset.info.task_templates
  
  							lowerCamelCase_  :    List[str]   	=			dataset.map(lowerCamelCase	)
  							assert mapped_dataset.info.task_templates == []
  							# reload from cache
  							lowerCamelCase_  :    List[Any]   	=			dataset.map(lowerCamelCase	)
  							assert mapped_dataset.info.task_templates == []
  
  
  
  
  
  					def      __a    (		self    :    int	):
  							lowerCamelCase_  :    List[Any]   	=			Features({'text': Value('string'	), 'label': ClassLabel(names=('pos', 'neg')	)}	)
  							lowerCamelCase_  :    Union[str, Any]   	=			TextClassification(text_column='text'					,			label_column='label'	)
  							lowerCamelCase_  :    str   	=			DatasetInfo(features=lowerCamelCase					,			task_templates=lowerCamelCase	)
  							lowerCamelCase_  :    str   	=			Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']}					,			info=lowerCamelCase	)
  
  							def process(lowerCamelCase    :    List[Any]	):
  									return example
  
  							lowerCamelCase_  :    List[str]   	=			dataset.remove_columns('label'	)
  							lowerCamelCase_  :    List[str]   	=			modified_dataset.map(lowerCamelCase	)
  							assert mapped_dataset.info.task_templates == []
  
style_context: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _lowercase		:     int 					={
      """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Any 					=["""MarianTokenizer"""]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Any 					=[
  		    """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
  		    """MarianForCausalLM""",
  		    """MarianModel""",
  		    """MarianMTModel""",
  		    """MarianPreTrainedModel""",
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Dict 					=["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Optional[Any] 					=["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]
  
  if TYPE_CHECKING:
  		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_marian import MarianTokenizer
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_marian import (
  				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    MarianForCausalLM,
  				    MarianModel,
  				    MarianMTModel,
  				    MarianPreTrainedModel,
  				)
  
  		try:
  				if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
  
  else:
  		import sys
  
  		_lowercase		:     List[str] 					=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  