






"""simple docstring"""


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_								=	{
    '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    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					task_cls()
  __A		     =					asdict(__UpperCamelCase					)
  __A		     =					task_template_from_dict(__UpperCamelCase					)
  assert task == reloaded


class    snake_case						:


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					LanguageModeling(text_column='''input_text'''    )
     assert {"input_text": "text"} == task.column_mapping






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					Features({'''text''': Value('''string'''    )}    )
     __A		     =					{'''text_column''': '''input_text'''}
     __A		     =					LanguageModeling.from_dict(_lowerCamelCase    )
     assert "language-modeling" == task.task
     assert input_schema == task.input_schema





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					sorted(['''pos''', '''neg''']    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					TextClassification(text_column='''input_text''',			label_column='''input_label'''    )
     self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''},			task.column_mapping    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					Features({'''text''': Value('''string'''    )}    )
     # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
     __A		     =					Features({'''labels''': ClassLabel}    )
     __A		     =					{'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
     __A		     =					TextClassification.from_dict(_lowerCamelCase    )
     self.assertEqual('''text-classification''',			task.task    )
     self.assertEqual(_lowerCamelCase,			task.input_schema    )
     self.assertEqual(_lowerCamelCase,			task.label_schema    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					TextClassification(text_column='''input_text''',			label_column='''input_label'''    )
     self.assertEqual(task.label_schema['''labels'''],			_lowerCamelCase    )
     __A		     =					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels    )}    )    )
     self.assertEqual(task.label_schema['''labels'''],			ClassLabel(names=self.labels    )    )





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					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 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					Features({'''question''': Value('''string'''    ), '''context''': Value('''string'''    )}    )
     __A		     =					Features(
         {
             '''answers''': Sequence(
                 {
                     '''text''': Value('''string'''    ),
                     '''answer_start''': Value('''int32'''    ),
                 }    )
         }    )
     __A		     =					{
         '''context_column''': '''input_input_context''',
         '''question_column''': '''input_question''',
         '''answers_column''': '''input_answers''',
     }
     __A		     =					QuestionAnsweringExtractive.from_dict(_lowerCamelCase    )
     self.assertEqual('''question-answering-extractive''',			task.task    )
     self.assertEqual(_lowerCamelCase,			task.input_schema    )
     self.assertEqual(_lowerCamelCase,			task.label_schema    )





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					Summarization(text_column='''input_text''',			summary_column='''input_summary'''    )
     self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''},			task.column_mapping    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					Features({'''text''': Value('''string'''    )}    )
     __A		     =					Features({'''summary''': Value('''string'''    )}    )
     __A		     =					{'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
     __A		     =					Summarization.from_dict(_lowerCamelCase    )
     self.assertEqual('''summarization''',			task.task    )
     self.assertEqual(_lowerCamelCase,			task.input_schema    )
     self.assertEqual(_lowerCamelCase,			task.label_schema    )





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					AutomaticSpeechRecognition(audio_column='''input_audio''',			transcription_column='''input_transcription'''    )
     self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''},			task.column_mapping    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					Features({'''audio''': Audio()}    )
     __A		     =					Features({'''transcription''': Value('''string'''    )}    )
     __A		     =					{
         '''audio_column''': '''input_audio''',
         '''transcription_column''': '''input_transcription''',
     }
     __A		     =					AutomaticSpeechRecognition.from_dict(_lowerCamelCase    )
     self.assertEqual('''automatic-speech-recognition''',			task.task    )
     self.assertEqual(_lowerCamelCase,			task.input_schema    )
     self.assertEqual(_lowerCamelCase,			task.label_schema    )





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					sorted(['''pos''', '''neg''']    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					AudioClassification(audio_column='''input_audio''',			label_column='''input_label'''    )
     self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''},			task.column_mapping    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					Features({'''audio''': Audio()}    )
     __A		     =					Features({'''labels''': ClassLabel}    )
     __A		     =					{
         '''audio_column''': '''input_image''',
         '''label_column''': '''input_label''',
     }
     __A		     =					AudioClassification.from_dict(_lowerCamelCase    )
     self.assertEqual('''audio-classification''',			task.task    )
     self.assertEqual(_lowerCamelCase,			task.input_schema    )
     self.assertEqual(_lowerCamelCase,			task.label_schema    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					AudioClassification(audio_column='''input_audio''',			label_column='''input_label'''    )
     self.assertEqual(task.label_schema['''labels'''],			_lowerCamelCase    )
     __A		     =					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels    )}    )    )
     self.assertEqual(task.label_schema['''labels'''],			ClassLabel(names=self.labels    )    )





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

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

     '''simple docstring'''
     __A		     =					sorted(['''pos''', '''neg''']    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					ImageClassification(image_column='''input_image''',			label_column='''input_label'''    )
     self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''},			task.column_mapping    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     __A		     =					Features({'''image''': Image()}    )
     __A		     =					Features({'''labels''': ClassLabel}    )
     __A		     =					{
         '''image_column''': '''input_image''',
         '''label_column''': '''input_label''',
     }
     __A		     =					ImageClassification.from_dict(_lowerCamelCase    )
     self.assertEqual('''image-classification''',			task.task    )
     self.assertEqual(_lowerCamelCase,			task.input_schema    )
     self.assertEqual(_lowerCamelCase,			task.label_schema    )






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

     '''simple docstring'''
     __A		     =					ImageClassification(image_column='''input_image''',			label_column='''input_label'''    )
     self.assertEqual(task.label_schema['''labels'''],			_lowerCamelCase    )
     __A		     =					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels    )}    )    )
     self.assertEqual(task.label_schema['''labels'''],			ClassLabel(names=self.labels    )    )





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					DatasetInfo(task_templates=QuestionAnsweringExtractive()    )
     __A		     =					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					:	Optional[int]    ):
       return x

     def dont_keep_task(_lowerCamelCase					:	int    ):
       __A		     =					deepcopy(_lowerCamelCase    )
       __A		     =					0
       return out

     __A		     =					dataset.map(_lowerCamelCase    )
     assert mapped_dataset.info.task_templates == dataset.info.task_templates
     # reload from cache
     __A		     =					dataset.map(_lowerCamelCase    )
     assert mapped_dataset.info.task_templates == dataset.info.task_templates

     __A		     =					dataset.map(_lowerCamelCase    )
     assert mapped_dataset.info.task_templates == []
     # reload from cache
     __A		     =					dataset.map(_lowerCamelCase    )
     assert mapped_dataset.info.task_templates == []






   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					Features({'''text''': Value('''string'''    ), '''label''': ClassLabel(names=('''pos''', '''neg''')    )}    )
     __A		     =					TextClassification(text_column='''text''',			label_column='''label'''    )
     __A		     =					DatasetInfo(features=_lowerCamelCase,			task_templates=_lowerCamelCase    )
     __A		     =					Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']},			info=_lowerCamelCase    )

     def process(_lowerCamelCase					:	List[str]    ):
       return example

     __A		     =					dataset.remove_columns('''label'''    )
     __A		     =					modified_dataset.map(_lowerCamelCase    )
     assert mapped_dataset.info.task_templates == []





def  UpperCAmelCase__(    lowerCamelCase_ = 1000	):



			"""simple docstring"""





			return sum(i for i in range(lowerCamelCase_	) if i % 3 == 0 or i % 5 == 0	)


if __name__ == "__main__":
			print(F"{solution() = }")

