


"""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


lowerCamelCase_  :  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_1_5]},
}
@pytest.mark.parametrize(
    '''task_cls'''			,       [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]			,       )
def _A		(		lowercase						):







    """simple docstring"""
    a											=task_cls()
    a											=asdict(lowercase						)
    a											=task_template_from_dict(lowercase						)
    assert task == reloaded






class  __A							:






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											=LanguageModeling(text_column='''input_text'''		)
     assert {"input_text": "text"} == task.column_mapping





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     a											=Features({'''text''': Value('''string'''		)}		)
     a											={'''text_column''': '''input_text'''}
     a											=LanguageModeling.from_dict(__A		)
     assert "language-modeling" == task.task
     assert input_schema == task.input_schema






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     a											=sorted(['''pos''', '''neg''']		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     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		)   ->	int:
     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(__A		)
     self.assertEqual('''text-classification'''					,       task.task		)
     self.assertEqual(__A					,       task.input_schema		)
     self.assertEqual(__A					,       task.label_schema		)





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     a											=TextClassification(text_column='''input_text'''					,       label_column='''input_label'''		)
     self.assertEqual(task.label_schema['''labels''']					,       __A		)
     a											=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels		)}		)		)
     self.assertEqual(task.label_schema['''labels''']					,       ClassLabel(names=self.labels		)		)






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     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		)   ->	Optional[Any]:
     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(__A		)
     self.assertEqual('''question-answering-extractive'''					,       task.task		)
     self.assertEqual(__A					,       task.input_schema		)
     self.assertEqual(__A					,       task.label_schema		)






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     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		)   ->	Tuple:
     a											=Features({'''text''': Value('''string'''		)}		)
     a											=Features({'''summary''': Value('''string'''		)}		)
     a											={'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
     a											=Summarization.from_dict(__A		)
     self.assertEqual('''summarization'''					,       task.task		)
     self.assertEqual(__A					,       task.input_schema		)
     self.assertEqual(__A					,       task.label_schema		)






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     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		)   ->	Dict:
     a											=Features({'''audio''': Audio()}		)
     a											=Features({'''transcription''': Value('''string'''		)}		)
     a											={
         '''audio_column''': '''input_audio''',
         '''transcription_column''': '''input_transcription''',
     }
     a											=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							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     a											=sorted(['''pos''', '''neg''']		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     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		)   ->	Dict:
     a											=Features({'''audio''': Audio()}		)
     a											=Features({'''labels''': ClassLabel}		)
     a											={
         '''audio_column''': '''input_image''',
         '''label_column''': '''input_label''',
     }
     a											=AudioClassification.from_dict(__A		)
     self.assertEqual('''audio-classification'''					,       task.task		)
     self.assertEqual(__A					,       task.input_schema		)
     self.assertEqual(__A					,       task.label_schema		)





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     a											=AudioClassification(audio_column='''input_audio'''					,       label_column='''input_label'''		)
     self.assertEqual(task.label_schema['''labels''']					,       __A		)
     a											=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels		)}		)		)
     self.assertEqual(task.label_schema['''labels''']					,       ClassLabel(names=self.labels		)		)






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     a											=sorted(['''pos''', '''neg''']		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     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		)   ->	Optional[Any]:
     a											=Features({'''image''': Image()}		)
     a											=Features({'''labels''': ClassLabel}		)
     a											={
         '''image_column''': '''input_image''',
         '''label_column''': '''input_label''',
     }
     a											=ImageClassification.from_dict(__A		)
     self.assertEqual('''image-classification'''					,       task.task		)
     self.assertEqual(__A					,       task.input_schema		)
     self.assertEqual(__A					,       task.label_schema		)





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     a											=ImageClassification(image_column='''input_image'''					,       label_column='''input_label'''		)
     self.assertEqual(task.label_schema['''labels''']					,       __A		)
     a											=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels		)}		)		)
     self.assertEqual(task.label_schema['''labels''']					,       ClassLabel(names=self.labels		)		)






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     a											=DatasetInfo(task_templates=QuestionAnsweringExtractive()		)
     a											=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(__A		):
         return x

     def dont_keep_task(__A		):
         a											=deepcopy(__A		)
         a											=0
         return out

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

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





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     a											=Features({'''text''': Value('''string'''		), '''label''': ClassLabel(names=('''pos''', '''neg''')		)}		)
     a											=TextClassification(text_column='''text'''					,       label_column='''label'''		)
     a											=DatasetInfo(features=__A					,       task_templates=__A		)
     a											=Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}					,       info=__A		)

     def process(__A		):
         return example

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



"""simple docstring"""







import pytest

from datasets.utils.version import Version
@pytest.mark.parametrize(
    '''other, expected_equality'''			,       [
        (Version('''1.0.0'''						), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''						), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]			,       )
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=Version('''1.0.0'''						)
    assert (version == other) is expected_equality
    assert (version != other) is not expected_equality
    assert (hash(lowercase						) == hash(lowercase						)) is expected_equality