

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


__A	:   str	=					{
    '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     (      _A					:							Dict		)				->Optional[int]:



							"""simple docstring"""
							lowerCamelCase_								=task_cls()
							lowerCamelCase_								=asdict(_A		)
							lowerCamelCase_								=task_template_from_dict(_A		)
							assert task == reloaded


class 			_SCREAMING_SNAKE_CASE      :







						def        _snake_case				(			self							)->							List[str]:
													lowerCamelCase_								=LanguageModeling(text_column="""input_text"""							)
													assert {"input_text": "text"} == task.column_mapping







						def        _snake_case				(			self							)->							Dict:
													lowerCamelCase_								=Features({"""text""": Value("""string"""							)}							)
													lowerCamelCase_								={"""text_column""": """input_text"""}
													lowerCamelCase_								=LanguageModeling.from_dict(lowerCamelCase__							)
													assert "language-modeling" == task.task
													assert input_schema == task.input_schema


class 			_SCREAMING_SNAKE_CASE      (  __UpperCAmelCase):







						def        _snake_case				(			self							)->							str:
													lowerCamelCase_								=sorted(["""pos""", """neg"""]							)







						def        _snake_case				(			self							)->							Dict:
													lowerCamelCase_								=TextClassification(text_column="""input_text"""     ,						label_column="""input_label"""							)
													self.assertDictEqual({"""input_text""": """text""", """input_label""": """labels"""}     ,						task.column_mapping							)







						def        _snake_case				(			self							)->							str:
													lowerCamelCase_								=Features({"""text""": Value("""string"""							)}							)
													# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
													lowerCamelCase_								=Features({"""labels""": ClassLabel}							)
													lowerCamelCase_								={"""text_column""": """input_text""", """label_column""": """input_labels"""}
													lowerCamelCase_								=TextClassification.from_dict(lowerCamelCase__							)
													self.assertEqual("""text-classification"""     ,						task.task							)
													self.assertEqual(lowerCamelCase__     ,						task.input_schema							)
													self.assertEqual(lowerCamelCase__     ,						task.label_schema							)







						def        _snake_case				(			self							)->							Union[str, Any]:
													lowerCamelCase_								=TextClassification(text_column="""input_text"""     ,						label_column="""input_label"""							)
													self.assertEqual(task.label_schema["""labels"""]     ,						lowerCamelCase__							)
													lowerCamelCase_								=task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels							)}							)							)
													self.assertEqual(task.label_schema["""labels"""]     ,						ClassLabel(names=self.labels							)							)


class 			_SCREAMING_SNAKE_CASE      (  __UpperCAmelCase):







						def        _snake_case				(			self							)->							Tuple:
													lowerCamelCase_								=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        _snake_case				(			self							)->							Dict:
													lowerCamelCase_								=Features({"""question""": Value("""string"""							), """context""": Value("""string"""							)}							)
													lowerCamelCase_								=Features(
													    {
													        """answers""": Sequence(
													            {
													                """text""": Value("""string"""							),
													                """answer_start""": Value("""int32"""							),
													            }							)
													    }							)
													lowerCamelCase_								={
													    """context_column""": """input_input_context""",
													    """question_column""": """input_question""",
													    """answers_column""": """input_answers""",
													}
													lowerCamelCase_								=QuestionAnsweringExtractive.from_dict(lowerCamelCase__							)
													self.assertEqual("""question-answering-extractive"""     ,						task.task							)
													self.assertEqual(lowerCamelCase__     ,						task.input_schema							)
													self.assertEqual(lowerCamelCase__     ,						task.label_schema							)


class 			_SCREAMING_SNAKE_CASE      (  __UpperCAmelCase):







						def        _snake_case				(			self							)->							str:
													lowerCamelCase_								=Summarization(text_column="""input_text"""     ,						summary_column="""input_summary"""							)
													self.assertDictEqual({"""input_text""": """text""", """input_summary""": """summary"""}     ,						task.column_mapping							)







						def        _snake_case				(			self							)->							List[str]:
													lowerCamelCase_								=Features({"""text""": Value("""string"""							)}							)
													lowerCamelCase_								=Features({"""summary""": Value("""string"""							)}							)
													lowerCamelCase_								={"""text_column""": """input_text""", """summary_column""": """input_summary"""}
													lowerCamelCase_								=Summarization.from_dict(lowerCamelCase__							)
													self.assertEqual("""summarization"""     ,						task.task							)
													self.assertEqual(lowerCamelCase__     ,						task.input_schema							)
													self.assertEqual(lowerCamelCase__     ,						task.label_schema							)


class 			_SCREAMING_SNAKE_CASE      (  __UpperCAmelCase):







						def        _snake_case				(			self							)->							List[Any]:
													lowerCamelCase_								=AutomaticSpeechRecognition(audio_column="""input_audio"""     ,						transcription_column="""input_transcription"""							)
													self.assertDictEqual({"""input_audio""": """audio""", """input_transcription""": """transcription"""}     ,						task.column_mapping							)







						def        _snake_case				(			self							)->							Union[str, Any]:
													lowerCamelCase_								=Features({"""audio""": Audio()}							)
													lowerCamelCase_								=Features({"""transcription""": Value("""string"""							)}							)
													lowerCamelCase_								={
													    """audio_column""": """input_audio""",
													    """transcription_column""": """input_transcription""",
													}
													lowerCamelCase_								=AutomaticSpeechRecognition.from_dict(lowerCamelCase__							)
													self.assertEqual("""automatic-speech-recognition"""     ,						task.task							)
													self.assertEqual(lowerCamelCase__     ,						task.input_schema							)
													self.assertEqual(lowerCamelCase__     ,						task.label_schema							)


class 			_SCREAMING_SNAKE_CASE      (  __UpperCAmelCase):







						def        _snake_case				(			self							)->							Any:
													lowerCamelCase_								=sorted(["""pos""", """neg"""]							)







						def        _snake_case				(			self							)->							Optional[int]:
													lowerCamelCase_								=AudioClassification(audio_column="""input_audio"""     ,						label_column="""input_label"""							)
													self.assertDictEqual({"""input_audio""": """audio""", """input_label""": """labels"""}     ,						task.column_mapping							)







						def        _snake_case				(			self							)->							List[str]:
													lowerCamelCase_								=Features({"""audio""": Audio()}							)
													lowerCamelCase_								=Features({"""labels""": ClassLabel}							)
													lowerCamelCase_								={
													    """audio_column""": """input_image""",
													    """label_column""": """input_label""",
													}
													lowerCamelCase_								=AudioClassification.from_dict(lowerCamelCase__							)
													self.assertEqual("""audio-classification"""     ,						task.task							)
													self.assertEqual(lowerCamelCase__     ,						task.input_schema							)
													self.assertEqual(lowerCamelCase__     ,						task.label_schema							)







						def        _snake_case				(			self							)->							int:
													lowerCamelCase_								=AudioClassification(audio_column="""input_audio"""     ,						label_column="""input_label"""							)
													self.assertEqual(task.label_schema["""labels"""]     ,						lowerCamelCase__							)
													lowerCamelCase_								=task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels							)}							)							)
													self.assertEqual(task.label_schema["""labels"""]     ,						ClassLabel(names=self.labels							)							)


class 			_SCREAMING_SNAKE_CASE      (  __UpperCAmelCase):







						def        _snake_case				(			self							)->							Optional[Any]:
													lowerCamelCase_								=sorted(["""pos""", """neg"""]							)







						def        _snake_case				(			self							)->							Union[str, Any]:
													lowerCamelCase_								=ImageClassification(image_column="""input_image"""     ,						label_column="""input_label"""							)
													self.assertDictEqual({"""input_image""": """image""", """input_label""": """labels"""}     ,						task.column_mapping							)







						def        _snake_case				(			self							)->							int:
													lowerCamelCase_								=Features({"""image""": Image()}							)
													lowerCamelCase_								=Features({"""labels""": ClassLabel}							)
													lowerCamelCase_								={
													    """image_column""": """input_image""",
													    """label_column""": """input_label""",
													}
													lowerCamelCase_								=ImageClassification.from_dict(lowerCamelCase__							)
													self.assertEqual("""image-classification"""     ,						task.task							)
													self.assertEqual(lowerCamelCase__     ,						task.input_schema							)
													self.assertEqual(lowerCamelCase__     ,						task.label_schema							)







						def        _snake_case				(			self							)->							Optional[Any]:
													lowerCamelCase_								=ImageClassification(image_column="""input_image"""     ,						label_column="""input_label"""							)
													self.assertEqual(task.label_schema["""labels"""]     ,						lowerCamelCase__							)
													lowerCamelCase_								=task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels							)}							)							)
													self.assertEqual(task.label_schema["""labels"""]     ,						ClassLabel(names=self.labels							)							)


class 			_SCREAMING_SNAKE_CASE      (  __UpperCAmelCase):







						def        _snake_case				(			self							)->							List[Any]:
													lowerCamelCase_								=DatasetInfo(task_templates=QuestionAnsweringExtractive()							)
													lowerCamelCase_								=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(_SCREAMING_SNAKE_CASE							):
																				return x

													def dont_keep_task(_SCREAMING_SNAKE_CASE							):
																				lowerCamelCase_								=deepcopy(lowerCamelCase__							)
																				lowerCamelCase_								=0
																				return out

													lowerCamelCase_								=dataset.map(lowerCamelCase__							)
													assert mapped_dataset.info.task_templates == dataset.info.task_templates
													# reload from cache
													lowerCamelCase_								=dataset.map(lowerCamelCase__							)
													assert mapped_dataset.info.task_templates == dataset.info.task_templates

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







						def        _snake_case				(			self							)->							Dict:
													lowerCamelCase_								=Features({"""text""": Value("""string"""							), """label""": ClassLabel(names=("""pos""", """neg""")							)}							)
													lowerCamelCase_								=TextClassification(text_column="""text"""     ,						label_column="""label"""							)
													lowerCamelCase_								=DatasetInfo(features=lowerCamelCase__     ,						task_templates=lowerCamelCase__							)
													lowerCamelCase_								=Dataset.from_dict({"""text""": ["""A sentence."""], """label""": ["""pos"""]}     ,						info=lowerCamelCase__							)

													def process(_SCREAMING_SNAKE_CASE							):
																				return example

													lowerCamelCase_								=dataset.remove_columns("""label"""							)
													lowerCamelCase_								=modified_dataset.map(lowerCamelCase__							)
													assert mapped_dataset.info.task_templates == []

"""simple docstring"""






from sklearn.neural_network import MLPClassifier

lowerCAmelCase__    :    Any			=      [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase__    :    Optional[int]			=      [0, 1, 0, 0]


lowerCAmelCase__    :    Dict			=      MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase__    :    Optional[int]			=      [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase__    :    Union[str, Any]			=      clf.predict(test)



def     a_		(   lowerCamelCase			):
				return list(lowerCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



