






"""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


__a							:						Optional[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_):
					a__ 							=	task_cls()
					a__ 							=	asdict(lowerCamelCase_)
					a__ 							=	task_template_from_dict(lowerCamelCase_)
					assert task == reloaded







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

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





							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												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 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





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

												'''simple docstring'''

												a__ 							=	sorted(['''pos''', '''neg''']     )




							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

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




							def 			lowercase		( self:  Tuple     ):

												'''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(__A     )
												self.assertEqual('''text-classification'''					,		task.task     )
												self.assertEqual(__A					,		task.input_schema     )
												self.assertEqual(__A					,		task.label_schema     )





							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												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 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							def 			lowercase		( self:  Optional[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 			lowercase		( 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(__A     )
												self.assertEqual('''question-answering-extractive'''					,		task.task     )
												self.assertEqual(__A					,		task.input_schema     )
												self.assertEqual(__A					,		task.label_schema     )







class 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

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





							def 			lowercase		( self:  Dict     ):

												'''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(__A     )
												self.assertEqual('''summarization'''					,		task.task     )
												self.assertEqual(__A					,		task.input_schema     )
												self.assertEqual(__A					,		task.label_schema     )







class 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							def 			lowercase		( self:  str     ):

												'''simple docstring'''

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





							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												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 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												a__ 							=	sorted(['''pos''', '''neg''']     )




							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

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




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

												'''simple docstring'''

												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 			lowercase		( self:  List[str]     ):

												'''simple docstring'''

												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 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												a__ 							=	sorted(['''pos''', '''neg''']     )




							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

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




							def 			lowercase		( 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(__A     )
												self.assertEqual('''image-classification'''					,		task.task     )
												self.assertEqual(__A					,		task.input_schema     )
												self.assertEqual(__A					,		task.label_schema     )





							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												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 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												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:  List[Any]     ):
																	return x

												def dont_keep_task(__A:  Any     ):
																	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 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												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:  Dict     ):
																	return example

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










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__a							:						Tuple   							=			{}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a							:						str   							=			['CpmTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a							:						List[str]   							=			['CpmTokenizerFast']


if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_cpm import CpmTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_cpm_fast import CpmTokenizerFast

else:
	import sys

	__a							:						Tuple   							=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


