






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          = {
    '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    __A    (   _lowercase       ):

					'''simple docstring'''







					_A									=       task_cls()
					_A									=       asdict(_lowercase       )
					_A									=       task_template_from_dict(_lowercase       )
					assert task == reloaded





class   SCREAMING_SNAKE_CASE :





							"""simple docstring"""

							def 					__A       (     self:      List[str]      )			-> Any:
												_A									=       LanguageModeling(text_column='''input_text'''      )
												assert {"input_text": "text"} == task.column_mapping



							def 					__A       (     self:      int      )			-> Union[str, Any]:
												_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 					__A       (     self:      Dict      )			-> Union[str, Any]:
												_A									=       sorted(['''pos''', '''neg''']      )

							def 					__A       (     self:      int      )			-> Optional[Any]:
												_A									=       TextClassification(text_column='''input_text'''						,   label_column='''input_label'''      )
												self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}						,   task.column_mapping      )

							def 					__A       (     self:      Dict      )			-> 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 					__A       (     self:      str      )			-> 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   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							def 					__A       (     self:      Optional[int]      )			-> List[str]:
												_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 					__A       (     self:      List[str]      )			-> 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   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							def 					__A       (     self:      Tuple      )			-> Optional[int]:
												_A									=       Summarization(text_column='''input_text'''						,   summary_column='''input_summary'''      )
												self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}						,   task.column_mapping      )



							def 					__A       (     self:      Optional[Any]      )			-> List[str]:
												_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 					__A       (     self:      Optional[int]      )			-> Union[str, Any]:
												_A									=       AutomaticSpeechRecognition(audio_column='''input_audio'''						,   transcription_column='''input_transcription'''      )
												self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}						,   task.column_mapping      )



							def 					__A       (     self:      List[Any]      )			-> Tuple:
												_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 					__A       (     self:      List[str]      )			-> Optional[int]:
												_A									=       sorted(['''pos''', '''neg''']      )

							def 					__A       (     self:      List[Any]      )			-> str:
												_A									=       AudioClassification(audio_column='''input_audio'''						,   label_column='''input_label'''      )
												self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}						,   task.column_mapping      )

							def 					__A       (     self:      Union[str, Any]      )			-> Union[str, Any]:
												_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 					__A       (     self:      Optional[Any]      )			-> List[str]:
												_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 					__A       (     self:      int      )			-> str:
												_A									=       sorted(['''pos''', '''neg''']      )

							def 					__A       (     self:      Dict      )			-> int:
												_A									=       ImageClassification(image_column='''input_image'''						,   label_column='''input_label'''      )
												self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}						,   task.column_mapping      )

							def 					__A       (     self:      Optional[Any]      )			-> 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 					__A       (     self:      int      )			-> int:
												_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 					__A       (     self:      Optional[Any]      )			-> Optional[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:      Union[str, Any]      ):
																	return x

												def dont_keep_task(__A:      Optional[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 					__A       (     self:      List[Any]      )			-> 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:      Optional[Any]      ):
																	return example

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











from math import factorial




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					return factorial(_lowercase       ) / (factorial(_lowercase       ) * factorial(n - r       ))




def    __A    (   ):

					'''simple docstring'''







					_A									=       0

					for i in range(1    ,	1_01       ):
										for j in range(1    ,	i + 1       ):
															if combinations(_lowercase    ,	_lowercase       ) > 1e6:
																				total += 1
					return total


if __name__ == "__main__":
				print(solution())



