





"""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       =	{
    '''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 		lowerCAmelCase_	(  lowercase_   :							Tuple		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       task_cls()
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       asdict(lowercase_		)
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       task_template_from_dict(lowercase_		)
 assert task == reloaded





class 			snake_case		:

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       LanguageModeling(text_column='''input_text'''						)
       assert {"input_text": "text"} == task.column_mapping



      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			str												=       Features({'''text''': Value('''string'''						)}						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       {'''text_column''': '''input_text'''}
       __SCREAMING_SNAKE_CASE :			Any												=       LanguageModeling.from_dict(_lowerCamelCase						)
       assert "language-modeling" == task.task
       assert input_schema == task.input_schema







class 			snake_case		(   __UpperCAmelCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE :			int												=       sorted(['''pos''', '''neg''']						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       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   :Any						):
       __SCREAMING_SNAKE_CASE :			Any												=       Features({'''text''': Value('''string'''						)}						)
       # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
       __SCREAMING_SNAKE_CASE :			List[str]												=       Features({'''labels''': ClassLabel}						)
       __SCREAMING_SNAKE_CASE :			Dict												=       {'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       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   :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       TextClassification(text_column='''input_text'''						,							label_column='''input_label'''						)
       self.assertEqual(task.label_schema['''labels''']						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels						)}						)						)
       self.assertEqual(task.label_schema['''labels''']						,							ClassLabel(names=self.labels						)						)







class 			snake_case		(   __UpperCAmelCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       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   :str						):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       Features({'''question''': Value('''string'''						), '''context''': Value('''string'''						)}						)
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       Features(
           {
               '''answers''': Sequence(
                   {
                       '''text''': Value('''string'''						),
                       '''answer_start''': Value('''int32'''						),
                   }						)
           }						)
       __SCREAMING_SNAKE_CASE :			str												=       {
           '''context_column''': '''input_input_context''',
           '''question_column''': '''input_question''',
           '''answers_column''': '''input_answers''',
       }
       __SCREAMING_SNAKE_CASE :			Dict												=       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		(   __UpperCAmelCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       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]						):
       __SCREAMING_SNAKE_CASE :			int												=       Features({'''text''': Value('''string'''						)}						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       Features({'''summary''': Value('''string'''						)}						)
       __SCREAMING_SNAKE_CASE :			Any												=       {'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
       __SCREAMING_SNAKE_CASE :			str												=       Summarization.from_dict(_lowerCamelCase						)
       self.assertEqual('''summarization'''						,							task.task						)
       self.assertEqual(_lowerCamelCase						,							task.input_schema						)
       self.assertEqual(_lowerCamelCase						,							task.label_schema						)







class 			snake_case		(   __UpperCAmelCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       __SCREAMING_SNAKE_CASE :			int												=       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   :Any						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       Features({'''audio''': Audio()}						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       Features({'''transcription''': Value('''string'''						)}						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       {
           '''audio_column''': '''input_audio''',
           '''transcription_column''': '''input_transcription''',
       }
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       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		(   __UpperCAmelCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       sorted(['''pos''', '''neg''']						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			Any												=       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   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       Features({'''audio''': Audio()}						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       Features({'''labels''': ClassLabel}						)
       __SCREAMING_SNAKE_CASE :			str												=       {
           '''audio_column''': '''input_image''',
           '''label_column''': '''input_label''',
       }
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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   :int						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       AudioClassification(audio_column='''input_audio'''						,							label_column='''input_label'''						)
       self.assertEqual(task.label_schema['''labels''']						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Dict												=       task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels						)}						)						)
       self.assertEqual(task.label_schema['''labels''']						,							ClassLabel(names=self.labels						)						)







class 			snake_case		(   __UpperCAmelCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			str												=       sorted(['''pos''', '''neg''']						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE :			int												=       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   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Any												=       Features({'''image''': Image()}						)
       __SCREAMING_SNAKE_CASE :			Dict												=       Features({'''labels''': ClassLabel}						)
       __SCREAMING_SNAKE_CASE :			Any												=       {
           '''image_column''': '''input_image''',
           '''label_column''': '''input_label''',
       }
       __SCREAMING_SNAKE_CASE :			Any												=       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   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Dict												=       ImageClassification(image_column='''input_image'''						,							label_column='''input_label'''						)
       self.assertEqual(task.label_schema['''labels''']						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Any												=       task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels						)}						)						)
       self.assertEqual(task.label_schema['''labels''']						,							ClassLabel(names=self.labels						)						)







class 			snake_case		(   __UpperCAmelCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			int												=       DatasetInfo(task_templates=QuestionAnsweringExtractive()						)
       __SCREAMING_SNAKE_CASE :			Any												=       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   :Any						):
        return x

       def dont_keep_task(_lowerCamelCase   :Dict						):
        __SCREAMING_SNAKE_CASE :			int												=       deepcopy(_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE :			List[Any]												=       0
        return out

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       dataset.map(_lowerCamelCase						)
       assert mapped_dataset.info.task_templates == dataset.info.task_templates
       # reload from cache
       __SCREAMING_SNAKE_CASE :			List[Any]												=       dataset.map(_lowerCamelCase						)
       assert mapped_dataset.info.task_templates == dataset.info.task_templates

       __SCREAMING_SNAKE_CASE :			List[Any]												=       dataset.map(_lowerCamelCase						)
       assert mapped_dataset.info.task_templates == []
       # reload from cache
       __SCREAMING_SNAKE_CASE :			List[Any]												=       dataset.map(_lowerCamelCase						)
       assert mapped_dataset.info.task_templates == []



      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       Features({'''text''': Value('''string'''						), '''label''': ClassLabel(names=('''pos''', '''neg''')						)}						)
       __SCREAMING_SNAKE_CASE :			Dict												=       TextClassification(text_column='''text'''						,							label_column='''label'''						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       DatasetInfo(features=_lowerCamelCase						,							task_templates=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}						,							info=_lowerCamelCase						)

       def process(_lowerCamelCase   :int						):
        return example

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       dataset.remove_columns('''label'''						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       modified_dataset.map(_lowerCamelCase						)
       assert mapped_dataset.info.task_templates == []








"""simple docstring"""





import math




def 		lowerCAmelCase_	(  lowercase_   :							int		):

 '''simple docstring'''






 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5 ,      int(math.sqrt(lowercase_		) + 1		) ,      6		):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True




def 		lowerCAmelCase_	(  lowercase_   :							int = 6008_5147_5143		):

 '''simple docstring'''






 try:
  __SCREAMING_SNAKE_CASE :			Any												=       int(lowercase_		)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''		)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       0
 if is_prime(lowercase_		):
  return n
 while n % 2 == 0:
  n //= 2
 if is_prime(lowercase_		):
  return n
 for i in range(3 ,      int(math.sqrt(lowercase_		)		) + 1 ,      2		):
  if n % i == 0:
   if is_prime(n // i		):
    __SCREAMING_SNAKE_CASE :			Optional[int]												=       n // i
    break
   elif is_prime(lowercase_		):
    __SCREAMING_SNAKE_CASE :			Any												=       i
 return max_number


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

