

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''': [515]},
}




@pytest.mark.parametrize(
    '''task_cls'''      , [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]      , )
def      UpperCAmelCase__							(     _A :					List[str]				):





  '''simple docstring'''







  a__    =task_cls()
  a__    =asdict(__UpperCamelCase				)
  a__    =task_template_from_dict(__UpperCamelCase				)
  assert task == reloaded







class 		__magic_name__     :

   '''simple docstring'''




   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =Features({'''text''': Value('''string'''			)}			)
     a__    ={'''text_column''': '''input_text'''}
     a__    =LanguageModeling.from_dict(UpperCamelCase__			)
     assert "language-modeling" == task.task
     assert input_schema == task.input_schema







class 		__magic_name__     (				__a				):

   '''simple docstring'''




   def 					_UpperCAmelCase					(							self			)				->       Dict:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							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 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






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



   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     a__    =TextClassification(text_column='''input_text''',       label_column='''input_label'''			)
     self.assertEqual(task.label_schema['''labels'''],       UpperCamelCase__			)
     a__    =task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels			)}			)			)
     self.assertEqual(task.label_schema['''labels'''],       ClassLabel(names=self.labels			)			)







class 		__magic_name__     (				__a				):

   '''simple docstring'''




   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """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 					_UpperCAmelCase					(							self			)				->       Optional[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(UpperCamelCase__			)
     self.assertEqual('''question-answering-extractive''',       task.task			)
     self.assertEqual(UpperCamelCase__,       task.input_schema			)
     self.assertEqual(UpperCamelCase__,       task.label_schema			)







class 		__magic_name__     (				__a				):

   '''simple docstring'''




   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """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(UpperCamelCase__			)
     self.assertEqual('''summarization''',       task.task			)
     self.assertEqual(UpperCamelCase__,       task.input_schema			)
     self.assertEqual(UpperCamelCase__,       task.label_schema			)







class 		__magic_name__     (				__a				):

   '''simple docstring'''




   def 					_UpperCAmelCase					(							self			)				->       Dict:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self			)				->       List[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(UpperCamelCase__			)
     self.assertEqual('''automatic-speech-recognition''',       task.task			)
     self.assertEqual(UpperCamelCase__,       task.input_schema			)
     self.assertEqual(UpperCamelCase__,       task.label_schema			)







class 		__magic_name__     (				__a				):

   '''simple docstring'''




   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self			)				->       Any:






     """simple docstring"""






     a__    =Features({'''audio''': Audio()}			)
     a__    =Features({'''labels''': ClassLabel}			)
     a__    ={
         '''audio_column''': '''input_image''',
         '''label_column''': '''input_label''',
     }
     a__    =AudioClassification.from_dict(UpperCamelCase__			)
     self.assertEqual('''audio-classification''',       task.task			)
     self.assertEqual(UpperCamelCase__,       task.input_schema			)
     self.assertEqual(UpperCamelCase__,       task.label_schema			)



   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =AudioClassification(audio_column='''input_audio''',       label_column='''input_label'''			)
     self.assertEqual(task.label_schema['''labels'''],       UpperCamelCase__			)
     a__    =task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels			)}			)			)
     self.assertEqual(task.label_schema['''labels'''],       ClassLabel(names=self.labels			)			)







class 		__magic_name__     (				__a				):

   '''simple docstring'''




   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							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 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =Features({'''image''': Image()}			)
     a__    =Features({'''labels''': ClassLabel}			)
     a__    ={
         '''image_column''': '''input_image''',
         '''label_column''': '''input_label''',
     }
     a__    =ImageClassification.from_dict(UpperCamelCase__			)
     self.assertEqual('''image-classification''',       task.task			)
     self.assertEqual(UpperCamelCase__,       task.input_schema			)
     self.assertEqual(UpperCamelCase__,       task.label_schema			)



   def 					_UpperCAmelCase					(							self			)				->       Optional[int]:






     """simple docstring"""






     a__    =ImageClassification(image_column='''input_image''',       label_column='''input_label'''			)
     self.assertEqual(task.label_schema['''labels'''],       UpperCamelCase__			)
     a__    =task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels			)}			)			)
     self.assertEqual(task.label_schema['''labels'''],       ClassLabel(names=self.labels			)			)







class 		__magic_name__     (				__a				):

   '''simple docstring'''




   def 					_UpperCAmelCase					(							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=UpperCamelCase__			)
     assert isinstance(dataset.info.task_templates,       UpperCamelCase__			)
     assert len(dataset.info.task_templates			) == 1

     def keep_task(lowercase_			):
       return x

     def dont_keep_task(lowercase_			):
       a__    =deepcopy(UpperCamelCase__			)
       a__    =0
       return out

     a__    =dataset.map(UpperCamelCase__			)
     assert mapped_dataset.info.task_templates == dataset.info.task_templates
     # reload from cache
     a__    =dataset.map(UpperCamelCase__			)
     assert mapped_dataset.info.task_templates == dataset.info.task_templates

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



   def 					_UpperCAmelCase					(							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=UpperCamelCase__,       task_templates=UpperCamelCase__			)
     a__    =Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']},       info=UpperCamelCase__			)

     def process(lowercase_			):
       return example

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

"""simple docstring"""

class 		_lowercase :



						"""simple docstring"""




						def __init__(				self							:    List[str]     , UpperCamelCase__							:    int				)				->				None:
													'''simple docstring'''



													__UpperCamelCase							=data
													__UpperCamelCase							=None
													__UpperCamelCase							=None






def    lowerCAmelCase			(__UpperCamelCase							:    BinaryTreeNode | None							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							if node is None:
														__UpperCamelCase							=BinaryTreeNode(__UpperCamelCase			)
														return node

							# binary search tree is not empty,
							# so we will insert it into the tree
							# if new_value is less than value of data in node,
							#  add it to left subtree and proceed recursively
							if new_value < node.data:
														__UpperCamelCase							=insert(node.left_child							,					__UpperCamelCase			)
							else:
														# if new_value is greater than value of data in node,
														#  add it to right subtree and proceed recursively
														__UpperCamelCase							=insert(node.right_child							,					__UpperCamelCase			)
							return node


def    lowerCAmelCase			(__UpperCamelCase							:    None | BinaryTreeNode			):  # if node is None,return
							"""simple docstring"""


							if node:
														__UpperCamelCase							=inorder(node.left_child			)
														__UpperCamelCase							=[*inorder_array, node.data]
														__UpperCamelCase							=inorder_array + inorder(node.right_child			)
							else:
														__UpperCamelCase							=[]
							return inorder_array


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							=insert(__UpperCamelCase							,					1_5			)
							insert(__UpperCamelCase							,					1_0			)
							insert(__UpperCamelCase							,					2_5			)
							insert(__UpperCamelCase							,					6			)
							insert(__UpperCamelCase							,					1_4			)
							insert(__UpperCamelCase							,					2_0			)
							insert(__UpperCamelCase							,					6_0			)
							return root


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							=make_tree()
							print('''Printing values of binary search tree in Inorder Traversal.'''			)
							inorder(__UpperCamelCase			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()



