


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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[int]	)     ->	Dict:



	"""simple docstring"""






	a_     			=   task_cls()
	a_     			=   asdict(UpperCamelCase	)
	a_     			=   task_template_from_dict(UpperCamelCase	)
	assert task == reloaded

class 			lowerCamelCase_					:





							def   __magic_name__      (							self    ):
								a_     			=   LanguageModeling(text_column="""input_text"""    )
								assert {"input_text": "text"} == task.column_mapping





							def   __magic_name__      (							self    ):
								a_     			=   Features({"""text""": Value("""string"""    )}    )
								a_     			=   {"""text_column""": """input_text"""}
								a_     			=   LanguageModeling.from_dict(_SCREAMING_SNAKE_CASE    )
								assert "language-modeling" == task.task
								assert input_schema == task.input_schema







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								a_     			=   sorted(["""pos""", """neg"""]    )





							def   __magic_name__      (							self    ):
								a_     			=   TextClassification(text_column="""input_text"""			,       label_column="""input_label"""    )
								self.assertDictEqual({"""input_text""": """text""", """input_label""": """labels"""}			,       task.column_mapping    )





							def   __magic_name__      (							self    ):
								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(_SCREAMING_SNAKE_CASE    )
								self.assertEqual("""text-classification"""			,       task.task    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.input_schema    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.label_schema    )





							def   __magic_name__      (							self    ):
								a_     			=   TextClassification(text_column="""input_text"""			,       label_column="""input_label"""    )
								self.assertEqual(task.label_schema["""labels"""]			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels    )}    )    )
								self.assertEqual(task.label_schema["""labels"""]			,       ClassLabel(names=self.labels    )    )







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								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   __magic_name__      (							self    ):
								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(_SCREAMING_SNAKE_CASE    )
								self.assertEqual("""question-answering-extractive"""			,       task.task    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.input_schema    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.label_schema    )







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								a_     			=   Summarization(text_column="""input_text"""			,       summary_column="""input_summary"""    )
								self.assertDictEqual({"""input_text""": """text""", """input_summary""": """summary"""}			,       task.column_mapping    )





							def   __magic_name__      (							self    ):
								a_     			=   Features({"""text""": Value("""string"""    )}    )
								a_     			=   Features({"""summary""": Value("""string"""    )}    )
								a_     			=   {"""text_column""": """input_text""", """summary_column""": """input_summary"""}
								a_     			=   Summarization.from_dict(_SCREAMING_SNAKE_CASE    )
								self.assertEqual("""summarization"""			,       task.task    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.input_schema    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.label_schema    )







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								a_     			=   AutomaticSpeechRecognition(audio_column="""input_audio"""			,       transcription_column="""input_transcription"""    )
								self.assertDictEqual({"""input_audio""": """audio""", """input_transcription""": """transcription"""}			,       task.column_mapping    )





							def   __magic_name__      (							self    ):
								a_     			=   Features({"""audio""": Audio()}    )
								a_     			=   Features({"""transcription""": Value("""string"""    )}    )
								a_     			=   {
								    """audio_column""": """input_audio""",
								    """transcription_column""": """input_transcription""",
								}
								a_     			=   AutomaticSpeechRecognition.from_dict(_SCREAMING_SNAKE_CASE    )
								self.assertEqual("""automatic-speech-recognition"""			,       task.task    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.input_schema    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.label_schema    )







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								a_     			=   sorted(["""pos""", """neg"""]    )





							def   __magic_name__      (							self    ):
								a_     			=   AudioClassification(audio_column="""input_audio"""			,       label_column="""input_label"""    )
								self.assertDictEqual({"""input_audio""": """audio""", """input_label""": """labels"""}			,       task.column_mapping    )





							def   __magic_name__      (							self    ):
								a_     			=   Features({"""audio""": Audio()}    )
								a_     			=   Features({"""labels""": ClassLabel}    )
								a_     			=   {
								    """audio_column""": """input_image""",
								    """label_column""": """input_label""",
								}
								a_     			=   AudioClassification.from_dict(_SCREAMING_SNAKE_CASE    )
								self.assertEqual("""audio-classification"""			,       task.task    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.input_schema    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.label_schema    )





							def   __magic_name__      (							self    ):
								a_     			=   AudioClassification(audio_column="""input_audio"""			,       label_column="""input_label"""    )
								self.assertEqual(task.label_schema["""labels"""]			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels    )}    )    )
								self.assertEqual(task.label_schema["""labels"""]			,       ClassLabel(names=self.labels    )    )







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								a_     			=   sorted(["""pos""", """neg"""]    )





							def   __magic_name__      (							self    ):
								a_     			=   ImageClassification(image_column="""input_image"""			,       label_column="""input_label"""    )
								self.assertDictEqual({"""input_image""": """image""", """input_label""": """labels"""}			,       task.column_mapping    )





							def   __magic_name__      (							self    ):
								a_     			=   Features({"""image""": Image()}    )
								a_     			=   Features({"""labels""": ClassLabel}    )
								a_     			=   {
								    """image_column""": """input_image""",
								    """label_column""": """input_label""",
								}
								a_     			=   ImageClassification.from_dict(_SCREAMING_SNAKE_CASE    )
								self.assertEqual("""image-classification"""			,       task.task    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.input_schema    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       task.label_schema    )





							def   __magic_name__      (							self    ):
								a_     			=   ImageClassification(image_column="""input_image"""			,       label_column="""input_label"""    )
								self.assertEqual(task.label_schema["""labels"""]			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels    )}    )    )
								self.assertEqual(task.label_schema["""labels"""]			,       ClassLabel(names=self.labels    )    )







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def   __magic_name__      (							self    ):
								a_     			=   DatasetInfo(task_templates=QuestionAnsweringExtractive()    )
								a_     			=   Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}			,       info=_SCREAMING_SNAKE_CASE    )
								assert isinstance(dataset.info.task_templates			,       _SCREAMING_SNAKE_CASE    )
								assert len(dataset.info.task_templates    ) == 1

								def keep_task(_SCREAMING_SNAKE_CASE    ):
									return x

								def dont_keep_task(_SCREAMING_SNAKE_CASE    ):
									a_     			=   deepcopy(_SCREAMING_SNAKE_CASE    )
									a_     			=   0
									return out

								a_     			=   dataset.map(_SCREAMING_SNAKE_CASE    )
								assert mapped_dataset.info.task_templates == dataset.info.task_templates
								# reload from cache
								a_     			=   dataset.map(_SCREAMING_SNAKE_CASE    )
								assert mapped_dataset.info.task_templates == dataset.info.task_templates

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





							def   __magic_name__      (							self    ):
								a_     			=   Features({"""text""": Value("""string"""    ), """label""": ClassLabel(names=("""pos""", """neg""")    )}    )
								a_     			=   TextClassification(text_column="""text"""			,       label_column="""label"""    )
								a_     			=   DatasetInfo(features=_SCREAMING_SNAKE_CASE			,       task_templates=_SCREAMING_SNAKE_CASE    )
								a_     			=   Dataset.from_dict({"""text""": ["""A sentence."""], """label""": ["""pos"""]}			,       info=_SCREAMING_SNAKE_CASE    )

								def process(_SCREAMING_SNAKE_CASE    ):
									return example

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



from .balanced_parentheses import balanced_parentheses
from .stack import Stack

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	int:



	"""simple docstring"""






	return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(UpperCamelCase     ,					-1	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	str:



	"""simple docstring"""






	if not balanced_parentheses(UpperCamelCase	):
		raise ValueError("""Mismatched parentheses"""	)
	a_     			=   Stack()
	a_     			=   []
	for char in expression_str:
		if char.isalpha() or char.isdigit():
			postfix.append(UpperCamelCase	)
		elif char == "(":
			stack.push(UpperCamelCase	)
		elif char == ")":
			while not stack.is_empty() and stack.peek() != "(":
				postfix.append(stack.pop()	)
			stack.pop()
		else:
			while not stack.is_empty() and precedence(UpperCamelCase	) <= precedence(stack.peek()	):
				postfix.append(stack.pop()	)
			stack.push(UpperCamelCase	)
	while not stack.is_empty():
		postfix.append(stack.pop()	)
	return " ".join(UpperCamelCase	)


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			_A							     =       'a+b*(c^d-e)^(f+g*h)-i'

			print('Infix to Postfix Notation demonstration:\n')
			print('Infix notation: ' + expression)
			print('Postfix notation: ' + infix_to_postfix(expression))