






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


snake_case_   :	Tuple							      ={
    '''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						( lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							task_cls()
		__A    =							asdict(A__	)
		__A    =							task_template_from_dict(A__	)
		assert task == reloaded





class        a__      :






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							LanguageModeling(text_column="input_text"						)
				assert {"input_text": "text"} == task.column_mapping






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__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        a__      (	A_		):






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							sorted(["pos", "neg"]						)






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							TextClassification(text_column="input_text"							, label_column="input_label"						)
				self.assertDictEqual({"input_text": "text", "input_label": "labels"}							, task.column_mapping						)






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__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 					_lowerCamelCase					(      self						)				->				Dict:
				__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        a__      (	A_		):






		def 					_lowerCamelCase					(      self						)				->				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 					_lowerCamelCase					(      self						)				->				int:
				__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        a__      (	A_		):






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							Summarization(text_column="input_text"							, summary_column="input_summary"						)
				self.assertDictEqual({"input_text": "text", "input_summary": "summary"}							, task.column_mapping						)






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__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        a__      (	A_		):






		def 					_lowerCamelCase					(      self						)				->				Any:
				__A    =							AutomaticSpeechRecognition(audio_column="input_audio"							, transcription_column="input_transcription"						)
				self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}							, task.column_mapping						)






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__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        a__      (	A_		):






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							sorted(["pos", "neg"]						)






		def 					_lowerCamelCase					(      self						)				->				Any:
				__A    =							AudioClassification(audio_column="input_audio"							, label_column="input_label"						)
				self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}							, task.column_mapping						)






		def 					_lowerCamelCase					(      self						)				->				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 					_lowerCamelCase					(      self						)				->				Dict:
				__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        a__      (	A_		):






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							sorted(["pos", "neg"]						)






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							ImageClassification(image_column="input_image"							, label_column="input_label"						)
				self.assertDictEqual({"input_image": "image", "input_label": "labels"}							, task.column_mapping						)






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__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 					_lowerCamelCase					(      self						)				->				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        a__      (	A_		):






		def 					_lowerCamelCase					(      self						)				->				int:
				__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(lowercase__						):
						return x

				def dont_keep_task(lowercase__						):
						__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 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__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(lowercase__						):
						return example

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



_A       =					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}



def 		lowercase_     (  A__						)				->				Union[str, Any]:
    """simple docstring"""

    return c.isdigit()



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    snake_case						=   []

    # iterate over the string in reverse order
    for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(A__						):
            stack.append(int(A__						)						)

        else:
            # pop values from stack can calculate the result
            # push the result onto the stack again
            snake_case						=   stack.pop()
            snake_case						=   stack.pop()
            stack.append(calc[c](A__					,						A__						)						)

    return stack.pop()


# Driver code
if __name__ == "__main__":
   _A       =					"+ 9 * 2 6"
   print(evaluate(test_expression))

   _A       =					"/ * 10 2 + 4 1 "
   print(evaluate(test_expression))

