

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							:						Dict     			=		{
    """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_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Dict							= task_cls()
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= asdict(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= task_template_from_dict(_UpperCAmelCase					)
						assert task == reloaded



class 						__lowercase       :




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= LanguageModeling(text_column="input_text")
									assert {"input_text": "text"} == task.column_mapping




			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_:				List[Any]							= Features({"text": Value("string")})
									SCREAMING_SNAKE_CASE_:				List[str]							= {"text_column": "input_text"}
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= LanguageModeling.from_dict(lowerCAmelCase__)
									assert "language-modeling" == task.task
									assert input_schema == task.input_schema







class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= sorted(["pos", "neg"])



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







class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				List[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						:  int):
									SCREAMING_SNAKE_CASE_:				Any							= Features({"question": Value("string"), "context": Value("string")})
									SCREAMING_SNAKE_CASE_:				Optional[int]							= Features(
									    {
									        "answers": Sequence(
									            {
									                "text": Value("string"),
									                "answer_start": Value("int32"),
									            })
									    })
									SCREAMING_SNAKE_CASE_:				Optional[int]							= {
									    "context_column": "input_input_context",
									    "question_column": "input_question",
									    "answers_column": "input_answers",
									}
									SCREAMING_SNAKE_CASE_:				str							= QuestionAnsweringExtractive.from_dict(lowerCAmelCase__)
									self.assertEqual("question-answering-extractive"     ,		task.task)
									self.assertEqual(lowerCAmelCase__     ,		task.input_schema)
									self.assertEqual(lowerCAmelCase__     ,		task.label_schema)







class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







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







class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									SCREAMING_SNAKE_CASE_:				List[str]							= 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						:  Tuple):
									SCREAMING_SNAKE_CASE_:				str							= Features({"audio": Audio()})
									SCREAMING_SNAKE_CASE_:				Optional[int]							= Features({"transcription": Value("string")})
									SCREAMING_SNAKE_CASE_:				List[str]							= {
									    "audio_column": "input_audio",
									    "transcription_column": "input_transcription",
									}
									SCREAMING_SNAKE_CASE_:				List[str]							= AutomaticSpeechRecognition.from_dict(lowerCAmelCase__)
									self.assertEqual("automatic-speech-recognition"     ,		task.task)
									self.assertEqual(lowerCAmelCase__     ,		task.input_schema)
									self.assertEqual(lowerCAmelCase__     ,		task.label_schema)







class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									SCREAMING_SNAKE_CASE_:				List[str]							= sorted(["pos", "neg"])



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									SCREAMING_SNAKE_CASE_:				Dict							= 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						:  str):
									SCREAMING_SNAKE_CASE_:				Tuple							= Features({"audio": Audio()})
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= Features({"labels": ClassLabel})
									SCREAMING_SNAKE_CASE_:				Tuple							= {
									    "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						:  Tuple):
									SCREAMING_SNAKE_CASE_:				List[Any]							= AudioClassification(audio_column="input_audio"     ,		label_column="input_label")
									self.assertEqual(task.label_schema["labels"]     ,		lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Tuple							= task.align_with_features(Features({"input_label": ClassLabel(names=self.labels)}))
									self.assertEqual(task.label_schema["labels"]     ,		ClassLabel(names=self.labels))







class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									SCREAMING_SNAKE_CASE_:				List[str]							= sorted(["pos", "neg"])



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 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						:  Dict):
									SCREAMING_SNAKE_CASE_:				str							= Features({"image": Image()})
									SCREAMING_SNAKE_CASE_:				List[Any]							= Features({"labels": ClassLabel})
									SCREAMING_SNAKE_CASE_:				Dict							= {
									    "image_column": "input_image",
									    "label_column": "input_label",
									}
									SCREAMING_SNAKE_CASE_:				List[str]							= 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						:  Any):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= ImageClassification(image_column="input_image"     ,		label_column="input_label")
									self.assertEqual(task.label_schema["labels"]     ,		lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				str							= task.align_with_features(Features({"input_label": ClassLabel(names=self.labels)}))
									self.assertEqual(task.label_schema["labels"]     ,		ClassLabel(names=self.labels))







class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									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__						:  Optional[int]):
															return x

									def dont_keep_task(lowerCAmelCase__						:  Dict):
															SCREAMING_SNAKE_CASE_:				int							= deepcopy(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:				Optional[int]							= 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_:				Tuple							= dataset.map(lowerCAmelCase__)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates

									SCREAMING_SNAKE_CASE_:				str							= dataset.map(lowerCAmelCase__)
									assert mapped_dataset.info.task_templates == []
									# reload from cache
									SCREAMING_SNAKE_CASE_:				List[str]							= dataset.map(lowerCAmelCase__)
									assert mapped_dataset.info.task_templates == []




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

									def process(lowerCAmelCase__						:  List[str]):
															return example

									SCREAMING_SNAKE_CASE_:				List[str]							= dataset.remove_columns("label")
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= modified_dataset.map(lowerCAmelCase__)
									assert mapped_dataset.info.task_templates == []









def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= []
						SCREAMING_SNAKE_CASE_:				Any							= 1

						for i in range(len(_UpperCAmelCase					)					):
												if i + 1 < len(_UpperCAmelCase					) and text[i] == text[i + 1]:
																		count += 1
												else:
																		encoded.append((text[i], count)					)
																		SCREAMING_SNAKE_CASE_:				List[Any]							= 1

						return encoded







def     A_   (					_UpperCAmelCase					):
						return "".join(char * length for char, length in encoded					)


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

						testmod(name="""run_length_encode""", verbose=True)
						testmod(name="""run_length_decode""", verbose=True)






