


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


__UpperCamelCase      						=    {
    '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_1_5]},
}
@pytest.mark.parametrize(
    "task_cls"			,       [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]			,       )
def     UpperCamelCase_(			_A				:int					)->						Tuple:
			UpperCamelCase__   =    task_cls()
			UpperCamelCase__   =    asdict(_A					)
			UpperCamelCase__   =    task_template_from_dict(_A					)
			assert task == reloaded







class     lowerCamelCase__		:






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    sorted(["pos", "neg"]       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


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




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    Features({"text": Value("string"       )}       )
						# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
						UpperCamelCase__   =    Features({"labels": ClassLabel}       )
						UpperCamelCase__   =    {"text_column": "input_text", "label_column": "input_labels"}
						UpperCamelCase__   =    TextClassification.from_dict(snake_case       )
						self.assertEqual("text-classification" ,  task.task       )
						self.assertEqual(snake_case ,  task.input_schema       )
						self.assertEqual(snake_case ,  task.label_schema       )







			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    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 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    Features({"question": Value("string"       ), "context": Value("string"       )}       )
						UpperCamelCase__   =    Features(
						    {
						        "answers": Sequence(
						            {
						                "text": Value("string"       ),
						                "answer_start": Value("int32"       ),
						            }       )
						    }       )
						UpperCamelCase__   =    {
						    "context_column": "input_input_context",
						    "question_column": "input_question",
						    "answers_column": "input_answers",
						}
						UpperCamelCase__   =    QuestionAnsweringExtractive.from_dict(snake_case       )
						self.assertEqual("question-answering-extractive" ,  task.task       )
						self.assertEqual(snake_case ,  task.input_schema       )
						self.assertEqual(snake_case ,  task.label_schema       )







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    sorted(["pos", "neg"]       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


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




			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    sorted(["pos", "neg"]       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


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




			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    DatasetInfo(task_templates=QuestionAnsweringExtractive()       )
						UpperCamelCase__   =    Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()} ,  info=snake_case       )
						assert isinstance(dataset.info.task_templates ,  snake_case       )
						assert len(dataset.info.task_templates       ) == 1

						def keep_task(snake_case       ):
									return x

						def dont_keep_task(snake_case       ):
									UpperCamelCase__   =    deepcopy(snake_case       )
									UpperCamelCase__   =    0
									return out

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

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







			def 			snake_case__			(							self       ):

						'''simple docstring'''


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

						def process(snake_case       ):
									return example

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






from __future__ import annotations
def     UpperCamelCase_(			_A				:list					)->						int | float:
			UpperCamelCase__   =    sorted(_A					)
			UpperCamelCase__   =    len(_A					)
			UpperCamelCase__   =    length >> 1
			return (
			    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
			    if length % 2 == 0
			    else sorted_list[mid_index]
			)
def     UpperCamelCase_(			)->						List[Any]:
			import doctest

			doctest.testmod()


if __name__ == "__main__":
						main()


