"""simple docstring"""

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def __init__(				self							:    Optional[Any]     , UpperCamelCase__							:    NestedDataStructureLike[PathLike]     , UpperCamelCase__							:    Optional[NamedSplit] = None     , UpperCamelCase__							:    Optional[Features] = None     , UpperCamelCase__							:    str = None     , UpperCamelCase__							:    bool = False     , UpperCamelCase__							:    bool = False     , UpperCamelCase__							:    Optional[int] = None     , **UpperCamelCase__							:    Tuple     , )				->				Union[str, Any]:
													'''simple docstring'''



													super().__init__(
													    UpperCamelCase__     , split=UpperCamelCase__     , features=UpperCamelCase__     , cache_dir=UpperCamelCase__     , keep_in_memory=UpperCamelCase__     , streaming=UpperCamelCase__     , num_proc=UpperCamelCase__     , **UpperCamelCase__     , )
													__UpperCamelCase							=path_or_paths if isinstance(UpperCamelCase__     , UpperCamelCase__				) else {self.split: path_or_paths}
													__UpperCamelCase							=Csv(
													    cache_dir=UpperCamelCase__     , data_files=UpperCamelCase__     , features=UpperCamelCase__     , **UpperCamelCase__     , )





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				int:
													'''simple docstring'''



													if self.streaming:
																				__UpperCamelCase							=self.builder.as_streaming_dataset(split=self.split				)
													# Build regular (map-style) dataset
													else:
																				__UpperCamelCase							=None
																				__UpperCamelCase							=None
																				__UpperCamelCase							=None
																				__UpperCamelCase							=None

																				self.builder.download_and_prepare(
																				    download_config=UpperCamelCase__     , download_mode=UpperCamelCase__     , verification_mode=UpperCamelCase__     , base_path=UpperCamelCase__     , num_proc=self.num_proc     , )
																				__UpperCamelCase							=self.builder.as_dataset(
																				    split=self.split     , verification_mode=UpperCamelCase__     , in_memory=self.keep_in_memory				)
													return dataset






class 		_lowercase :



						"""simple docstring"""




						def __init__(				self							:    Dict     , UpperCamelCase__							:    Dataset     , UpperCamelCase__							:    Union[PathLike, BinaryIO]     , UpperCamelCase__							:    Optional[int] = None     , UpperCamelCase__							:    Optional[int] = None     , **UpperCamelCase__							:    Dict     , )				->				List[Any]:
													'''simple docstring'''



													if num_proc is not None and num_proc <= 0:
																				raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""				)

													__UpperCamelCase							=dataset
													__UpperCamelCase							=path_or_buf
													__UpperCamelCase							=batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
													__UpperCamelCase							=num_proc
													__UpperCamelCase							='''utf-8'''
													__UpperCamelCase							=to_csv_kwargs





						def   UpperCAmelCase_ (				self							:    int				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=self.to_csv_kwargs.pop('''path_or_buf'''     , UpperCamelCase__				)
													__UpperCamelCase							=self.to_csv_kwargs.pop('''header'''     , UpperCamelCase__				)
													__UpperCamelCase							=self.to_csv_kwargs.pop('''index'''     , UpperCamelCase__				)

													if isinstance(self.path_or_buf     , (str, bytes, os.PathLike)				):
																				with open(self.path_or_buf     , '''wb+'''				) as buffer:
																											__UpperCamelCase							=self._write(file_obj=UpperCamelCase__     , header=UpperCamelCase__     , index=UpperCamelCase__     , **self.to_csv_kwargs				)
													else:
																				__UpperCamelCase							=self._write(file_obj=self.path_or_buf     , header=UpperCamelCase__     , index=UpperCamelCase__     , **self.to_csv_kwargs				)
													return written





						def   UpperCAmelCase_ (				self							:    Tuple     , UpperCamelCase__							:    Union[str, Any]				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase							=args

													__UpperCamelCase							=query_table(
													    table=self.dataset.data     , key=slice(UpperCamelCase__     , offset + self.batch_size				)     , indices=self.dataset._indices     , )
													__UpperCamelCase							=batch.to_pandas().to_csv(
													    path_or_buf=UpperCamelCase__     , header=header if (offset == 0) else False     , index=UpperCamelCase__     , **UpperCamelCase__				)
													return csv_str.encode(self.encoding				)





						def   UpperCAmelCase_ (				self							:    Optional[int]     , UpperCamelCase__							:    BinaryIO     , UpperCamelCase__							:    Any     , UpperCamelCase__							:    Union[str, Any]     , **UpperCamelCase__							:    Dict				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=0

													if self.num_proc is None or self.num_proc == 1:
																				for offset in logging.tqdm(
																				    range(0     , len(self.dataset				)     , self.batch_size				)     , unit='''ba'''     , disable=not logging.is_progress_bar_enabled()     , desc='''Creating CSV from Arrow format'''     , ):
																											__UpperCamelCase							=self._batch_csv((offset, header, index, to_csv_kwargs)				)
																											written += file_obj.write(UpperCamelCase__				)

													else:
																				__UpperCamelCase	,      __UpperCamelCase							=len(self.dataset				), self.batch_size
																				with multiprocessing.Pool(self.num_proc				) as pool:
																											for csv_str in logging.tqdm(
																											    pool.imap(
																											        self._batch_csv     , [(offset, header, index, to_csv_kwargs) for offset in range(0     , UpperCamelCase__     , UpperCamelCase__				)]     , )     , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     , unit='''ba'''     , disable=not logging.is_progress_bar_enabled()     , desc='''Creating CSV from Arrow format'''     , ):
																																		written += file_obj.write(UpperCamelCase__				)

													return written




"""simple docstring"""

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


__lowercase    =				{
    '''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    lowerCAmelCase			(__UpperCamelCase							:    List[str]			):
							"""simple docstring"""


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






class 		_lowercase :



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				List[Any]:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    str				)				->				int:
													'''simple docstring'''



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






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				Dict:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=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]				)				->				Union[str, Any]:
													'''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(UpperCamelCase__				)
													self.assertEqual('''text-classification'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Optional[Any]:
													'''simple docstring'''



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






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Dict				)				->				List[Any]:
													'''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   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				Optional[Any]:
													'''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(UpperCamelCase__				)
													self.assertEqual('''question-answering-extractive'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				str:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    int				)				->				List[Any]:
													'''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(UpperCamelCase__				)
													self.assertEqual('''summarization'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				Dict:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				List[str]:
													'''simple docstring'''



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






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[str]:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				int:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    Dict				)				->				Any:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''audio''': Audio()}				)
													__UpperCamelCase							=Features({'''labels''': ClassLabel}				)
													__UpperCamelCase							={
													    '''audio_column''': '''input_image''',
													    '''label_column''': '''input_label''',
													}
													__UpperCamelCase							=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							:    Tuple				)				->				List[str]:
													'''simple docstring'''



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






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				str:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    str				)				->				Tuple:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''image''': Image()}				)
													__UpperCamelCase							=Features({'''labels''': ClassLabel}				)
													__UpperCamelCase							={
													    '''image_column''': '''input_image''',
													    '''label_column''': '''input_label''',
													}
													__UpperCamelCase							=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							:    str				)				->				Optional[int]:
													'''simple docstring'''



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






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=DatasetInfo(task_templates=QuestionAnsweringExtractive()				)
													__UpperCamelCase							=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(UpperCamelCase__							:    Optional[int]				):
																				return x

													def dont_keep_task(UpperCamelCase__							:    Optional[int]				):
																				__UpperCamelCase							=deepcopy(UpperCamelCase__				)
																				__UpperCamelCase							=0
																				return out

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

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





						def   UpperCAmelCase_ (				self							:    List[str]				)				->				Dict:
													'''simple docstring'''



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

													def process(UpperCamelCase__							:    Any				):
																				return example

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



