


import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


UpperCAmelCase__        =				get_logger(__name__)
def 							UpperCAmelCase_   (				__snake_case		)	->     Dict:







 """simple docstring"""


 return TestCommand(
     args.dataset	,		args.name	,		args.cache_dir	,		args.data_dir	,		args.all_configs	,		args.save_info or args.save_infos	,		args.ignore_verifications	,		args.force_redownload	,		args.clear_cache	,		)







class  lowerCamelCase__  (			_A):
     SCREAMING_SNAKE_CASE__      =     False  # to tell pytest it's not a test class





     @staticmethod
     def 	__A   (UpperCAmelCase			)	->  List[Any]:
      _lowercase				 =parser.add_parser('''test'''						,	help='''Test dataset implementation.'''			)
      test_parser.add_argument('''--name'''						,	type=__lowerCamelCase						,	default=__lowerCamelCase						,	help='''Dataset processing name'''			)
      test_parser.add_argument(
          '''--cache_dir'''						,	type=__lowerCamelCase						,	default=__lowerCamelCase						,	help='''Cache directory where the datasets are stored.'''						,	)
      test_parser.add_argument(
          '''--data_dir'''						,	type=__lowerCamelCase						,	default=__lowerCamelCase						,	help='''Can be used to specify a manual directory to get the files from.'''						,	)
      test_parser.add_argument('''--all_configs'''						,	action='''store_true'''						,	help='''Test all dataset configurations'''			)
      test_parser.add_argument(
          '''--save_info'''						,	action='''store_true'''						,	help='''Save the dataset infos in the dataset card (README.md)'''			)
      test_parser.add_argument(
          '''--ignore_verifications'''						,	action='''store_true'''						,	help='''Run the test without checksums and splits checks.'''						,	)
      test_parser.add_argument('''--force_redownload'''						,	action='''store_true'''						,	help='''Force dataset redownload'''			)
      test_parser.add_argument(
          '''--clear_cache'''						,	action='''store_true'''						,	help='''Remove downloaded files and cached datasets after each config test'''						,	)
      # aliases
      test_parser.add_argument('''--save_infos'''						,	action='''store_true'''						,	help='''alias to save_info'''			)
      test_parser.add_argument('''dataset'''						,	type=__lowerCamelCase						,	help='''Name of the dataset to download'''			)
      test_parser.set_defaults(func=_test_command_factory			)





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	)	->  List[Any]:
      _lowercase				 =dataset
      _lowercase				 =name
      _lowercase				 =cache_dir
      _lowercase				 =data_dir
      _lowercase				 =all_configs
      _lowercase				 =save_infos
      _lowercase				 =ignore_verifications
      _lowercase				 =force_redownload
      _lowercase				 =clear_cache
      if clear_cache and not cache_dir:
       print(
           '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
           '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
           '''Please provide a --cache_dir that will be used to test the dataset script.'''			)
       exit(1			)
      if save_infos:
       _lowercase				 =True





     def 	__A   (self			)	->  List[Any]:
      fl_logger().setLevel(__lowerCamelCase			)
      if self._name is not None and self._all_configs:
       print('''Both parameters `config` and `all_configs` can\'t be used at once.'''			)
       exit(1			)
      _lowercase				 =self._dataset, self._name
      _lowercase				 =dataset_module_factory(__lowerCamelCase			)
      _lowercase				 =import_main_class(module.module_path			)
      _lowercase				 =len(builder_cls.BUILDER_CONFIGS			) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

      def get_builders() -> Generator[DatasetBuilder, None, None]:
       if self._all_configs and builder_cls.BUILDER_CONFIGS:
        for i, config in enumerate(builder_cls.BUILDER_CONFIGS			):
         if "config_name" in module.builder_kwargs:
          yield builder_cls(
              cache_dir=self._cache_dir						,	data_dir=self._data_dir						,	**module.builder_kwargs						,	)
         else:
          yield builder_cls(
              config_name=config.name						,	cache_dir=self._cache_dir						,	data_dir=self._data_dir						,	**module.builder_kwargs						,	)
       else:
        if "config_name" in module.builder_kwargs:
         yield builder_cls(cache_dir=self._cache_dir						,	data_dir=self._data_dir						,	**module.builder_kwargs			)
        else:
         yield builder_cls(
             config_name=__lowerCamelCase						,	cache_dir=self._cache_dir						,	data_dir=self._data_dir						,	**module.builder_kwargs						,	)

      for j, builder in enumerate(get_builders()			):
       print(f"Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"			)
       _lowercase				 =os.path.exists(
           os.path.join(builder.get_imported_module_dir()						,	datasets.config.DATASETDICT_INFOS_FILENAME			)			)  # record checksums only if we need to update a (deprecated) dataset_infos.json
       builder.download_and_prepare(
           download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
           if not self._force_redownload
           else DownloadMode.FORCE_REDOWNLOAD						,	verification_mode=VerificationMode.NO_CHECKS
           if self._ignore_verifications
           else VerificationMode.ALL_CHECKS						,	try_from_hf_gcs=__lowerCamelCase						,	)
       builder.as_dataset()
       if self._save_infos:
        builder._save_infos()

       # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
       # The dataset_infos are saved in the YAML part of the README.md

       # Let's move it to the original directory of the dataset script, to allow the user to
       # upload them on S3 at the same time afterwards.
       if self._save_infos:
        _lowercase				 =os.path.join(builder_cls.get_imported_module_dir()						,	'''README.md'''			)
        _lowercase				 =Path(__lowerCamelCase			).name + ".py"
        _lowercase				 =os.path.join(__lowerCamelCase						,	__lowerCamelCase			)
        if os.path.isfile(__lowerCamelCase			):
         _lowercase				 =os.path.dirname(__lowerCamelCase			)
        elif os.path.isfile(__lowerCamelCase			):
         _lowercase				 =path
        elif os.path.isdir(__lowerCamelCase			):  # for local directories containing only data files
         _lowercase				 =path
        else:  # in case of a remote dataset
         _lowercase				 =None
         print(f"Dataset card saved at {dataset_readme_path}"			)

        # Move dataset_info back to the user
        if dataset_dir is not None:
         _lowercase				 =os.path.join(__lowerCamelCase						,	'''README.md'''			)
         copyfile(__lowerCamelCase						,	__lowerCamelCase			)
         print(f"Dataset card saved at {user_dataset_readme_path}"			)

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
       if self._clear_cache:
        if os.path.isdir(builder._cache_dir			):
         logger.warning(f"Clearing cache at {builder._cache_dir}"			)
         rmtree(builder._cache_dir			)
        _lowercase				 =os.path.join(self._cache_dir						,	datasets.config.DOWNLOADED_DATASETS_DIR			)
        if os.path.isdir(__lowerCamelCase			):
         logger.warning(f"Clearing cache at {download_dir}"			)
         rmtree(__lowerCamelCase			)

      print('''Test successful.'''			)






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


__SCREAMING_SNAKE_CASE     =	{
    """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     ):
			A				:       Optional[int]														=    task_cls()
			A				:       int														=    asdict(_lowerCamelCase     )
			A				:       int														=    task_template_from_dict(_lowerCamelCase     )
			assert task == reloaded

class 						lowerCamelCase_     :






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       str														=    LanguageModeling(text_column="input_text"			)
									assert {"input_text": "text"} == task.column_mapping







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					str:
									A				:       Tuple														=    Features({"text": Value("string"			)}			)
									A				:       Dict														=    {"text_column": "input_text"}
									A				:       Optional[int]														=    LanguageModeling.from_dict(__lowerCamelCase			)
									assert "language-modeling" == task.task
									assert input_schema == task.input_schema







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					str:
									A				:       List[Any]														=    sorted(["pos", "neg"]			)







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







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Dict:
									A				:       Optional[int]														=    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   :      Optional[int]			)   ->					Tuple:
									A				:       Dict														=    Features({"question": Value("string"			), "context": Value("string"			)}			)
									A				:       int														=    Features(
									    {
									        "answers": Sequence(
									            {
									                "text": Value("string"			),
									                "answer_start": Value("int32"			),
									            }			)
									    }			)
									A				:       Optional[int]														=    {
									    "context_column": "input_input_context",
									    "question_column": "input_question",
									    "answers_column": "input_answers",
									}
									A				:       Tuple														=    QuestionAnsweringExtractive.from_dict(__lowerCamelCase			)
									self.assertEqual("question-answering-extractive"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    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   :      List[Any]			)   ->					Any:
									A				:       Union[str, Any]														=    Features({"text": Value("string"			)}			)
									A				:       Tuple														=    Features({"summary": Value("string"			)}			)
									A				:       Optional[Any]														=    {"text_column": "input_text", "summary_column": "input_summary"}
									A				:       str														=    Summarization.from_dict(__lowerCamelCase			)
									self.assertEqual("summarization"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       Union[str, Any]														=    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   :      List[Any]			)   ->					str:
									A				:       Tuple														=    Features({"audio": Audio()}			)
									A				:       Any														=    Features({"transcription": Value("string"			)}			)
									A				:       List[str]														=    {
									    "audio_column": "input_audio",
									    "transcription_column": "input_transcription",
									}
									A				:       Dict														=    AutomaticSpeechRecognition.from_dict(__lowerCamelCase			)
									self.assertEqual("automatic-speech-recognition"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Union[str, Any]:
									A				:       Optional[int]														=    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   :      Tuple			)   ->					Tuple:
									A				:       Union[str, Any]														=    Features({"audio": Audio()}			)
									A				:       Dict														=    Features({"labels": ClassLabel}			)
									A				:       Tuple														=    {
									    "audio_column": "input_image",
									    "label_column": "input_label",
									}
									A				:       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			)   ->					Any:
									A				:       str														=    AudioClassification(audio_column="input_audio"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       List[str]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Optional[int]:
									A				:       int														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									A				:       List[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   :      Any			)   ->					int:
									A				:       Optional[int]														=    Features({"image": Image()}			)
									A				:       str														=    Features({"labels": ClassLabel}			)
									A				:       List[str]														=    {
									    "image_column": "input_image",
									    "label_column": "input_label",
									}
									A				:       Tuple														=    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   :      Union[str, Any]			)   ->					Tuple:
									A				:       str														=    ImageClassification(image_column="input_image"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       Optional[Any]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Optional[int]:
									A				:       Dict														=    DatasetInfo(task_templates=QuestionAnsweringExtractive()			)
									A				:       List[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   :      List[str]			):
												return x

									def dont_keep_task(__lowerCamelCase   :      Any			):
												A				:       Optional[Any]														=    deepcopy(__lowerCamelCase			)
												A				:       List[Any]														=    0
												return out

									A				:       Optional[int]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates
									# reload from cache
									A				:       List[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates

									A				:       Union[str, Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []
									# reload from cache
									A				:       Optional[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []







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

									def process(__lowerCamelCase   :      Dict			):
												return example

									A				:       List[Any]														=    dataset.remove_columns("label"			)
									A				:       Dict														=    modified_dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []