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


lowerCamelCase_				:		Tuple       						=       get_logger(__name__)
def 							lowerCAmelCase(	__lowerCamelCase						):
					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 						a__							(   __snake_case		):
			A__				:   List[Any]								=			False  # to tell pytest it's not a test class



			@staticmethod
			def __SCREAMING_SNAKE_CASE							(						UpperCAmelCase    ) ->    str:
								__a							=   parser.add_parser('test'      ,						help='Test dataset implementation.'    )
								test_parser.add_argument('--name'      ,						type=UpperCAmelCase      ,						default=UpperCAmelCase      ,						help='Dataset processing name'    )
								test_parser.add_argument(
								    '--cache_dir'      ,						type=UpperCAmelCase      ,						default=UpperCAmelCase      ,						help='Cache directory where the datasets are stored.'      ,						)
								test_parser.add_argument(
								    '--data_dir'      ,						type=UpperCAmelCase      ,						default=UpperCAmelCase      ,						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=UpperCAmelCase      ,						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      ,						) ->    Tuple:
								__a							=   dataset
								__a							=   name
								__a							=   cache_dir
								__a							=   data_dir
								__a							=   all_configs
								__a							=   save_infos
								__a							=   ignore_verifications
								__a							=   force_redownload
								__a							=   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:
													__a							=   True







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[Any]:
								fl_logger().setLevel(UpperCAmelCase    )
								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    )
								__a					,      __a							=   self._dataset, self._name
								__a							=   dataset_module_factory(UpperCAmelCase    )
								__a							=   import_main_class(module.module_path    )
								__a							=   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=UpperCAmelCase      ,						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})'''    )
													__a							=   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=UpperCAmelCase      ,						)
													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:
																		__a							=   os.path.join(builder_cls.get_imported_module_dir()      ,						'README.md'    )
																		__a							=   Path(UpperCAmelCase    ).name + '.py'
																		__a							=   os.path.join(UpperCAmelCase      ,						UpperCAmelCase    )
																		if os.path.isfile(UpperCAmelCase    ):
																							__a							=   os.path.dirname(UpperCAmelCase    )
																		elif os.path.isfile(UpperCAmelCase    ):
																							__a							=   path
																		elif os.path.isdir(UpperCAmelCase    ):  # for local directories containing only data files
																							__a							=   path
																		else:  # in case of a remote dataset
																							__a							=   None
																							print(f'''Dataset card saved at {dataset_readme_path}'''    )

																		# Move dataset_info back to the user
																		if dataset_dir is not None:
																							__a							=   os.path.join(UpperCAmelCase      ,						'README.md'    )
																							copyfile(UpperCAmelCase      ,						UpperCAmelCase    )
																							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    )
																		__a							=   os.path.join(self._cache_dir      ,						datasets.config.DOWNLOADED_DATASETS_DIR    )
																		if os.path.isdir(UpperCAmelCase    ):
																							logger.warning(f'''Clearing cache at {download_dir}'''    )
																							rmtree(UpperCAmelCase    )

								print('Test successful.'    )





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 						a__							(   __snake_case		):



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls    ) ->    Optional[int]:
								return cls







			@slow
			@require_torch_gpu
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   self.get_auto_remove_tmp_dir()
								__a							=   f'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

								# we want this test to run even if there is only one GPU, but if there are more we use them all
								__a							=   get_gpu_count()
								__a							=   f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
								__a							=   [sys.executable] + distributed_args + args
								execute_subprocess_async(UpperCAmelCase      ,						env=self.get_env()    )

								__a							=   os.path.join(UpperCAmelCase      ,						'test_bleu.json'    )
								__a							=   load_json(UpperCAmelCase    )
								# print(metrics)
								self.assertGreaterEqual(metrics['bleu']      ,						2_5    )




