

# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


A__							=				logging.get_logger(__name__)


if is_torch_available():
						import torch
						from torch import nn
						from torch.utils.data import Dataset

						from transformers import Trainer

						class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):







							def __init__( self:			List[str]	,			__UpperCamelCase:			int = 1_01   ):

												'''simple docstring'''





												__magic_name__				=     length







							def __len__( self:			Optional[int]   ):

												'''simple docstring'''





												return self.length







							def __getitem__( self:			Tuple	,			__UpperCamelCase:			Dict   ):

												'''simple docstring'''





												return i




						class 					__UpperCamelCase					:







							def __call__( self:			Union[str, Any]	,			__UpperCamelCase:			List[Any]   ):

												'''simple docstring'''





												return {"input_ids": torch.tensor(__UpperCamelCase   ), "labels": torch.tensor(__UpperCamelCase   )}




						class 					__UpperCamelCase					(       nn.Module      ):







							def __init__( self:			Tuple   ):

												'''simple docstring'''





												super().__init__()
												# Add some (unused) params otherwise DDP will complain.
												__magic_name__				=     nn.Linear(1_20	,			80   )







							def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			Union[str, Any]	,			__UpperCamelCase:			Union[str, Any]=None   ):

												'''simple docstring'''





												if labels is not None:
																	return torch.tensor(0.0	,			device=input_ids.device   ), input_ids
												else:
																	return input_ids




def        _lowercase  (					)					->						List[Any]:




					'''simple docstring'''







					__magic_name__				=     HfArgumentParser((TrainingArguments,)					)
					sys.argv += ["--output_dir", "./examples"]
					__magic_name__				=     parser.parse_args_into_dataclasses()[0]

					logger.warning(
					    F'Process rank: {training_args.local_rank}, device: {training_args.device}, '
					    F'tpu_num_cores: {training_args.tpu_num_cores}'       ,)

					# Essentially, what we want to verify in the distributed case is
					# that we get all samples back, in the right order.
					# (this is crucial for prediction for instance)
					for dataset_length in [1_0_0_1, 2_5_6, 1_5]:
										__magic_name__				=     DummyDataset(a_					)

										def compute_metrics(a_					:       EvalPrediction					) -> Dict:
															__magic_name__				=     list(range(len(a_					)					)					)
															__magic_name__				=     p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
															return {"success": success}

										__magic_name__				=     Trainer(
										    model=DummyModel()       ,args=a_       ,data_collator=DummyDataCollator()       ,eval_dataset=a_       ,compute_metrics=a_       ,)
										__magic_name__				=     trainer.evaluate()
										logger.info(a_					)
										if metrics["eval_success"] is not True:
															logger.error(a_					)
															exit(1					)

										__magic_name__				=     trainer.predict(a_					)
										logger.info(p.metrics					)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics					)
															exit(1					)

										__magic_name__				=     2

										__magic_name__				=     trainer.evaluate()
										logger.info(a_					)
										if metrics["eval_success"] is not True:
															logger.error(a_					)
															exit(1					)

										__magic_name__				=     trainer.predict(a_					)
										logger.info(p.metrics					)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics					)
															exit(1					)

										__magic_name__				=     None

					logger.info('🔥 All distributed tests successful'					)




def        _lowercase  (					a_					:       Tuple					)					->						int:




					'''simple docstring'''







					main()


if __name__ == "__main__":
						main()







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


A__							=				get_logger(__name__)




def        _lowercase  (					a_					:       int					)					->						Optional[int]:




					'''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 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: List[Any]												=	False  # to tell pytest it's not a test class







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( __UpperCamelCase:			ArgumentParser   ):

						'''simple docstring'''





						__magic_name__				=     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:			List[str]	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			):

						'''simple docstring'''





						__magic_name__				=     dataset
						__magic_name__				=     name
						__magic_name__				=     cache_dir
						__magic_name__				=     data_dir
						__magic_name__				=     all_configs
						__magic_name__				=     save_infos
						__magic_name__				=     ignore_verifications
						__magic_name__				=     force_redownload
						__magic_name__				=     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:
											__magic_name__				=     True







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						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   )
						__magic_name__,						__magic_name__				=     self._dataset, self._name
						__magic_name__				=     dataset_module_factory(__UpperCamelCase   )
						__magic_name__				=     import_main_class(module.module_path   )
						__magic_name__				=     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})'   )
											__magic_name__				=     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:
																__magic_name__				=     os.path.join(builder_cls.get_imported_module_dir()	,			'README.md'   )
																__magic_name__				=     Path(__UpperCamelCase   ).name + '.py'
																__magic_name__				=     os.path.join(__UpperCamelCase	,			__UpperCamelCase   )
																if os.path.isfile(__UpperCamelCase   ):
																					__magic_name__				=     os.path.dirname(__UpperCamelCase   )
																elif os.path.isfile(__UpperCamelCase   ):
																					__magic_name__				=     path
																elif os.path.isdir(__UpperCamelCase   ):  # for local directories containing only data files
																					__magic_name__				=     path
																else:  # in case of a remote dataset
																					__magic_name__				=     None
																					print(F'Dataset card saved at {dataset_readme_path}'   )

																# Move dataset_info back to the user
																if dataset_dir is not None:
																					__magic_name__				=     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   )
																__magic_name__				=     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.'   )




