




"""simple docstring"""






import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict







@pytest.mark.parametrize(
    'files'							,					[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]							,					)
def   _snake_case    (       lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Optional[Any]					=					tmp_path_factory.mktemp('dset_infos_dir'	)
				if "full:README.md" in files:
								with open(dataset_infos_dir / 'README.md'							,					'w'	) as f:
												f.write('---\ndataset_info:\n  dataset_size: 42\n---'	)
				if "empty:README.md" in files:
								with open(dataset_infos_dir / 'README.md'							,					'w'	) as f:
												f.write(''	)
    # we want to support dataset_infos.json for backward compatibility
				if "dataset_infos.json" in files:
								with open(dataset_infos_dir / 'dataset_infos.json'							,					'w'	) as f:
												f.write('{"default": {"dataset_size": 42}}'	)
				_lowerCamelCase  :    List[Any]					=					DatasetInfosDict.from_directory(lowercase__	)
				assert dataset_infos
				assert dataset_infos["default"].dataset_size == 42







@pytest.mark.parametrize(
    'dataset_info'							,					[
        DatasetInfo(),
        DatasetInfo(
            description='foo'							,					features=Features({'a': Value('int32'	)}	)							,					builder_name='builder'							,					config_name='config'							,					version='1.0.0'							,					splits=[{'name': 'train'}]							,					download_size=42							,					),
    ]							,					)
def   _snake_case    (       lowercase__							,					lowercase__	):
				_lowerCamelCase  :    List[str]					=					str(lowercase__	)
				dataset_info.write_to_directory(lowercase__	)
				_lowerCamelCase  :    Union[str, Any]					=					DatasetInfo.from_directory(lowercase__	)
				assert dataset_info == reloaded
				assert os.path.exists(os.path.join(lowercase__							,					'dataset_info.json'	)	)







def   _snake_case    (       ):
				_lowerCamelCase  :    int					=					DatasetInfo(
				    description='foo'							,					citation='bar'							,					homepage='https://foo.bar'							,					license='CC0'							,					features=Features({'a': Value('int32'	)}	)							,					post_processed={}							,					supervised_keys=()							,					task_templates=[]							,					builder_name='builder'							,					config_name='config'							,					version='1.0.0'							,					splits=[{'name': 'train', 'num_examples': 42}]							,					download_checksums={}							,					download_size=1337							,					post_processing_size=442							,					dataset_size=1234							,					size_in_bytes=1337 + 442 + 1234							,					)
				_lowerCamelCase  :    Optional[Any]					=					dataset_info._to_yaml_dict()
				assert sorted(lowercase__	) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML	)
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								assert key in dataset_info_yaml_dict
								assert isinstance(dataset_info_yaml_dict[key]							,					(list, dict, int, str)	)
				_lowerCamelCase  :    Optional[Any]					=					yaml.safe_dump(lowercase__	)
				_lowerCamelCase  :    str					=					yaml.safe_load(lowercase__	)
				assert dataset_info_yaml_dict == reloaded







def   _snake_case    (       ):
				_lowerCamelCase  :    str					=					DatasetInfo()
				_lowerCamelCase  :    str					=					dataset_info._to_yaml_dict()
				assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    'dataset_infos_dict'							,					[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}	),
        DatasetInfosDict({'my_config_name': DatasetInfo()}	),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'							,					features=Features({'a': Value('int32'	)}	)							,					builder_name='builder'							,					config_name='config'							,					version='1.0.0'							,					splits=[{'name': 'train'}]							,					download_size=42							,					)
            }	),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42	),
                'v2': DatasetInfo(dataset_size=1337	),
            }	),
    ]							,					)
def   _snake_case    (       lowercase__							,					lowercase__	):
				_lowerCamelCase  :    int					=					str(lowercase__	)
				dataset_infos_dict.write_to_directory(lowercase__	)
				_lowerCamelCase  :    List[str]					=					DatasetInfosDict.from_directory(lowercase__	)

				# the config_name of the dataset_infos_dict take over the attribute
				for config_name, dataset_info in dataset_infos_dict.items():
								_lowerCamelCase  :    Optional[int]					=					config_name
								# the yaml representation doesn't include fields like description or citation
								# so we just test that we can recover what we can from the yaml
								_lowerCamelCase  :    Any					=					DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()	)
				assert dataset_infos_dict == reloaded

				if dataset_infos_dict:
								assert os.path.exists(os.path.join(lowercase__							,					'README.md'	)	)





"""simple docstring"""






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowercase__  		=		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 lowerCAmelCase__			(  lowercase		):






						'''simple docstring'''



						def __init__(						self						,   lowercase = 101						):
										_lowerCamelCase  :    Optional[int]					=					length
						def __len__(						self						):
										return self.length






						def __getitem__(						self						,   lowercase						):
										return i






			class lowerCAmelCase__			:






						'''simple docstring'''



						def __call__(						self						,   lowercase						):
										return {"input_ids": torch.tensor(lowercase						), "labels": torch.tensor(lowercase						)}






			class lowerCAmelCase__			(  nn.Module		):






						'''simple docstring'''



						def __init__(						self						):
										super().__init__()
										# Add some (unused) params otherwise DDP will complain.
										_lowerCamelCase  :    int					=					nn.Linear(120						,   80						)






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






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@require_torch_neuroncore
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
							_lowerCamelCase  :    int					=					self.get_auto_remove_tmp_dir()
							_lowerCamelCase  :    str					=					F'''--output_dir {output_dir}'''.split()
							_lowerCamelCase  :    Tuple					=					['torchrun'] + distributed_args + args
							execute_subprocess_async(lowercase						,   env=self.get_env()						)
							# successful return here == success - any errors would have caused an error in the sub-call






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@require_torch_multi_gpu
			def       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
							_lowerCamelCase  :    List[str]					=					self.get_auto_remove_tmp_dir()
							_lowerCamelCase  :    Any					=					F'''--output_dir {output_dir}'''.split()
							_lowerCamelCase  :    Any					=					['torchrun'] + distributed_args + args
							execute_subprocess_async(lowercase						,   env=self.get_env()						)
							# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
			# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
			#
			# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

			lowercase__  		=		HfArgumentParser((TrainingArguments,))
			lowercase__  		=		parser.parse_args_into_dataclasses()[0]

			logger.warning(
			    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
			    F"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
			)

			# 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 [101, 40, 7]:
						lowercase__  		=		DummyDataset(dataset_length)







						def   _snake_case    (       lowercase__	):
										_lowerCamelCase  :    int					=					list(range(len(lowercase__	)	)	)
										_lowerCamelCase  :    Optional[int]					=					p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
										if not success and training_args.local_rank == 0:
														logger.warning(
														    'Predictions and/or labels do not match expected results:\n  - predictions: '
														    f'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''	)
										return {"success": success}

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

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

						lowercase__  		=		2

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

						lowercase__  		=		trainer.predict(dataset)
						logger.info(p.metrics)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics)
									exit(1)
						lowercase__  		=		None