





"""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     A__			(       __lowerCamelCase,		__lowerCamelCase					):





  """simple docstring"""
  _lowerCAmelCase	      =					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	      =					DatasetInfosDict.from_directory(_snake_case					)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 4_2







@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=4_2,		),
    ],		)
def     A__			(       __lowerCamelCase,		__lowerCamelCase					):





  """simple docstring"""
  _lowerCAmelCase	      =					str(_snake_case					)
  dataset_info.write_to_directory(_snake_case					)
  _lowerCAmelCase	      =					DatasetInfo.from_directory(_snake_case					)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(_snake_case,		'dataset_info.json'					)					)







def     A__			(       ):





  """simple docstring"""
  _lowerCAmelCase	      =					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': 4_2}],		download_checksums={},		download_size=1_3_3_7,		post_processing_size=4_4_2,		dataset_size=1_2_3_4,		size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4,		)
  _lowerCAmelCase	      =					dataset_info._to_yaml_dict()
  assert sorted(_snake_case					) == 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	      =					yaml.safe_dump(_snake_case					)
  _lowerCAmelCase	      =					yaml.safe_load(_snake_case					)
  assert dataset_info_yaml_dict == reloaded







def     A__			(       ):





  """simple docstring"""
  _lowerCAmelCase	      =					DatasetInfo()
  _lowerCAmelCase	      =					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=4_2,		)
            }					),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=4_2					),
                'v2': DatasetInfo(dataset_size=1_3_3_7					),
            }					),
    ],		)
def     A__			(       __lowerCamelCase,		__lowerCamelCase					):





  """simple docstring"""
  _lowerCAmelCase	      =					str(_snake_case					)
  dataset_infos_dict.write_to_directory(_snake_case					)
  _lowerCAmelCase	      =					DatasetInfosDict.from_directory(_snake_case					)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    _lowerCAmelCase	      =					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	      =					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(_snake_case,		'README.md'					)					)









"""simple docstring"""

import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a							      =     logging.getLogger(__name__)





@dataclass
class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[float]     =      field(
     default=0.0     ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
 UpperCAmelCase       :	bool     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''whether to use adafactor'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(
     default='''linear'''     ,      metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}     ,      )

