






'''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 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	Tuple							)			->			Any:





						"""simple docstring"""
						lowercase_      :   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}}'							)
						lowercase_      :   List[Any]        =			DatasetInfosDict.from_directory(a							)
						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 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	DatasetInfo							)			->			str:





						"""simple docstring"""
						lowercase_      :   Dict        =			str(a							)
						dataset_info.write_to_directory(a							)
						lowercase_      :   Tuple        =			DatasetInfo.from_directory(a							)
						assert dataset_info == reloaded
						assert os.path.exists(os.path.join(a						,			'dataset_info.json'							)							)






def 	_UpperCAmelCase    (  )			->			Optional[int]:





						"""simple docstring"""
						lowercase_      :   Dict        =			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						,			)
						lowercase_      :   int        =			dataset_info._to_yaml_dict()
						assert sorted(a							) == 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)							)
						lowercase_      :   List[Any]        =			yaml.safe_dump(a							)
						lowercase_      :   Dict        =			yaml.safe_load(a							)
						assert dataset_info_yaml_dict == reloaded






def 	_UpperCAmelCase    (  )			->			str:





						"""simple docstring"""
						lowercase_      :   Any        =			DatasetInfo()
						lowercase_      :   Dict        =			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 	_UpperCAmelCase    (  a		:	Dict						,			a		:	DatasetInfosDict							)			->			Optional[Any]:





						"""simple docstring"""
						lowercase_      :   Dict        =			str(a							)
						dataset_infos_dict.write_to_directory(a							)
						lowercase_      :   int        =			DatasetInfosDict.from_directory(a							)

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








'''simple docstring'''

from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:      int  				=				{
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      Union[str, Any]  				=				[
						    "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TrajectoryTransformerModel",
						    "TrajectoryTransformerPreTrainedModel",
						    "load_tf_weights_in_trajectory_transformer",
						]


if TYPE_CHECKING:
						from .configuration_trajectory_transformer import (
						    TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TrajectoryTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trajectory_transformer import (
												    TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TrajectoryTransformerModel,
												    TrajectoryTransformerPreTrainedModel,
												    load_tf_weights_in_trajectory_transformer,
												)


else:
						import sys

						A:      int  				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
