
"""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	(     SCREAMING_SNAKE_CASE:       str  ,	SCREAMING_SNAKE_CASE:       List[Any]    ):






							"""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(UpperCamelCase__    )
							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	(     SCREAMING_SNAKE_CASE:       Dict  ,	SCREAMING_SNAKE_CASE:       DatasetInfo    ):






							"""simple docstring"""



							_lowerCAmelCase							=      str(UpperCamelCase__    )
							dataset_info.write_to_directory(UpperCamelCase__    )
							_lowerCAmelCase							=      DatasetInfo.from_directory(UpperCamelCase__    )
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(UpperCamelCase__  ,	'dataset_info.json'    )    )






def 						__snake_case	(     ):






							"""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': 42}]  ,	download_checksums={}  ,	download_size=1337  ,	post_processing_size=442  ,	dataset_size=1234  ,	size_in_bytes=1337 + 442 + 1234  ,	)
							_lowerCAmelCase							=      dataset_info._to_yaml_dict()
							assert sorted(UpperCamelCase__    ) == 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(UpperCamelCase__    )
							_lowerCAmelCase							=      yaml.safe_load(UpperCamelCase__    )
							assert dataset_info_yaml_dict == reloaded






def 						__snake_case	(     ):






							"""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=42  ,	)
            }    ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42    ),
                'v2': DatasetInfo(dataset_size=1337    ),
            }    ),
    ]  ,	)
def 						__snake_case	(     SCREAMING_SNAKE_CASE:       Dict  ,	SCREAMING_SNAKE_CASE:       DatasetInfosDict    ):






							"""simple docstring"""



							_lowerCAmelCase							=      str(UpperCamelCase__    )
							dataset_infos_dict.write_to_directory(UpperCamelCase__    )
							_lowerCAmelCase							=      DatasetInfosDict.from_directory(UpperCamelCase__    )

							# 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(UpperCamelCase__  ,	'README.md'    )    )













import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			List[Any]    )->Optional[int]:
       # Initialise PyTorch model
       A__		   =		RemBertConfig.from_json_file(UpperCamelCase__    )
       print('''Building PyTorch model from configuration: {}'''.format(str(UpperCamelCase__    )    )    )
       A__		   =		RemBertModel(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    )

       # Save pytorch-model
       print('''Save PyTorch model to {}'''.format(UpperCamelCase__    )    )
       torch.save(model.state_dict()					,	UpperCamelCase__    )


if __name__ == "__main__":
       a__: List[Any]							   =						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--rembert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained RemBERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       a__: Union[str, Any]							   =						parser.parse_args()
       convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





