




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       ) ->			Any:
	a									=				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}}"""       )
	a									=				DatasetInfosDict.from_directory(__lowerCamelCase       )
	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 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[int]:
	a									=				str(__lowerCamelCase       )
	dataset_info.write_to_directory(__lowerCamelCase       )
	a									=				DatasetInfo.from_directory(__lowerCamelCase       )
	assert dataset_info == reloaded
	assert os.path.exists(os.path.join(__lowerCamelCase				,     """dataset_info.json"""       )       )





def 						__A						(      ) ->			str:
	a									=				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				,     )
	a									=				dataset_info._to_yaml_dict()
	assert sorted(__lowerCamelCase       ) == 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)       )
	a									=				yaml.safe_dump(__lowerCamelCase       )
	a									=				yaml.safe_load(__lowerCamelCase       )
	assert dataset_info_yaml_dict == reloaded





def 						__A						(      ) ->			Any:
	a									=				DatasetInfo()
	a									=				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 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				str(__lowerCamelCase       )
	dataset_infos_dict.write_to_directory(__lowerCamelCase       )
	a									=				DatasetInfosDict.from_directory(__lowerCamelCase       )

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







import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :			Optional[Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				RobertaPreLayerNormConfig.from_pretrained(
	    __lowerCamelCase				,     architectures=["""RobertaPreLayerNormForMaskedLM"""]       )

	# convert state_dict
	a									=				torch.load(hf_hub_download(repo_id=__lowerCamelCase				,     filename="""pytorch_model.bin"""       )       )
	a									=				{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("""roberta."""       ):
			a									=				"""roberta_prelayernorm.""" + tensor_key[len("""roberta."""       ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(""".self.LayerNorm.weight"""       ) or tensor_key.endswith(""".self.LayerNorm.bias"""       ):
			continue

		a									=				tensor_value

	a									=				RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=__lowerCamelCase				,     config=__lowerCamelCase				,     state_dict=__lowerCamelCase       )
	model.save_pretrained(__lowerCamelCase       )

	# convert tokenizer
	a									=				AutoTokenizer.from_pretrained(__lowerCamelCase       )
	tokenizer.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Union[str, Any]          = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint-repo",
		    default=None,
		    type=str,
		    required=True,
		    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		__UpperCamelCase   :			Any          = parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

