
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					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : Tuple    ):
       snake_case       :   Dict							=      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}}"    )
       snake_case       :   Union[str, Any]							=      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 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : DatasetInfo    ):
       snake_case       :   Union[str, Any]							=      str(__lowerCamelCase    )
       dataset_info.write_to_directory(__lowerCamelCase    )
       snake_case       :   Optional[Any]							=      DatasetInfo.from_directory(__lowerCamelCase    )
       assert dataset_info == reloaded
       assert os.path.exists(os.path.join(__lowerCamelCase				,					"dataset_info.json"    )    )


def 			UpperCamelCase					(    ):
       snake_case       :   Tuple							=      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				,					)
       snake_case       :   List[str]							=      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)    )
       snake_case       :   str							=      yaml.safe_dump(__lowerCamelCase    )
       snake_case       :   str							=      yaml.safe_load(__lowerCamelCase    )
       assert dataset_info_yaml_dict == reloaded


def 			UpperCamelCase					(    ):
       snake_case       :   Any							=      DatasetInfo()
       snake_case       :   Union[str, Any]							=      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 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : DatasetInfosDict    ):
       snake_case       :   Union[str, Any]							=      str(__lowerCamelCase    )
       dataset_infos_dict.write_to_directory(__lowerCamelCase    )
       snake_case       :   Optional[int]							=      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():
              snake_case       :   List[str]							=      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
              snake_case       :   int							=      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"    )    )












"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:str											=							BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
  print(F"Building PyTorch model from configuration: {config}"			)
  lowerCAmelCase__			:int											=							BertForPreTraining(_SCREAMING_SNAKE_CASE			)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # Save pytorch-model
  print(F"Save PyTorch model to {pytorch_dump_path}"			)
  torch.save(model.state_dict()     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  __A											=		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(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT 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											=		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
