






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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]					,  snake_case_ :							Optional[int]       ):
 snake_case__			:				List[str]       =  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__			:				int       =  DatasetInfosDict.from_directory(_a       )
 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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Any					,  snake_case_ :							DatasetInfo       ):
 snake_case__			:				Optional[Any]       =  str(_a       )
 dataset_info.write_to_directory(_a       )
 snake_case__			:				Tuple       =  DatasetInfo.from_directory(_a       )
 assert dataset_info == reloaded
 assert os.path.exists(os.path.join(_a					,  "dataset_info.json"       )       )







def 					SCREAMING_SNAKE_CASE    (			):
 snake_case__			:				List[Any]       =  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__			:				Optional[Any]       =  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)       )
 snake_case__			:				List[Any]       =  yaml.safe_dump(_a       )
 snake_case__			:				Any       =  yaml.safe_load(_a       )
 assert dataset_info_yaml_dict == reloaded







def 					SCREAMING_SNAKE_CASE    (			):
 snake_case__			:				List[Any]       =  DatasetInfo()
 snake_case__			:				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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							DatasetInfosDict       ):
 snake_case__			:				Dict       =  str(_a       )
 dataset_infos_dict.write_to_directory(_a       )
 snake_case__			:				Any       =  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():
  snake_case__			:				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
  snake_case__			:				Optional[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"       )       )











import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


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



