"""simple docstring"""




import os

try:
			from .build_directory_md import good_file_paths
except ImportError:
			from build_directory_md import good_file_paths  # type: ignore

__lowerCAmelCase    :							Dict													=						list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__lowerCAmelCase    :							Optional[Any]													=						[file for file in filepaths if file != file.lower()]
if upper_files:
			print(F"{len(upper_files)} files contain uppercase characters:")
			print("\n".join(upper_files) + "\n")

__lowerCAmelCase    :							Tuple													=						[file for file in filepaths if ' ' in file]
if space_files:
			print(F"{len(space_files)} files contain space characters:")
			print("\n".join(space_files) + "\n")

__lowerCAmelCase    :							Any													=						[file for file in filepaths if '-' in file]
if hyphen_files:
			print(F"{len(hyphen_files)} files contain hyphen characters:")
			print("\n".join(hyphen_files) + "\n")

__lowerCAmelCase    :							List[str]													=						[file for file in filepaths if os.sep not in file]
if nodir_files:
			print(F"{len(nodir_files)} files are not in a directory:")
			print("\n".join(nodir_files) + "\n")

__lowerCAmelCase    :							Any													=						len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
			import sys

			sys.exit(bad_files)





"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   MobileBertConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   MobileBertForPreTraining(lowerCamelCase__     )
		# Load weights from tf checkpoint
		lowerCAmelCase__        =   load_tf_weights_in_mobilebert(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						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    :							Optional[int]													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




