


"""simple docstring"""
import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			Dict	,   __snake_case       :			Union[str, Any]      ):




   """simple docstring"""







   _lowerCamelCase  :				Optional[int]      =		TaConfig.from_json_file(__snake_case      )
   print(F'Building PyTorch model from configuration: {config}'      )
   _lowerCamelCase  :				Any      =		TaForConditionalGeneration(__snake_case      )

   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__snake_case	,   __snake_case	,   __snake_case      )

   # Save pytorch-model
   print(F'Save PyTorch model to {pytorch_dump_path}'      )
   model.save_pretrained(__snake_case      )


if __name__ == "__main__":
     UpperCAmelCase   =	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(
         """--config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     UpperCAmelCase   =	parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""
import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


UpperCAmelCase   =	2 << 20  # 2mb
UpperCAmelCase   =	get_logger(__name__)




class   lowercase__							(				A_							):





   def 		UpperCamelCase_     (       self)     ->     Dict:
      _lowerCamelCase  :				Tuple      =		self._options.get_all_options().get("""runner""")
      return runner in [None, "DirectRunner", "PortableRunner"]







def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Any=False      ):




   """simple docstring"""







   _lowerCamelCase  :				Any      =		FileSystems
   if fs.exists(__snake_case      ):
      if force_upload:
         logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
      else:
         logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
         return
   with fs.create(__snake_case      ) as remote_file:
      with open(__snake_case	,   """rb"""      ) as local_file:
         _lowerCamelCase  :				Optional[Any]      =		local_file.read(__snake_case      )
         while chunk:
            remote_file.write(__snake_case      )
            _lowerCamelCase  :				Tuple      =		local_file.read(__snake_case      )





def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Union[str, Any]=False      ):




   """simple docstring"""







   _lowerCamelCase  :				int      =		FileSystems
   if os.path.exists(__snake_case      ):
      if force_download:
         logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
      else:
         logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
         return
   with fs.open(__snake_case      ) as remote_file:
      with open(__snake_case	,   """wb"""      ) as local_file:
         _lowerCamelCase  :				str      =		remote_file.read(__snake_case      )
         while chunk:
            local_file.write(__snake_case      )
            _lowerCamelCase  :				Any      =		remote_file.read(__snake_case      )

