






"""simple docstring"""




# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


lowerCamelCase__			=   "Update an existing config file with the latest defaults while maintaining the old configuration."






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	str    ):
  A__											=							args.config_file
  if config_file is None and Path(UpperCamelCase    ).exists():
    A__											=							default_config_file
  elif not Path(UpperCamelCase    ).exists():
    raise ValueError(F"""The passed config file located at {config_file} doesn't exist."""    )
  A__											=							load_config_from_file(UpperCamelCase    )

  if config_file.endswith(""".json"""    ):
    config.to_json_file(UpperCamelCase    )
  else:
    config.to_yaml_file(UpperCamelCase    )
  return config_file






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	Dict    ):
  A__											=							parser.add_parser("""update"""		,				parents=UpperCamelCase		,				help=UpperCamelCase		,				formatter_class=UpperCamelCase    )
  parser.add_argument(
      """--config_file"""		,				default=UpperCamelCase		,				help=(
          """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      )		,				)

  parser.set_defaults(func=UpperCamelCase    )
  return parser






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]    ):
  A__											=							update_config(UpperCamelCase    )
  print(F"""Sucessfully updated the configuration file at {config_file}."""    )












"""simple docstring"""




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Tuple		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	Optional[int]    ):
  # Initialise PyTorch model
  A__											=							BertConfig.from_pretrained(
      """bert-large-cased"""		,				vocab_size=UpperCamelCase		,				max_position_embeddings=512		,				is_decoder=UpperCamelCase		,				add_cross_attention=UpperCamelCase		,				)
  A__											=							bert_config.to_dict()
  del bert_config_dict["type_vocab_size"]
  A__											=							BertGenerationConfig(**UpperCamelCase    )
  if is_encoder:
    A__											=							BertGenerationEncoder(UpperCamelCase    )
  else:
    A__											=							BertGenerationDecoder(UpperCamelCase    )
  print(F"""Building PyTorch model from configuration: {config}"""    )

  # Load weights from tf checkpoint
  load_tf_weights_in_bert_generation(
      UpperCamelCase		,				UpperCamelCase		,				model_class="""bert"""		,				is_encoder_named_decoder=UpperCamelCase		,				is_encoder=UpperCamelCase		,				)

  # Save pytorch-model
  print(F"""Save PyTorch model and config to {pytorch_dump_path}"""    )
  model.save_pretrained(UpperCamelCase    )


if __name__ == "__main__":
      lowerCamelCase__			=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
      lowerCamelCase__			=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )




