


import argparse

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


logging.set_verbosity_info()

def  lowercase_						(						_A     :       Dict       ,   _A     :       Any       ,   _A     :       Optional[int]       ,   _A     :       List[Any]       ,   _A     :       Optional[Any]		):

      """simple docstring"""




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

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

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


if __name__ == "__main__":
    A :       int						=   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=50358, type=int, help="Vocab size of model")
    A :       Tuple						=   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,
    )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A :       Optional[int]						=   {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[Any]						=   ["MPNetTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[str]						=   [
        "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MPNetForMaskedLM",
        "MPNetForMultipleChoice",
        "MPNetForQuestionAnswering",
        "MPNetForSequenceClassification",
        "MPNetForTokenClassification",
        "MPNetLayer",
        "MPNetModel",
        "MPNetPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       Tuple						=   [
        "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFMPNetEmbeddings",
        "TFMPNetForMaskedLM",
        "TFMPNetForMultipleChoice",
        "TFMPNetForQuestionAnswering",
        "TFMPNetForSequenceClassification",
        "TFMPNetForTokenClassification",
        "TFMPNetMainLayer",
        "TFMPNetModel",
        "TFMPNetPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    A :       Union[str, Any]						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


