

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase     :   Optional[Any]						     =  {
    'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
    'tokenization_canine': ['CanineTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase     :   Union[str, Any]						     =  [
        'CANINE_PRETRAINED_MODEL_ARCHIVE_LIST',
        'CanineForMultipleChoice',
        'CanineForQuestionAnswering',
        'CanineForSequenceClassification',
        'CanineForTokenClassification',
        'CanineLayer',
        'CanineModel',
        'CaninePreTrainedModel',
        'load_tf_weights_in_canine',
    ]


if TYPE_CHECKING:
    from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
    from .tokenization_canine import CanineTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_canine import (
            CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
            CanineForMultipleChoice,
            CanineForQuestionAnswering,
            CanineForSequenceClassification,
            CanineForTokenClassification,
            CanineLayer,
            CanineModel,
            CaninePreTrainedModel,
            load_tf_weights_in_canine,
        )


else:
    import sys

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









import argparse

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


logging.set_verbosity_info()

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     TaConfig.from_json_file(a  )
      print(f"Building PyTorch model from configuration: {config}"  )
      SCREAMING_SNAKE_CASE_				:  Tuple           =     TaForConditionalGeneration(a  )

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

      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}"  )
      model.save_pretrained(a  )


if __name__ == "__main__":
    lowerCAmelCase     :   int						     =  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.'
    )
    lowerCAmelCase     :   List[str]						     =  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






