




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__       :      List[str]  =   {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__       :      Dict  =   [
           'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'IBertForMaskedLM',
           'IBertForMultipleChoice',
           'IBertForQuestionAnswering',
           'IBertForSequenceClassification',
           'IBertForTokenClassification',
           'IBertModel',
           'IBertPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_ibert import (
                  IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  IBertForMaskedLM,
                  IBertForMultipleChoice,
                  IBertForQuestionAnswering,
                  IBertForSequenceClassification,
                  IBertForTokenClassification,
                  IBertModel,
                  IBertPreTrainedModel,
              )

else:
       import sys

       SCREAMING_SNAKE_CASE__       :      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      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       # Initialise PyTorch model
       lowerCamelCase   :      Any			 =					TaConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
       print(f'''Building PyTorch model from configuration: {config}'''    )
       lowerCamelCase   :      str			 =					TaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

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

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      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(
           '--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.'
       )
       SCREAMING_SNAKE_CASE__       :      str  =   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






