



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_		:     List[str]        =     {
    '''configuration_table_transformer''': [
        '''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TableTransformerConfig''',
        '''TableTransformerOnnxConfig''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_		:     Any        =     [
         '''TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TableTransformerForObjectDetection''',
         '''TableTransformerModel''',
         '''TableTransformerPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_table_transformer import (
         TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         TableTransformerConfig,
         TableTransformerOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_table_transformer import (
              TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TableTransformerForObjectDetection,
              TableTransformerModel,
              TableTransformerPreTrainedModel,
          )

else:
     import sys

     A_		:     Tuple        =     _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 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		TaConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		TaForConditionalGeneration(UpperCAmelCase__					)

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

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


if __name__ == "__main__":
     A_		:     Union[str, Any]        =     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.'''
     )
     A_		:     Tuple        =     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

