


'''simple docstring'''







from typing import TYPE_CHECKING

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


lowerCAmelCase     :  Optional[int]											=    {
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Any											=    [
           """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """XLMForMultipleChoice""",
           """XLMForQuestionAnswering""",
           """XLMForQuestionAnsweringSimple""",
           """XLMForSequenceClassification""",
           """XLMForTokenClassification""",
           """XLMModel""",
           """XLMPreTrainedModel""",
           """XLMWithLMHeadModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Dict											=    [
           """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFXLMForMultipleChoice""",
           """TFXLMForQuestionAnsweringSimple""",
           """TFXLMForSequenceClassification""",
           """TFXLMForTokenClassification""",
           """TFXLMMainLayer""",
           """TFXLMModel""",
           """TFXLMPreTrainedModel""",
           """TFXLMWithLMHeadModel""",
       ]


if TYPE_CHECKING:
       from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
       from .tokenization_xlm import XLMTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlm import (
                  XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMForMultipleChoice,
                  XLMForQuestionAnswering,
                  XLMForQuestionAnsweringSimple,
                  XLMForSequenceClassification,
                  XLMForTokenClassification,
                  XLMModel,
                  XLMPreTrainedModel,
                  XLMWithLMHeadModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlm import (
                  TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLMForMultipleChoice,
                  TFXLMForQuestionAnsweringSimple,
                  TFXLMForSequenceClassification,
                  TFXLMForTokenClassification,
                  TFXLMMainLayer,
                  TFXLMModel,
                  TFXLMPreTrainedModel,
                  TFXLMWithLMHeadModel,
              )

else:
       import sys

       lowerCAmelCase     :  List[Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





'''simple docstring'''







import argparse

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


logging.set_verbosity_info()






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							TaConfig.from_json_file(_A       )
       print(f'Building PyTorch model from configuration: {config}'       )
       _lowerCAmelCase     :				Union[str, Any]       =							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     :  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."""
       )
       lowerCAmelCase     :  int											=    parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

