'''simple docstring'''





import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


SCREAMING_SNAKE_CASE_:     Optional[int]  ={
    'cola': 2,
    'mnli': 3,
    'mrpc': 2,
    'sst-2': 2,
    'sts-b': 1,
    'qqp': 2,
    'qnli': 2,
    'rte': 2,
    'wnli': 2,
}


logging.set_verbosity_info()





def 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Optional[int]					,    snake_case_       : List[str]					,    snake_case_       : Optional[int]=None			)    ->      Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   XLNetConfig.from_json_file(_SCREAMING_SNAKE_CASE			)

     UpperCAmelCase_           =   finetuning_task.lower() if finetuning_task is not None else ""
     if finetuning_task in GLUE_TASKS_NUM_LABELS:
          print(f"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""			)
          UpperCAmelCase_           =   finetuning_task
          UpperCAmelCase_           =   GLUE_TASKS_NUM_LABELS[finetuning_task]
          UpperCAmelCase_           =   XLNetForSequenceClassification(_SCREAMING_SNAKE_CASE			)
     elif "squad" in finetuning_task:
          UpperCAmelCase_           =   finetuning_task
          UpperCAmelCase_           =   XLNetForQuestionAnswering(_SCREAMING_SNAKE_CASE			)
     else:
          UpperCAmelCase_           =   XLNetLMHeadModel(_SCREAMING_SNAKE_CASE			)

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

     # Save pytorch-model
     UpperCAmelCase_           =   os.path.join(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE			)
     UpperCAmelCase_           =   os.path.join(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE			)
     print(f"""Save PyTorch model to {os.path.abspath(_SCREAMING_SNAKE_CASE			)}"""			)
     torch.save(model.state_dict()					,    _SCREAMING_SNAKE_CASE			)
     print(f"""Save configuration file to {os.path.abspath(_SCREAMING_SNAKE_CASE			)}"""			)
     with open(_SCREAMING_SNAKE_CASE					,    "w"					,    encoding="utf-8"			) as f:
          f.write(config.to_json_string()			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     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(
        '--xlnet_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained XLNet model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default=None,
        type=str,
        required=True,
        help='Path to the folder to store the PyTorch model or dataset/vocab.',
    )
    parser.add_argument(
        '--finetuning_task',
        default=None,
        type=str,
        help='Name of a task on which the XLNet TensorFlow model was fine-tuned',
    )
    SCREAMING_SNAKE_CASE_:     Dict  =parser.parse_args()
    print(args)

    convert_xlnet_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
    )










from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   {'configuration_mmbt': ['MMBTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__       :      List[Any]  =   ['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
       from .configuration_mmbt import MMBTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
       import sys

       SCREAMING_SNAKE_CASE__       :      int  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






