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


a_							=      {
    """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      (						A_   : List[Any]						,	A_   : str						,	A_   : Union[str, Any]						,	A_   : str=None					)    ->  List[Any]:
  # Initialise PyTorch model
  __UpperCAmelCase						    =       XLNetConfig.from_json_file(__UpperCamelCase					)

  __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(__UpperCamelCase					)
  elif "squad" in finetuning_task:
    __UpperCAmelCase						    =       finetuning_task
    __UpperCAmelCase						    =       XLNetForQuestionAnswering(__UpperCamelCase					)
  else:
    __UpperCAmelCase						    =       XLNetLMHeadModel(__UpperCamelCase					)

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

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


if __name__ == "__main__":
   a_							=      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""",
   )
   a_							=      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_tokenizers_available, is_torch_available


a_							=      {
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """AltCLIPPreTrainedModel""",
           """AltCLIPModel""",
           """AltCLIPTextModel""",
           """AltCLIPVisionModel""",
       ]


if TYPE_CHECKING:
       from .configuration_altclip import (
           ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           AltCLIPConfig,
           AltCLIPTextConfig,
           AltCLIPVisionConfig,
       )
       from .processing_altclip import AltCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_altclip import (
                  ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  AltCLIPModel,
                  AltCLIPPreTrainedModel,
                  AltCLIPTextModel,
                  AltCLIPVisionModel,
              )


else:
       import sys

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



