






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__   :      str				=		{
    """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   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case=None			):







				"""simple docstring"""






				__magic_name__		:int   =   XLNetConfig.from_json_file(snake_case			)

				__magic_name__		:Optional[Any]   =   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}'''			)
								__magic_name__		:Union[str, Any]   =   finetuning_task
								__magic_name__		:int   =   GLUE_TASKS_NUM_LABELS[finetuning_task]
								__magic_name__		:List[Any]   =   XLNetForSequenceClassification(snake_case			)
				elif "squad" in finetuning_task:
								__magic_name__		:Tuple   =   finetuning_task
								__magic_name__		:Optional[Any]   =   XLNetForQuestionAnswering(snake_case			)
				else:
								__magic_name__		:Any   =   XLNetLMHeadModel(snake_case			)

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

				# Save pytorch-model
				__magic_name__		:Any   =   os.path.join(snake_case,	snake_case			)
				__magic_name__		:List[str]   =   os.path.join(snake_case,	snake_case			)
				print(f'''Save PyTorch model to {os.path.abspath(snake_case			)}'''			)
				torch.save(model.state_dict(),	snake_case			)
				print(f'''Save configuration file to {os.path.abspath(snake_case			)}'''			)
				with open(snake_case,	'''w''',	encoding='''utf-8'''			) as f:
								f.write(config.to_json_string()			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      List[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(
					    """--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__   :      Any				=		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_tf_available, is_torch_available


UpperCAmelCase__							  =						{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "RagModel",
         "RagPreTrainedModel",
         "RagSequenceForGeneration",
         "RagTokenForGeneration",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "TFRagModel",
         "TFRagPreTrainedModel",
         "TFRagSequenceForGeneration",
         "TFRagTokenForGeneration",
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

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


