






"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCAmelCase_     :				int         =				{
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_     :				Dict         =				["""Pix2StructImageProcessor"""]


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_     :				int         =				[
         """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """Pix2StructPreTrainedModel""",
         """Pix2StructForConditionalGeneration""",
         """Pix2StructVisionModel""",
         """Pix2StructTextModel""",
     ]

if TYPE_CHECKING:
     from .configuration_pixastruct import (
         PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         PixaStructConfig,
         PixaStructTextConfig,
         PixaStructVisionConfig,
     )
     from .processing_pixastruct import PixaStructProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_pixastruct import PixaStructImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_pixastruct import (
              PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
              PixaStructForConditionalGeneration,
              PixaStructPreTrainedModel,
              PixaStructTextModel,
              PixaStructVisionModel,
          )

else:
     import sys

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










"""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


UpperCAmelCase_     :				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       _A    (__a					,       __a					,       __a					,       __a=None							)				-> int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						str							=	XLNetConfig.from_json_file(__a							)

    SCREAMING_SNAKE_CASE_   :						List[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}'							)
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	finetuning_task
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	GLUE_TASKS_NUM_LABELS[finetuning_task]
        SCREAMING_SNAKE_CASE_   :						Dict							=	XLNetForSequenceClassification(__a							)
    elif "squad" in finetuning_task:
        SCREAMING_SNAKE_CASE_   :						str							=	finetuning_task
        SCREAMING_SNAKE_CASE_   :						Dict							=	XLNetForQuestionAnswering(__a							)
    else:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	XLNetLMHeadModel(__a							)

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

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


if __name__ == "__main__":
  UpperCAmelCase_     :				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""",
  )
  UpperCAmelCase_     :				int         =				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
  )


