




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


_snake_case            = {
    "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_,snake_case_,snake_case_,snake_case_=None  ):
      # Initialise PyTorch model
      _A							:  Union[str, Any]    =		XLNetConfig.from_json_file(snake_case_  )

      _A							:  List[str]    =		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}'''  )
            _A							:  str    =		finetuning_task
            _A							:  int    =		GLUE_TASKS_NUM_LABELS[finetuning_task]
            _A							:  int    =		XLNetForSequenceClassification(snake_case_  )
      elif "squad" in finetuning_task:
            _A							:  Optional[Any]    =		finetuning_task
            _A							:  str    =		XLNetForQuestionAnswering(snake_case_  )
      else:
            _A							:  Optional[int]    =		XLNetLMHeadModel(snake_case_  )

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

      # Save pytorch-model
      _A							:  Union[str, Any]    =		os.path.join(snake_case_,snake_case_  )
      _A							:  Any    =		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__":
  _snake_case            = 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",
  )
  _snake_case            = 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,
    is_vision_available,
)


_snake_case            = {
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = ["EfficientFormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientFormerForImageClassification",
      "EfficientFormerForImageClassificationWithTeacher",
      "EfficientFormerModel",
      "EfficientFormerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFEfficientFormerForImageClassification",
      "TFEfficientFormerForImageClassificationWithTeacher",
      "TFEfficientFormerModel",
      "TFEfficientFormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientformer import EfficientFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientformer import (
        EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientFormerForImageClassification,
        EfficientFormerForImageClassificationWithTeacher,
        EfficientFormerModel,
        EfficientFormerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_efficientformer import (
        TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEfficientFormerForImageClassification,
        TFEfficientFormerForImageClassificationWithTeacher,
        TFEfficientFormerModel,
        TFEfficientFormerPreTrainedModel,
    )

else:
  import sys

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

