
"""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	:				Dict  	= {
    """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						(				snake_case      :str				,					snake_case      :Dict				,					snake_case      :List[str]				,					snake_case      :str=None		)      ->	Any:
 __UpperCamelCase				  =       XLNetConfig.from_json_file(lowerCAmelCase__		)

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

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

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


if __name__ == "__main__":
      UpperCamelCase	:				Union[str, 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",
      )
      UpperCamelCase	:				List[str]  	= 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
      )




"""simple docstring"""






from __future__ import annotations

import numpy as np


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[float]		) ->    Optional[Any]:
      '''simple docstring'''


      return np.maximum(0					,		lowerCAmelCase__		)


if __name__ == "__main__":
       print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



