'''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


_a   :	List[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	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=None						)					->       Optional[int]:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		XLNetConfig.from_json_file(lowerCamelCase__						)

       __UpperCAmelCase  :				Dict       				=		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  :				int       				=		finetuning_task
              __UpperCAmelCase  :				int       				=		GLUE_TASKS_NUM_LABELS[finetuning_task]
              __UpperCAmelCase  :				List[str]       				=		XLNetForSequenceClassification(lowerCamelCase__						)
       elif "squad" in finetuning_task:
              __UpperCAmelCase  :				Optional[Any]       				=		finetuning_task
              __UpperCAmelCase  :				Dict       				=		XLNetForQuestionAnswering(lowerCamelCase__						)
       else:
              __UpperCAmelCase  :				Tuple       				=		XLNetLMHeadModel(lowerCamelCase__						)

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

       # Save pytorch-model
       __UpperCAmelCase  :				int       				=		os.path.join(lowerCamelCase__							,    lowerCamelCase__						)
       __UpperCAmelCase  :				List[str]       				=		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__":
   _a   :	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",
   )
   _a   :	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'''




def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       List[str]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		(boundary[1] - boundary[0]) / steps
       __UpperCAmelCase  :				Tuple       				=		boundary[0]
       __UpperCAmelCase  :				List[str]       				=		boundary[1]
       __UpperCAmelCase  :				List[Any]       				=		make_points(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)
       __UpperCAmelCase  :				int       				=		0.0
       y += (h / 2.0) * f(lowerCamelCase__						)
       for i in x_i:
              # print(i)
              y += h * f(lowerCamelCase__						)
       y += (h / 2.0) * f(lowerCamelCase__						)
       return y

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		a + h
       while x < (b - h):
              yield x
              __UpperCAmelCase  :				List[str]       				=		x + h

def _lowercase	(      lowerCamelCase__						)					->       Optional[Any]:  # enter your function here





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		(x - 0) * (x - 0)
       return y

def _lowercase	(      )					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Tuple       				=		0.0  # Lower bound of integration
       __UpperCAmelCase  :				Union[str, Any]       				=		1.0  # Upper bound of integration
       __UpperCAmelCase  :				Union[str, Any]       				=		10.0  # define number of steps or resolution
       __UpperCAmelCase  :				Dict       				=		[a, b]  # define boundary of integration
       __UpperCAmelCase  :				Optional[int]       				=		method_a(lowerCamelCase__							,    lowerCamelCase__						)
       print(f"""y = {y}"""						)


if __name__ == "__main__":
   main()






