

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	:     Tuple		       = {
    """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			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=None   ):







       '''simple docstring'''






       snake_case_           =					XLNetConfig.from_json_file(UpperCamelCase__   )

       snake_case_           =					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}'''   )
              snake_case_           =					finetuning_task
              snake_case_           =					GLUE_TASKS_NUM_LABELS[finetuning_task]
              snake_case_           =					XLNetForSequenceClassification(UpperCamelCase__   )
       elif "squad" in finetuning_task:
              snake_case_           =					finetuning_task
              snake_case_           =					XLNetForQuestionAnswering(UpperCamelCase__   )
       else:
              snake_case_           =					XLNetLMHeadModel(UpperCamelCase__   )

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

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


if __name__ == "__main__":
     _UpperCAmelCase	:     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""",
     )
     _UpperCAmelCase	:     Optional[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
     )







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)




