from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCAmelCase			:							int     =logging.get_logger(__name__)

lowerCAmelCase			:							Any     ={
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       'deberta-v2'



      def __init__(					self     :				int    ,  _UpperCamelCase     :				Tuple=12_8100    ,  _UpperCamelCase     :				int=1536    ,  _UpperCamelCase     :				Union[str, Any]=24    ,  _UpperCamelCase     :				Union[str, Any]=24    ,  _UpperCamelCase     :				Optional[Any]=6144    ,  _UpperCamelCase     :				int="gelu"    ,  _UpperCamelCase     :				Any=0.1    ,  _UpperCamelCase     :				Optional[int]=0.1    ,  _UpperCamelCase     :				Dict=512    ,  _UpperCamelCase     :				Union[str, Any]=0    ,  _UpperCamelCase     :				Optional[Any]=0.0_2    ,  _UpperCamelCase     :				Optional[int]=1E-7    ,  _UpperCamelCase     :				str=False    ,  _UpperCamelCase     :				Tuple=-1    ,  _UpperCamelCase     :				Tuple=0    ,  _UpperCamelCase     :				Union[str, Any]=True    ,  _UpperCamelCase     :				Dict=None    ,  _UpperCamelCase     :				Optional[int]=0    ,  _UpperCamelCase     :				int="gelu"    ,  **_UpperCamelCase     :				int    ,  )				->Any:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						hidden_size
          _lowerCamelCase     :		str 						=						num_hidden_layers
          _lowerCamelCase     :		Dict 						=						num_attention_heads
          _lowerCamelCase     :		List[str] 						=						intermediate_size
          _lowerCamelCase     :		Tuple 						=						hidden_act
          _lowerCamelCase     :		List[str] 						=						hidden_dropout_prob
          _lowerCamelCase     :		int 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		Any 						=						max_position_embeddings
          _lowerCamelCase     :		Any 						=						type_vocab_size
          _lowerCamelCase     :		Optional[Any] 						=						initializer_range
          _lowerCamelCase     :		Union[str, Any] 						=						relative_attention
          _lowerCamelCase     :		int 						=						max_relative_positions
          _lowerCamelCase     :		str 						=						pad_token_id
          _lowerCamelCase     :		int 						=						position_biased_input

          # Backwards compatibility
          if type(_UpperCamelCase) == str:
              _lowerCamelCase     :		List[str] 						=						[x.strip() for x in pos_att_type.lower().split("""|""")]

          _lowerCamelCase     :		List[Any] 						=						pos_att_type
          _lowerCamelCase     :		Dict 						=						vocab_size
          _lowerCamelCase     :		Optional[int] 						=						layer_norm_eps

          _lowerCamelCase     :		int 						=						kwargs.get("""pooler_hidden_size"""    ,  _UpperCamelCase)
          _lowerCamelCase     :		str 						=						pooler_dropout
          _lowerCamelCase     :		Optional[Any] 						=						pooler_hidden_act





class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Mapping[str, Mapping[int, str]]:


          """simple docstring"""


          if self.task == "multiple-choice":
              _lowerCamelCase     :		Optional[int] 						=						{0: """batch""", 1: """choice""", 2: """sequence"""}
          else:
              _lowerCamelCase     :		int 						=						{0: """batch""", 1: """sequence"""}
          if self._config.type_vocab_size > 0:
              return OrderedDict(
                  [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)])
          else:
              return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)])



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->int:


          """simple docstring"""


          return 12




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]    ,  _UpperCamelCase     :				int = -1    ,  _UpperCamelCase     :				int = -1    ,  _UpperCamelCase     :				int = -1    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				Optional["TensorType"] = None    ,  _UpperCamelCase     :				int = 3    ,  _UpperCamelCase     :				int = 40    ,  _UpperCamelCase     :				int = 40    ,  _UpperCamelCase     :				"PreTrainedTokenizerBase" = None    ,  )				->Mapping[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						super().generate_dummy_inputs(preprocessor=_UpperCamelCase    ,  framework=_UpperCamelCase)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
              del dummy_inputs["token_type_ids"]
          return dummy_inputs





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


lowerCAmelCase			:							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	):







    '''simple docstring'''
    # Initialise PyTorch model
    _lowerCamelCase     :		Tuple 						=						XLNetConfig.from_json_file(__A	)

    _lowerCamelCase     :		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}"""	)
        _lowerCamelCase     :		int 						=						finetuning_task
        _lowerCamelCase     :		Union[str, Any] 						=						GLUE_TASKS_NUM_LABELS[finetuning_task]
        _lowerCamelCase     :		int 						=						XLNetForSequenceClassification(__A	)
    elif "squad" in finetuning_task:
        _lowerCamelCase     :		Dict 						=						finetuning_task
        _lowerCamelCase     :		Optional[Any] 						=						XLNetForQuestionAnswering(__A	)
    else:
        _lowerCamelCase     :		Any 						=						XLNetLMHeadModel(__A	)

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

    # Save pytorch-model
    _lowerCamelCase     :		Optional[Any] 						=						os.path.join(__A   , __A	)
    _lowerCamelCase     :		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__":
    lowerCAmelCase			:							Dict     =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",
    )
    lowerCAmelCase			:							Union[str, Any]     =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
    )




