






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase__ =			{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase__ =			[
	    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecAudioForAudioFrameClassification',
	    'Data2VecAudioForCTC',
	    'Data2VecAudioForSequenceClassification',
	    'Data2VecAudioForXVector',
	    'Data2VecAudioModel',
	    'Data2VecAudioPreTrainedModel',
	]
	UpperCamelCase__ =			[
	    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecTextForCausalLM',
	    'Data2VecTextForMaskedLM',
	    'Data2VecTextForMultipleChoice',
	    'Data2VecTextForQuestionAnswering',
	    'Data2VecTextForSequenceClassification',
	    'Data2VecTextForTokenClassification',
	    'Data2VecTextModel',
	    'Data2VecTextPreTrainedModel',
	]
	UpperCamelCase__ =			[
	    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecVisionForImageClassification',
	    'Data2VecVisionForMaskedImageModeling',
	    'Data2VecVisionForSemanticSegmentation',
	    'Data2VecVisionModel',
	    'Data2VecVisionPreTrainedModel',
	]

if is_tf_available():
	UpperCamelCase__ =			[
	    'TFData2VecVisionForImageClassification',
	    'TFData2VecVisionForSemanticSegmentation',
	    'TFData2VecVisionModel',
	    'TFData2VecVisionPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
	from .configuration_dataavec_text import (
	    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecTextConfig,
	    DataaVecTextOnnxConfig,
	)
	from .configuration_dataavec_vision import (
	    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecVisionConfig,
	    DataaVecVisionOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dataavec_audio import (
		    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecAudioForAudioFrameClassification,
		    DataaVecAudioForCTC,
		    DataaVecAudioForSequenceClassification,
		    DataaVecAudioForXVector,
		    DataaVecAudioModel,
		    DataaVecAudioPreTrainedModel,
		)
		from .modeling_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecTextForCausalLM,
		    DataaVecTextForMaskedLM,
		    DataaVecTextForMultipleChoice,
		    DataaVecTextForQuestionAnswering,
		    DataaVecTextForSequenceClassification,
		    DataaVecTextForTokenClassification,
		    DataaVecTextModel,
		    DataaVecTextPreTrainedModel,
		)
		from .modeling_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForMaskedImageModeling,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		    DataaVecVisionPreTrainedModel,
		)
	if is_tf_available():
		from .modeling_tf_dataavec_vision import (
		    TFDataaVecVisionForImageClassification,
		    TFDataaVecVisionForSemanticSegmentation,
		    TFDataaVecVisionModel,
		    TFDataaVecVisionPreTrainedModel,
		)

else:
	import sys

	UpperCamelCase__ =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












'''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__ =			{
    '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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None							):


   """simple docstring"""




   lowercase_	:    Tuple           =     XLNetConfig.from_json_file(_UpperCamelCase							)

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

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

   # Save pytorch-model
   lowercase_	:    Optional[Any]           =     os.path.join(_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    Dict           =     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__ =			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__ =			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
 )




