



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


__snake_case						:Union[str, Any]						=					{
    '''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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None    ):
    # Initialise PyTorch model
    __a							=    XLNetConfig.from_json_file(_UpperCAmelCase    )

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

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

    # Save pytorch-model
    __a							=    os.path.join(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    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__":
 __snake_case						:int						=					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''',
 )
 __snake_case						:Tuple						=					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
 )










# Lint as: python3
import itertools
import os
import re


__snake_case						:int						=					re.compile(r'''([A-Z]+)([A-Z][a-z])''')
__snake_case						:List[Any]						=					re.compile(r'''([a-z\d])([A-Z])''')

__snake_case						:Dict						=					re.compile(r'''(?<!_)_(?!_)''')
__snake_case						:Optional[int]						=					re.compile(r'''(_{2,})''')

__snake_case						:Optional[Any]						=					r'''^\w+(\.\w+)*$'''

__snake_case						:Union[str, Any]						=					r'''<>:/\|?*'''





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    _uppercase_uppercase_re.sub(R'''\1_\2'''       ,			_UpperCAmelCase    )
    __a							=    _lowercase_uppercase_re.sub(R'''\1_\2'''       ,			_UpperCAmelCase    )
    return name.lower()





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    _single_underscore_re.split(_UpperCAmelCase    )
    __a							=    [_multiple_underscores_re.split(_UpperCAmelCase    ) for n in name]
    return "".join(n.capitalize() for n in itertools.chain.from_iterable(_UpperCAmelCase    ) if n != ''''''    )





def 					__snake_case     (      _UpperCAmelCase    ):
    if os.path.basename(_UpperCAmelCase    ) != name:
        raise ValueError(f'Should be a dataset name, not a path: {name}'    )
    return camelcase_to_snakecase(_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    if os.path.basename(_UpperCAmelCase    ) != name:
        raise ValueError(f'Should be a dataset name, not a path: {name}'    )
    if not re.match(_split_re       ,			_UpperCAmelCase    ):
        raise ValueError(f'Split name should match \'{_split_re}\'\' but got \'{split}\'.'    )
    return f'{filename_prefix_for_name(_UpperCAmelCase    )}-{split}'





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None    ):
    __a							=    filename_prefix_for_split(_UpperCAmelCase       ,			_UpperCAmelCase    )
    if filetype_suffix:
        prefix += f'.{filetype_suffix}'
    __a							=    os.path.join(_UpperCAmelCase       ,			_UpperCAmelCase    )
    return f'{filepath}*'





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None    ):
    __a							=    filename_prefix_for_split(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    os.path.join(_UpperCAmelCase       ,			_UpperCAmelCase    )

    if shard_lengths:
        __a							=    len(_UpperCAmelCase    )
        __a							=    [f'{prefix}-{shard_id:05d}-of-{num_shards:05d}' for shard_id in range(_UpperCAmelCase    )]
        if filetype_suffix:
            __a							=    [filename + f'.{filetype_suffix}' for filename in filenames]
        return filenames
    else:
        __a							=    prefix
        if filetype_suffix:
            filename += f'.{filetype_suffix}'
        return [filename]





