

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


_SCREAMING_SNAKE_CASE  :						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        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A=None       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					XLNetConfig.from_json_file(_A       )

     SCREAMING_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}'''       )
          SCREAMING_SNAKE_CASE__					=					finetuning_task
          SCREAMING_SNAKE_CASE__					=					GLUE_TASKS_NUM_LABELS[finetuning_task]
          SCREAMING_SNAKE_CASE__					=					XLNetForSequenceClassification(_A       )
     elif "squad" in finetuning_task:
          SCREAMING_SNAKE_CASE__					=					finetuning_task
          SCREAMING_SNAKE_CASE__					=					XLNetForQuestionAnswering(_A       )
     else:
          SCREAMING_SNAKE_CASE__					=					XLNetLMHeadModel(_A       )

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

     # Save pytorch-model
     SCREAMING_SNAKE_CASE__					=					os.path.join(_A   ,	_A       )
     SCREAMING_SNAKE_CASE__					=					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__":
       _SCREAMING_SNAKE_CASE  :						Optional[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''',
       )
       _SCREAMING_SNAKE_CASE  :						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
       )





from __future__ import annotations

import collections
import pprint
from pathlib import Path
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return "".join(sorted(_A       )       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return word_by_signature[signature(_A       )]


_SCREAMING_SNAKE_CASE  :						str         =    Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_SCREAMING_SNAKE_CASE  :						str         =    sorted({word.strip().lower() for word in data.splitlines()})

_SCREAMING_SNAKE_CASE  :						List[Any]         =    collections.defaultdict(list)
for word in word_list:
       word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Tuple         =    {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

       with open('''anagrams.txt''', '''w''') as file:
              file.write('''all_anagrams = \n ''')
              file.write(pprint.pformat(all_anagrams))


