






'''simple docstring'''
import os
from collections import deque

import torch
from torch.utils.data import Dataset


class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       List[str]  ,     a  :       List[Any]=""  ,     a  :       Optional[Any]="train"			):






       '''simple docstring'''






       assert os.path.isdir(a			)

       lowercase_	:    Any           =     []
       lowercase_	:    Tuple           =     os.listdir(a			)
       for story_filename in story_filenames_list:
          if "summary" in story_filename:
             continue
          lowercase_	:    List[Any]           =     os.path.join(a  ,     a			)
          if not os.path.isfile(a			):
             continue
          self.documents.append(a			)




    def __len__(			self  :       Optional[Any]			):






       '''simple docstring'''






       return len(self.documents			)





    def __getitem__(			self  :       Dict  ,     a  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     self.documents[idx]
       lowercase_	:    Optional[int]           =     document_path.split("/"			)[-1]
       with open(a  ,     encoding="utf-8"			) as source:
          lowercase_	:    Optional[int]           =     source.read()
          lowercase_    ,		lowercase_	:    Union[str, Any]           =     process_story(a			)
       return document_name, story_lines, summary_lines




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    str           =     list(filter(lambda _UpperCamelCase							: len(_UpperCamelCase							) != 0						,		[line.strip() for line in raw_story.split("\n"							)]							)							)

   # for some unknown reason some lines miss a period, add it
   lowercase_	:    Tuple           =     [_add_missing_period(_UpperCamelCase							) for line in nonempty_lines]

   # gather article lines
   lowercase_	:    str           =     []
   lowercase_	:    Optional[Any]           =     deque(_UpperCamelCase							)
   while True:
      try:
         lowercase_	:    int           =     lines.popleft()
         if element.startswith("@highlight"							):
            break
         story_lines.append(_UpperCamelCase							)
      except IndexError:
         # if "@highlight" is absent from the file we pop
         # all elements until there is None, raising an exception.
         return story_lines, []

    # gather summary lines
   lowercase_	:    List[str]           =     list(filter(lambda _UpperCamelCase							: not t.startswith("@highlight"							)						,		_UpperCamelCase							)							)

   return story_lines, summary_lines




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Dict           =     [".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
   if line.startswith("@highlight"							):
      return line
   if line[-1] in END_TOKENS:
      return line
   return line + "."




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   if len(_UpperCamelCase							) > block_size:
      return sequence[:block_size]
   else:
      sequence.extend([pad_token_id] * (block_size - len(_UpperCamelCase							))							)
      return sequence




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     torch.ones_like(_UpperCamelCase							)
   lowercase_	:    Optional[int]           =     sequence == pad_token_id
   lowercase_	:    List[str]           =     0
   return mask




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     [tokenizer.encode(_UpperCamelCase							) for line in story_lines]
   lowercase_	:    Optional[Any]           =     [token for sentence in story_lines_token_ids for token in sentence]
   lowercase_	:    int           =     [tokenizer.encode(_UpperCamelCase							) for line in summary_lines]
   lowercase_	:    str           =     [token for sentence in summary_lines_token_ids for token in sentence]

   return story_token_ids, summary_token_ids





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     []
   for sequence in batch:
      lowercase_	:    Any           =     -1
      lowercase_	:    Dict           =     []
      for s in sequence:
         if s == separator_token_id:
            sentence_num += 1
         embeddings.append(sentence_num % 2							)
      batch_embeddings.append(_UpperCamelCase							)
   return torch.tensor(_UpperCamelCase							)












'''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
 )




