





"""simple docstring"""





from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__A											=						TypeVar("""KT""")
__A											=						TypeVar("""VT""")






class    _lowerCAmelCase  (	Generic[KT, VT]			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase = "root"       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				key
         lowerCAmelCase__     :List[Any]											=				value
         lowerCAmelCase__     :list[Node[KT, VT]]											=				[]






       def __repr__(       self ):



         '''simple docstring'''

         return F"Node({self.key}: {self.value})"






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return len(self.forward )






class    _lowerCAmelCase  (	Generic[KT, VT]			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase = 0.5       ,					__UpperCAmelCase = 1_6 ):



         '''simple docstring'''

         lowerCAmelCase__     :Node[KT, VT]											=				Node[KT, VT]()
         lowerCAmelCase__     :Optional[Any]											=				0
         lowerCAmelCase__     :Tuple											=				p
         lowerCAmelCase__     :List[Any]											=				max_level






       def __str__(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				list(self )

         if len(__UpperCAmelCase ) == 0:
           return F"SkipList(level={self.level})"

         lowerCAmelCase__     :Union[str, Any]											=				max((len(str(__UpperCAmelCase ) ) for item in items)       ,					default=4 )
         lowerCAmelCase__     :Any											=				max(__UpperCAmelCase       ,					4 ) + 4

         lowerCAmelCase__     :Tuple											=				self.head
         lowerCAmelCase__     :Any											=				[]

         lowerCAmelCase__     :List[Any]											=				node.forward.copy()
         lines.append(F"[{node.key}]".ljust(__UpperCAmelCase       ,					'-' ) + '* ' * len(__UpperCAmelCase ) )
         lines.append(' ' * label_size + '| ' * len(__UpperCAmelCase ) )

         while len(node.forward ) != 0:
           lowerCAmelCase__     :Dict											=				node.forward[0]

           lines.append(
               F"[{node.key}]".ljust(__UpperCAmelCase       ,					'-' )
               + ' '.join(str(n.key ) if n.key == node.key else '|' for n in forwards ) )
           lines.append(' ' * label_size + '| ' * len(__UpperCAmelCase ) )
           lowerCAmelCase__     :Union[str, Any]											=				node.forward

         lines.append('None'.ljust(__UpperCAmelCase ) + '* ' * len(__UpperCAmelCase ) )
         return F"SkipList(level={self.level})\n" + "\n".join(__UpperCAmelCase )






       def __iter__(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				self.head

         while len(node.forward ) != 0:
           yield node.forward[0].key
           lowerCAmelCase__     :str											=				node.forward[0]






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				1
         while random() < self.p and level < self.max_level:
           level += 1

         return level






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				[]

         lowerCAmelCase__     :Tuple											=				self.head

         for i in reversed(range(self.level ) ):
           # i < node.level - When node level is lesser than `i` decrement `i`.
           # node.forward[i].key < key - Jumping to node with key value higher
           #                             or equal to searched key would result
           #                             in skipping searched key.
           while i < node.level and node.forward[i].key < key:
             lowerCAmelCase__     :Any											=				node.forward[i]
           # Each leftmost node (relative to searched node) will potentially have to
           # be updated.
           update_vector.append(__UpperCAmelCase )

         update_vector.reverse()  # Note that we were inserting values in reverse order.

         # len(node.forward) != 0 - If current node doesn't contain any further
         #                          references then searched key is not present.
         # node.forward[0].key == key - Next node key should be equal to search key
         #                              if key is present.
         if len(node.forward ) != 0 and node.forward[0].key == key:
           return node.forward[0], update_vector
         else:
           return None, update_vector






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Tuple											=				self._locate_node(__UpperCAmelCase )

         if node is not None:
           for i, update_node in enumerate(__UpperCAmelCase ):
             # Remove or replace all references to removed node.
             if update_node.level > i and update_node.forward[i].key == key:
               if node.level > i:
                 lowerCAmelCase__     :Optional[Any]											=				node.forward[i]
               else:
                 lowerCAmelCase__     :str											=				update_node.forward[:i]






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :str											=				self._locate_node(__UpperCAmelCase )
         if node is not None:
           lowerCAmelCase__     :Tuple											=				value
         else:
           lowerCAmelCase__     :Optional[Any]											=				self.random_level()

           if level > self.level:
             # After level increase we have to add additional nodes to head.
             for _ in range(self.level - 1       ,					__UpperCAmelCase ):
               update_vector.append(self.head )
             lowerCAmelCase__     :int											=				level

           lowerCAmelCase__     :int											=				Node(__UpperCAmelCase       ,					__UpperCAmelCase )

           for i, update_node in enumerate(update_vector[:level] ):
             # Change references to pass through new node.
             if update_node.level > i:
               new_node.forward.append(update_node.forward[i] )

             if update_node.level < i + 1:
               update_node.forward.append(__UpperCAmelCase )
             else:
               lowerCAmelCase__     :Dict											=				new_node






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				self._locate_node(__UpperCAmelCase )

         if node is not None:
           return node.value

         return None






def 					__A  ()   ->str:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				SkipList()
  skip_list.insert('Key1'	,  3     )
  skip_list.insert('Key2'	,  12     )
  skip_list.insert('Key3'	,  41     )
  skip_list.insert('Key4'	,  -19     )

  lowerCAmelCase__     :List[str]											=				skip_list.head
  lowerCAmelCase__     :Any											=				{}
  while node.level != 0:
    lowerCAmelCase__     :Optional[Any]											=				node.forward[0]
    lowerCAmelCase__     :Optional[int]											=				node.value

  assert len(_SCREAMING_SNAKE_CASE     ) == 4
  assert all_values["Key1"] == 3
  assert all_values["Key2"] == 12
  assert all_values["Key3"] == 41
  assert all_values["Key4"] == -19
def 					__A  ()   ->Dict:





  """simple docstring"""


  lowerCAmelCase__     :str											=				SkipList()
  skip_list.insert('Key1'	,  10     )
  skip_list.insert('Key1'	,  12     )

  skip_list.insert('Key5'	,  7     )
  skip_list.insert('Key7'	,  10     )
  skip_list.insert('Key10'	,  5     )

  skip_list.insert('Key7'	,  7     )
  skip_list.insert('Key5'	,  5     )
  skip_list.insert('Key10'	,  10     )

  lowerCAmelCase__     :str											=				skip_list.head
  lowerCAmelCase__     :List[Any]											=				{}
  while node.level != 0:
    lowerCAmelCase__     :int											=				node.forward[0]
    lowerCAmelCase__     :str											=				node.value

  if len(_SCREAMING_SNAKE_CASE     ) != 4:
    print()
  assert len(_SCREAMING_SNAKE_CASE     ) == 4
  assert all_values["Key1"] == 12
  assert all_values["Key7"] == 7
  assert all_values["Key5"] == 5
  assert all_values["Key10"] == 10
def 					__A  ()   ->int:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				SkipList()
  assert skip_list.find('Some key'     ) is None
def 					__A  ()   ->Any:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				SkipList()

  skip_list.insert('Key2'	,  20     )
  assert skip_list.find('Key2'     ) == 20

  skip_list.insert('Some Key'	,  10     )
  skip_list.insert('Key2'	,  8     )
  skip_list.insert('V'	,  13     )

  assert skip_list.find('Y'     ) is None
  assert skip_list.find('Key2'     ) == 8
  assert skip_list.find('Some Key'     ) == 10
  assert skip_list.find('V'     ) == 13
def 					__A  ()   ->Tuple:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				SkipList()
  skip_list.delete('Some key'     )

  assert len(skip_list.head.forward     ) == 0
def 					__A  ()   ->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				SkipList()

  skip_list.insert('Key1'	,  12     )
  skip_list.insert('V'	,  13     )
  skip_list.insert('X'	,  14     )
  skip_list.insert('Key2'	,  15     )

  skip_list.delete('V'     )
  skip_list.delete('Key2'     )

  assert skip_list.find('V'     ) is None
  assert skip_list.find('Key2'     ) is None
def 					__A  ()   ->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				SkipList()

  skip_list.insert('Key1'	,  12     )
  skip_list.insert('V'	,  13     )
  skip_list.insert('X'	,  14     )
  skip_list.insert('Key2'	,  15     )

  skip_list.delete('V'     )
  assert skip_list.find('V'     ) is None
  assert skip_list.find('X'     ) == 14
  assert skip_list.find('Key1'     ) == 12
  assert skip_list.find('Key2'     ) == 15

  skip_list.delete('X'     )
  assert skip_list.find('V'     ) is None
  assert skip_list.find('X'     ) is None
  assert skip_list.find('Key1'     ) == 12
  assert skip_list.find('Key2'     ) == 15

  skip_list.delete('Key1'     )
  assert skip_list.find('V'     ) is None
  assert skip_list.find('X'     ) is None
  assert skip_list.find('Key1'     ) is None
  assert skip_list.find('Key2'     ) == 15

  skip_list.delete('Key2'     )
  assert skip_list.find('V'     ) is None
  assert skip_list.find('X'     ) is None
  assert skip_list.find('Key1'     ) is None
  assert skip_list.find('Key2'     ) is None
def 					__A  ()   ->int:





  """simple docstring"""


  lowerCAmelCase__     :str											=				SkipList()

  skip_list.insert('Key1'	,  12     )
  skip_list.insert('V'	,  13     )
  skip_list.insert('X'	,  142     )
  skip_list.insert('Key2'	,  15     )

  skip_list.delete('X'     )

  def traverse_keys(_SCREAMING_SNAKE_CASE     ):
    yield node.key
    for forward_node in node.forward:
      yield from traverse_keys(_SCREAMING_SNAKE_CASE     )

  assert len(set(traverse_keys(skip_list.head     )     )     ) == 4
def 					__A  ()   ->Optional[int]:





  """simple docstring"""


  def is_sorted(_SCREAMING_SNAKE_CASE     ):
    return all(next_item >= item for item, next_item in zip(_SCREAMING_SNAKE_CASE	,  lst[1:]     )     )

  lowerCAmelCase__     :Optional[Any]											=				SkipList()
  for i in range(10     ):
    skip_list.insert(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  assert is_sorted(list(_SCREAMING_SNAKE_CASE     )     )
  skip_list.delete(5     )
  skip_list.delete(8     )
  skip_list.delete(2     )
  assert is_sorted(list(_SCREAMING_SNAKE_CASE     )     )
  skip_list.insert(-12	,  -12     )
  skip_list.insert(77	,  77     )
  assert is_sorted(list(_SCREAMING_SNAKE_CASE     )     )
def 					__A  ()   ->Any:





  """simple docstring"""


  for _ in range(100     ):
    # Repeat test 100 times due to the probabilistic nature of skip list
    # random values == random bugs
    test_insert()
    test_insert_overrides_existing_value()

    test_searching_empty_list_returns_none()
    test_search()

    test_deleting_item_from_empty_list_do_nothing()
    test_deleted_items_are_not_founded_by_find_method()
    test_delete_removes_only_given_key()
    test_delete_doesnt_leave_dead_nodes()

    test_iter_always_yields_sorted_values()
def 					__A  ()   ->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				SkipList()
  skip_list.insert(2	,  '2'     )
  skip_list.insert(4	,  '4'     )
  skip_list.insert(6	,  '4'     )
  skip_list.insert(4	,  '5'     )
  skip_list.insert(8	,  '4'     )
  skip_list.insert(9	,  '4'     )

  skip_list.delete(4     )

  print(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()







"""simple docstring"""





import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__A											=						logging.getLogger(__name__)


__A											=						list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__A											=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class    _lowerCAmelCase  :
       """simple docstring"""




       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={
               """help""": (
                   """The model checkpoint for weights initialization. Leave None if you want to train a model from"""
                   """ scratch."""
               )
           }    ,  )
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a			)}    ,  )
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,  )






@dataclass
class    _lowerCAmelCase  :
       """simple docstring"""




       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """The input training data file (a text file)."""}			)
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={
               """help""": (
                   """The input training data files (multiple files in glob format). """
                   """Very often splitting large files to smaller files can prevent tokenizer going out of memory"""
               )
           }    ,  )
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}    ,  )
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """An optional input train ref data file for whole word mask in Chinese."""}    ,  )
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """An optional input eval ref data file for whole word mask in Chinese."""}    ,  )
       __magic_name__	:bool						    =   field(
           default=a    ,  metadata={"""help""": """Whether distinct lines of text in the dataset are to be handled as distinct sequences."""}    ,  )

       __magic_name__	:bool						    =   field(
           default=a    ,  metadata={"""help""": """Train with masked-language modeling loss instead of language modeling."""}			)
       __magic_name__	:bool						    =   field(default=a    ,  metadata={"""help""": """Whether ot not to use whole word mask."""}			)
       __magic_name__	:float						    =   field(
           default=0.15    ,  metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}			)
       __magic_name__	:float						    =   field(
           default=1 / 6    ,  metadata={
               """help""": (
                   """Ratio of length of a span of masked tokens to surrounding context length for permutation language"""
                   """ modeling."""
               )
           }    ,  )
       __magic_name__	:int						    =   field(
           default=5    ,  metadata={"""help""": """Maximum length of a span of masked tokens for permutation language modeling."""}			)

       __magic_name__	:int						    =   field(
           default=-1    ,  metadata={
               """help""": (
                   """Optional input sequence length after tokenization."""
                   """The training dataset will be truncated in block of this size for training."""
                   """Default to the model max input length for single sentence inputs (take into account special tokens)."""
               )
           }    ,  )
       __magic_name__	:bool						    =   field(
           default=a    ,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)






def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = False	,  _SCREAMING_SNAKE_CASE = None	,  )   ->Optional[int]:





  """simple docstring"""


  def _dataset(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=None     ):
    if args.line_by_line:
      if ref_path is not None:
        if not args.whole_word_mask or not args.mlm:
          raise ValueError('You need to set world whole masking and mlm to True for Chinese Whole Word Mask'     )
        return LineByLineWithRefDataset(
            tokenizer=_SCREAMING_SNAKE_CASE	,  file_path=_SCREAMING_SNAKE_CASE	,  block_size=args.block_size	,  ref_path=_SCREAMING_SNAKE_CASE	,  )

      return LineByLineTextDataset(tokenizer=_SCREAMING_SNAKE_CASE	,  file_path=_SCREAMING_SNAKE_CASE	,  block_size=args.block_size     )
    else:
      return TextDataset(
          tokenizer=_SCREAMING_SNAKE_CASE	,  file_path=_SCREAMING_SNAKE_CASE	,  block_size=args.block_size	,  overwrite_cache=args.overwrite_cache	,  cache_dir=_SCREAMING_SNAKE_CASE	,  )

  if evaluate:
    return _dataset(args.eval_data_file	,  args.eval_ref_file     )
  elif args.train_data_files:
    return ConcatDataset([_dataset(_SCREAMING_SNAKE_CASE     ) for f in glob(args.train_data_files     )]     )
  else:
    return _dataset(args.train_data_file	,  args.train_ref_file     )
def 					__A  ()   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
  lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :int											=				parser.parse_args_into_dataclasses()

  if data_args.eval_data_file is None and training_args.do_eval:
    raise ValueError(
        'Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '
        'or remove the --do_eval argument.'     )
  if (
      os.path.exists(training_args.output_dir     )
      and os.listdir(training_args.output_dir     )
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
        ' --overwrite_output_dir to overcome.'     )

  # Setup logging
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'	,  datefmt='%m/%d/%Y %H:%M:%S'	,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,  )
  logger.warning(
      'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'	,  training_args.local_rank	,  training_args.device	,  training_args.n_gpu	,  bool(training_args.local_rank != -1     )	,  training_args.fpaa	,  )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank     ):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info('Training/evaluation parameters %s'	,  _SCREAMING_SNAKE_CASE     )

  # Set seed
  set_seed(training_args.seed     )

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  if model_args.config_name:
    lowerCAmelCase__     :Optional[Any]											=				AutoConfig.from_pretrained(model_args.config_name	,  cache_dir=model_args.cache_dir     )
  elif model_args.model_name_or_path:
    lowerCAmelCase__     :List[str]											=				AutoConfig.from_pretrained(model_args.model_name_or_path	,  cache_dir=model_args.cache_dir     )
  else:
    lowerCAmelCase__     :List[Any]											=				CONFIG_MAPPING[model_args.model_type]()
    logger.warning('You are instantiating a new config instance from scratch.'     )

  if model_args.tokenizer_name:
    lowerCAmelCase__     :Union[str, Any]											=				AutoTokenizer.from_pretrained(model_args.tokenizer_name	,  cache_dir=model_args.cache_dir     )
  elif model_args.model_name_or_path:
    lowerCAmelCase__     :str											=				AutoTokenizer.from_pretrained(model_args.model_name_or_path	,  cache_dir=model_args.cache_dir     )
  else:
    raise ValueError(
        'You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'
        ' script, save it,and load it from here, using --tokenizer_name'     )

  if model_args.model_name_or_path:
    lowerCAmelCase__     :Optional[Any]											=				AutoModelWithLMHead.from_pretrained(
        model_args.model_name_or_path	,  from_tf=bool('.ckpt' in model_args.model_name_or_path     )	,  config=_SCREAMING_SNAKE_CASE	,  cache_dir=model_args.cache_dir	,  )
  else:
    logger.info('Training new model from scratch'     )
    lowerCAmelCase__     :int											=				AutoModelWithLMHead.from_config(_SCREAMING_SNAKE_CASE     )

  model.resize_token_embeddings(len(_SCREAMING_SNAKE_CASE     )     )

  if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
    raise ValueError(
        'BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'
        '--mlm flag (masked language modeling).'     )

  if data_args.block_size <= 0:
    lowerCAmelCase__     :Dict											=				tokenizer.max_len
    # Our input block size will be the max possible for the model
  else:
    lowerCAmelCase__     :Any											=				min(data_args.block_size	,  tokenizer.max_len     )

  # Get datasets

  lowerCAmelCase__     :List[str]											=				(
      get_dataset(_SCREAMING_SNAKE_CASE	,  tokenizer=_SCREAMING_SNAKE_CASE	,  cache_dir=model_args.cache_dir     ) if training_args.do_train else None
  )
  lowerCAmelCase__     :Optional[int]											=				(
      get_dataset(_SCREAMING_SNAKE_CASE	,  tokenizer=_SCREAMING_SNAKE_CASE	,  evaluate=_SCREAMING_SNAKE_CASE	,  cache_dir=model_args.cache_dir     )
      if training_args.do_eval
      else None
  )
  if config.model_type == "xlnet":
    lowerCAmelCase__     :str											=				DataCollatorForPermutationLanguageModeling(
        tokenizer=_SCREAMING_SNAKE_CASE	,  plm_probability=data_args.plm_probability	,  max_span_length=data_args.max_span_length	,  )
  else:
    if data_args.mlm and data_args.whole_word_mask:
      lowerCAmelCase__     :Optional[Any]											=				DataCollatorForWholeWordMask(
          tokenizer=_SCREAMING_SNAKE_CASE	,  mlm_probability=data_args.mlm_probability     )
    else:
      lowerCAmelCase__     :str											=				DataCollatorForLanguageModeling(
          tokenizer=_SCREAMING_SNAKE_CASE	,  mlm=data_args.mlm	,  mlm_probability=data_args.mlm_probability     )

    # Initialize our Trainer
  lowerCAmelCase__     :Tuple											=				Trainer(
      model=_SCREAMING_SNAKE_CASE	,  args=_SCREAMING_SNAKE_CASE	,  data_collator=_SCREAMING_SNAKE_CASE	,  train_dataset=_SCREAMING_SNAKE_CASE	,  eval_dataset=_SCREAMING_SNAKE_CASE	,  prediction_loss_only=_SCREAMING_SNAKE_CASE	,  )

  # Training
  if training_args.do_train:
    lowerCAmelCase__     :Tuple											=				(
        model_args.model_name_or_path
        if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path     )
        else None
    )
    trainer.train(model_path=_SCREAMING_SNAKE_CASE     )
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir     )

    # Evaluation
  lowerCAmelCase__     :Optional[Any]											=				{}
  if training_args.do_eval:
    logger.info('*** Evaluate ***'     )

    lowerCAmelCase__     :Any											=				trainer.evaluate()

    lowerCAmelCase__     :Optional[Any]											=				math.exp(eval_output['eval_loss']     )
    lowerCAmelCase__     :Dict											=				{'perplexity': perplexity}

    lowerCAmelCase__     :List[Any]											=				os.path.join(training_args.output_dir	,  'eval_results_lm.txt'     )
    if trainer.is_world_master():
      with open(_SCREAMING_SNAKE_CASE	,  'w'     ) as writer:
        logger.info('***** Eval results *****'     )
        for key in sorted(result.keys()     ):
          logger.info('  %s = %s'	,  _SCREAMING_SNAKE_CASE	,  str(result[key]     )     )
          writer.write('%s = %s\n' % (key, str(result[key]     ))     )

    results.update(_SCREAMING_SNAKE_CASE     )

  return results
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Optional[int]:





  """simple docstring"""


  main()


if __name__ == "__main__":
  main()
