


'''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


__UpperCAmelCase    :int				       =     logging.getLogger(__name__)


__UpperCAmelCase    :str				       =     list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__UpperCAmelCase    :Tuple				       =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 			a       :


     """simple docstring"""






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



@dataclass
class 			a       :


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={"help": "The input training data file (a text file)."} )
     SCREAMING_SNAKE_CASE :				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"
             )
         }			,						)
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}			,						)
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={"help": "An optional input train ref data file for whole word mask in Chinese."}			,						)
     SCREAMING_SNAKE_CASE :				Optional[str]							     =     field(
         default=_a			,						metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."}			,						)
     SCREAMING_SNAKE_CASE :				bool							     =     field(
         default=_a			,						metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}			,						)

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

     SCREAMING_SNAKE_CASE :				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)."
             )
         }			,						)
     SCREAMING_SNAKE_CASE :				bool							     =     field(
         default=_a			,						metadata={"help": "Overwrite the cached training and evaluation sets"} )




def 			_a			(		_lowercase      :			DataTrainingArguments    ,      _lowercase      :			PreTrainedTokenizer    ,      _lowercase      :			bool = False    ,      _lowercase      :			Optional[str] = None    ,      ):





     '''simple docstring'''






     def _dataset(_lowercase      :			List[str]    ,      _lowercase      :			List[str]=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=_lowercase    ,      file_path=_lowercase    ,      block_size=args.block_size    ,      ref_path=_lowercase    ,      )

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

     if evaluate:
          return _dataset(args.eval_data_file    ,      args.eval_ref_file						)
     elif args.train_data_files:
          return ConcatDataset([_dataset(_lowercase						) for f in glob(args.train_data_files						)]						)
     else:
          return _dataset(args.train_data_file    ,      args.train_ref_file						)




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[int]													=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    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'''    ,      _lowercase						)

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

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

     model.resize_token_embeddings(len(_lowercase						)						)

     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:
          __UpperCAmelCase			:				Optional[int]													=    tokenizer.max_len
          # Our input block size will be the max possible for the model
     else:
          __UpperCAmelCase			:				Optional[int]													=    min(data_args.block_size    ,      tokenizer.max_len						)

     # Get datasets

     __UpperCAmelCase			:				Optional[Any]													=    (
         get_dataset(_lowercase    ,      tokenizer=_lowercase    ,      cache_dir=model_args.cache_dir						) if training_args.do_train else None
     )
     __UpperCAmelCase			:				Any													=    (
         get_dataset(_lowercase    ,      tokenizer=_lowercase    ,      evaluate=_lowercase    ,      cache_dir=model_args.cache_dir						)
         if training_args.do_eval
         else None
     )
     if config.model_type == "xlnet":
          __UpperCAmelCase			:				Union[str, Any]													=    DataCollatorForPermutationLanguageModeling(
              tokenizer=_lowercase    ,      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:
               __UpperCAmelCase			:				Optional[int]													=    DataCollatorForWholeWordMask(
                   tokenizer=_lowercase    ,      mlm_probability=data_args.mlm_probability						)
          else:
               __UpperCAmelCase			:				Any													=    DataCollatorForLanguageModeling(
                   tokenizer=_lowercase    ,      mlm=data_args.mlm    ,      mlm_probability=data_args.mlm_probability						)

    # Initialize our Trainer
     __UpperCAmelCase			:				List[str]													=    Trainer(
         model=_lowercase    ,      args=_lowercase    ,      data_collator=_lowercase    ,      train_dataset=_lowercase    ,      eval_dataset=_lowercase    ,      prediction_loss_only=_lowercase    ,      )

     # Training
     if training_args.do_train:
          __UpperCAmelCase			:				str													=    (
              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=_lowercase						)
          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
     __UpperCAmelCase			:				Union[str, Any]													=    {}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''						)

          __UpperCAmelCase			:				List[str]													=    trainer.evaluate()

          __UpperCAmelCase			:				List[str]													=    math.exp(eval_output['''eval_loss''']						)
          __UpperCAmelCase			:				str													=    {'''perplexity''': perplexity}

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

          results.update(_lowercase						)

     return results




def 			_a			(		_lowercase      :			Optional[Any]						):





     '''simple docstring'''






     main()


if __name__ == "__main__":
      main()



'''simple docstring'''



from collections.abc import Iterable
from typing import Any



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Any							,				snake_case  :	int | None = None    )		->							int:
          __UpperCAmelCase			:				str													=    value
          __UpperCAmelCase			:				Node | None													=    None  # Added in order to delete a node easier
          __UpperCAmelCase			:				Node | None													=    None
          __UpperCAmelCase			:				Node | None													=    None






     def __repr__(       self  :	str    )		->							str:
          from pprint import pformat

          if self.left is None and self.right is None:
               return str(self.value    )
          return pformat({f'{self.value}': (self.left, self.right)}							,				indent=1    )



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Optional[int]							,				snake_case  :	Node | None = None    )		->							str:
          __UpperCAmelCase			:				Optional[Any]													=    root

     def __str__(       self  :	str    )		->							str:
          return str(self.root    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	Node							,				snake_case  :	Node | None    )		->							None:
          if new_children is not None:  # reset its kids
               __UpperCAmelCase			:				List[str]													=    node.parent
          if node.parent is not None:  # reset its parent
               if self.is_right(snake_case    ):  # If it is the right children
                    __UpperCAmelCase			:				int													=    new_children
               else:
                    __UpperCAmelCase			:				Tuple													=    new_children
          else:
               __UpperCAmelCase			:				List[Any]													=    new_children

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Node    )		->							bool:
          if node.parent and node.parent.right:
               return node == node.parent.right
          return False

     def 				lowerCamelCase__				(       self  :	int    )		->							bool:
          return self.root is None

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Optional[Any]    )		->							None:
          __UpperCAmelCase			:				int													=    Node(snake_case    )  # create a new Node
          if self.empty():  # if Tree is empty
               __UpperCAmelCase			:				List[Any]													=    new_node  # set its root
          else:  # Tree is not empty
               __UpperCAmelCase			:				Tuple													=    self.root  # from root
               if parent_node is None:
                    return
               while True:  # While we don't get to a leaf
                    if value < parent_node.value:  # We go left
                         if parent_node.left is None:
                              __UpperCAmelCase			:				Optional[int]													=    new_node  # We insert the new node in a leaf
                              break
                         else:
                              __UpperCAmelCase			:				List[Any]													=    parent_node.left
                    else:
                         if parent_node.right is None:
                              __UpperCAmelCase			:				Optional[int]													=    new_node
                              break
                         else:
                              __UpperCAmelCase			:				List[str]													=    parent_node.right
               __UpperCAmelCase			:				int													=    parent_node

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				*snake_case  :	List[Any]    )		->							None:
          for value in values:
               self.__insert(snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Dict    )		->							Node | None:
          if self.empty():
               raise IndexError('''Warning: Tree is empty! please use another.'''    )
          else:
               __UpperCAmelCase			:				List[Any]													=    self.root
               # use lazy evaluation here to avoid NoneType Attribute error
               while node is not None and node.value is not value:
                    __UpperCAmelCase			:				Union[str, Any]													=    node.left if value < node.value else node.right
               return node

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	Node | None = None    )		->							Node | None:
          if node is None:
               if self.root is None:
                    return None
               __UpperCAmelCase			:				Optional[Any]													=    self.root

          if not self.empty():
               while node.right is not None:
                    __UpperCAmelCase			:				str													=    node.right
          return node

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	Node | None = None    )		->							Node | None:
          if node is None:
               __UpperCAmelCase			:				str													=    self.root
          if self.root is None:
               return None
          if not self.empty():
               __UpperCAmelCase			:				List[str]													=    self.root
               while node.left is not None:
                    __UpperCAmelCase			:				str													=    node.left
          return node

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	int    )		->							None:
          __UpperCAmelCase			:				List[str]													=    self.search(snake_case    )  # Look for the node with that label
          if node is not None:
               if node.left is None and node.right is None:  # If it has no children
                    self.__reassign_nodes(snake_case							,				snake_case    )
               elif node.left is None:  # Has only right children
                    self.__reassign_nodes(snake_case							,				node.right    )
               elif node.right is None:  # Has only left children
                    self.__reassign_nodes(snake_case							,				node.left    )
               else:
                    __UpperCAmelCase			:				Optional[int]													=    self.get_max(
                        node.left    )  # Gets the max value of the left branch
                    self.remove(tmp_node.value    )  # type: ignore
                    __UpperCAmelCase			:				List[str]													=    (
                        tmp_node.value  # type: ignore
                    )  # Assigns the value to the node to delete and keep tree structure

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	Node | None    )		->							Iterable:
          if node is not None:
               yield node  # Preorder Traversal
               yield from self.preorder_traverse(node.left    )
               yield from self.preorder_traverse(node.right    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	int=None    )		->							Any:
          if traversal_function is None:
               return self.preorder_traverse(self.root    )
          else:
               return traversal_function(self.root    )

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	list							,				snake_case  :	Node | None    )		->							None:
          if node:
               self.inorder(snake_case							,				node.left    )
               arr.append(node.value    )
               self.inorder(snake_case							,				node.right    )






     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	int							,				snake_case  :	Node    )		->							int:
          __UpperCAmelCase			:				list[int]													=    []
          self.inorder(snake_case							,				snake_case    )  # append all values to list using inorder traversal
          return arr[k - 1]




def 			_a			(		_lowercase      :			Node | None						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    []
     if curr_node is not None:
          __UpperCAmelCase			:				Union[str, Any]													=    postorder(curr_node.left						) + postorder(curr_node.right						) + [curr_node]
     return node_list




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    (8, 3, 6, 1, 10, 14, 13, 4, 7)
     __UpperCAmelCase			:				Union[str, Any]													=    BinarySearchTree()
     for i in testlist:
          t.insert(_lowercase						)

     # Prints all the elements of the list in order traversal
     print(_lowercase						)

     if t.search(6						) is not None:
          print('''The value 6 exists'''						)
     else:
          print('''The value 6 doesn\'t exist'''						)

     if t.search(-1						) is not None:
          print('''The value -1 exists'''						)
     else:
          print('''The value -1 doesn\'t exist'''						)

     if not t.empty():
          print('''Max Value: '''    ,      t.get_max().value						)  # type: ignore
          print('''Min Value: '''    ,      t.get_min().value						)  # type: ignore

     for i in testlist:
          t.remove(_lowercase						)
          print(_lowercase						)


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)