"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


_UpperCAmelCase     =			logging.getLogger(__name__)
_UpperCAmelCase     =			list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_UpperCAmelCase     =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 					a	:
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={
          'help': (
              'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
          )
      }	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCAmelCase__       )}	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={
          'help': (
              'Override some existing default config settings when a model is trained from scratch. Example: '
              'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
          )
      }	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,      )
  UpperCamelCase	:				bool     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}	,      )
  UpperCamelCase	:				str     						=					field(
      default='main'	,      metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,      )
  UpperCamelCase	:				bool     						=					field(
      default=UpperCAmelCase__	,      metadata={
          'help': (
              'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
              'with private models).'
          )
      }	,      )



  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[str]:


     '''simple docstring'''




     if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
        raise ValueError(
            """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""		)







@dataclass
class 					a	:
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'The name of the dataset to use (via the datasets library).'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}       )
  UpperCamelCase	:				Optional[str]     						=					field(default=UpperCAmelCase__	,      metadata={'help': 'The input training data file (a text file).'}       )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}	,      )
  UpperCamelCase	:				Optional[str]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}	,      )
  UpperCamelCase	:				bool     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'Overwrite the cached training and evaluation sets'}       )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=5	,      metadata={
          'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
      }	,      )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=UpperCAmelCase__	,      metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated. Default to the max input length of the model.'
          )
      }	,      )
  UpperCamelCase	:				Optional[int]     						=					field(
      default=UpperCAmelCase__	,      metadata={'help': 'The number of processes to use for the preprocessing.'}	,      )
  UpperCamelCase	:				float     						=					field(
      default=0.15	,      metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}       )
  UpperCamelCase	:				bool     						=					field(
      default=UpperCAmelCase__	,      metadata={
          'help': (
              'Whether to pad all samples to `max_seq_length`. '
              'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
          )
      }	,      )



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Optional[Any]:


     '''simple docstring'''




     if self.train_file is not None:
        SCREAMING_SNAKE_CASE_: Any     			=self.train_file.split("""."""		)[-1]
        assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
     if self.validation_file is not None:
        SCREAMING_SNAKE_CASE_: List[str]     			=self.validation_file.split("""."""		)[-1]
        assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def      __magic_name__				(							lowercase							,			lowercase ):
   with open(lowercase							,			"""r"""							,			encoding="""utf-8""" ) as f:
      SCREAMING_SNAKE_CASE_: int     			=[json.loads(lowercase ) for line in f.read().splitlines() if (len(lowercase ) > 0 and not line.isspace())]
   assert len(lowercase ) == len(lowercase )

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			={c: dataset[c] for c in dataset.column_names}
   SCREAMING_SNAKE_CASE_: List[str]     			=refs
   return Dataset.from_dict(lowercase )





def      __magic_name__				(							):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   SCREAMING_SNAKE_CASE_: Any     			=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
   if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
   else:
      SCREAMING_SNAKE_CASE_: Dict     			=parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=None
   if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
      SCREAMING_SNAKE_CASE_: Tuple     			=get_last_checkpoint(training_args.output_dir )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
         raise ValueError(
             f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
             """Use --overwrite_output_dir to overcome.""" )
      elif last_checkpoint is not None:
         logger.info(
             f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
             """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )

    # Setup logging
   logging.basicConfig(
       format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,			datefmt="""%m/%d/%Y %H:%M:%S"""							,			handlers=[logging.StreamHandler(sys.stdout )]							,			)
   logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )

   # Log on each process the small summary:
   logger.warning(
       f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
       + f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {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 before initializing model.
   set_seed(training_args.seed )

   # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
   # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
   # (the dataset will be downloaded automatically from the datasets Hub).
   #
   # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
   # 'text' is found. You can easily tweak this behavior (see below).
   #
   # In distributed training, the load_dataset function guarantee that only one local process can concurrently
   # download the dataset.
   if data_args.dataset_name is not None:
      # Downloading and loading a dataset from the hub.
      SCREAMING_SNAKE_CASE_: List[str]     			=load_dataset(data_args.dataset_name							,			data_args.dataset_config_name )
      if "validation" not in datasets.keys():
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_dataset(
             data_args.dataset_name							,			data_args.dataset_config_name							,			split=f'''train[:{data_args.validation_split_percentage}%]'''							,			)
         SCREAMING_SNAKE_CASE_: Optional[int]     			=load_dataset(
             data_args.dataset_name							,			data_args.dataset_config_name							,			split=f'''train[{data_args.validation_split_percentage}%:]'''							,			)
   else:
      SCREAMING_SNAKE_CASE_: List[Any]     			={}
      if data_args.train_file is not None:
         SCREAMING_SNAKE_CASE_: List[str]     			=data_args.train_file
      if data_args.validation_file is not None:
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=data_args.validation_file
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=data_args.train_file.split(""".""" )[-1]
      if extension == "txt":
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""text"""
      SCREAMING_SNAKE_CASE_: Optional[int]     			=load_dataset(lowercase							,			data_files=lowercase )
   # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
   # https://huggingface.co/docs/datasets/loading_datasets.html.

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   SCREAMING_SNAKE_CASE_: Any     			={
       """cache_dir""": model_args.cache_dir,
       """revision""": model_args.model_revision,
       """use_auth_token""": True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      SCREAMING_SNAKE_CASE_: int     			=AutoConfig.from_pretrained(model_args.config_name							,			**lowercase )
   elif model_args.model_name_or_path:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoConfig.from_pretrained(model_args.model_name_or_path							,			**lowercase )
   else:
      SCREAMING_SNAKE_CASE_: List[str]     			=CONFIG_MAPPING[model_args.model_type]()
      logger.warning("""You are instantiating a new config instance from scratch.""" )
      if model_args.config_overrides is not None:
         logger.info(f'''Overriding config: {model_args.config_overrides}''' )
         config.update_from_string(model_args.config_overrides )
         logger.info(f'''New config: {config}''' )

   SCREAMING_SNAKE_CASE_: List[Any]     			={
       """cache_dir""": model_args.cache_dir,
       """use_fast""": model_args.use_fast_tokenizer,
       """revision""": model_args.model_revision,
       """use_auth_token""": True if model_args.use_auth_token else None,
   }
   if model_args.tokenizer_name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoTokenizer.from_pretrained(model_args.tokenizer_name							,			**lowercase )
   elif model_args.model_name_or_path:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoTokenizer.from_pretrained(model_args.model_name_or_path							,			**lowercase )
   else:
      raise ValueError(
          """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
          """You can do it from another script, save it, and load it from here, using --tokenizer_name.""" )

   if model_args.model_name_or_path:
      SCREAMING_SNAKE_CASE_: Dict     			=AutoModelForMaskedLM.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							,			revision=model_args.model_revision							,			use_auth_token=True if model_args.use_auth_token else None							,			)
   else:
      logger.info("""Training new model from scratch""" )
      SCREAMING_SNAKE_CASE_: str     			=AutoModelForMaskedLM.from_config(lowercase )

   model.resize_token_embeddings(len(lowercase ) )

   # Preprocessing the datasets.
   # First we tokenize all the texts.
   if training_args.do_train:
      SCREAMING_SNAKE_CASE_: str     			=datasets["""train"""].column_names
   else:
      SCREAMING_SNAKE_CASE_: List[Any]     			=datasets["""validation"""].column_names
   SCREAMING_SNAKE_CASE_: Dict     			="""text""" if """text""" in column_names else column_names[0]

   SCREAMING_SNAKE_CASE_: List[str]     			="""max_length""" if data_args.pad_to_max_length else False

   def tokenize_function(lowercase ):
      # Remove empty lines
      SCREAMING_SNAKE_CASE_: List[str]     			=[line for line in examples["""text"""] if len(lowercase ) > 0 and not line.isspace()]
      return tokenizer(examples["""text"""]							,			padding=lowercase							,			truncation=lowercase							,			max_length=data_args.max_seq_length )

   SCREAMING_SNAKE_CASE_: int     			=datasets.map(
       lowercase							,			batched=lowercase							,			num_proc=data_args.preprocessing_num_workers							,			remove_columns=[text_column_name]							,			load_from_cache_file=not data_args.overwrite_cache							,			)

   # Add the chinese references if provided
   if data_args.train_ref_file is not None:
      SCREAMING_SNAKE_CASE_: Dict     			=add_chinese_references(tokenized_datasets["""train"""]							,			data_args.train_ref_file )
   if data_args.validation_ref_file is not None:
      SCREAMING_SNAKE_CASE_: List[Any]     			=add_chinese_references(
          tokenized_datasets["""validation"""]							,			data_args.validation_ref_file )
   # If we have ref files, need to avoid it removed by trainer
   SCREAMING_SNAKE_CASE_: Dict     			=data_args.train_ref_file or data_args.validation_ref_file
   if has_ref:
      SCREAMING_SNAKE_CASE_: Tuple     			=False

   # Data collator
   # This one will take care of randomly masking the tokens.
   SCREAMING_SNAKE_CASE_: Tuple     			=DataCollatorForWholeWordMask(tokenizer=lowercase							,			mlm_probability=data_args.mlm_probability )

   # Initialize our Trainer
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=Trainer(
       model=lowercase							,			args=lowercase							,			train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None							,			eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None							,			tokenizer=lowercase							,			data_collator=lowercase							,			)

   # Training
   if training_args.do_train:
      if last_checkpoint is not None:
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=last_checkpoint
      elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
         SCREAMING_SNAKE_CASE_: Dict     			=model_args.model_name_or_path
      else:
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=None
      SCREAMING_SNAKE_CASE_: str     			=trainer.train(resume_from_checkpoint=lowercase )
      trainer.save_model()  # Saves the tokenizer too for easy upload

      SCREAMING_SNAKE_CASE_: Any     			=os.path.join(training_args.output_dir							,			"""train_results.txt""" )
      if trainer.is_world_process_zero():
         with open(lowercase							,			"""w""" ) as writer:
            logger.info("""***** Train results *****""" )
            for key, value in sorted(train_result.metrics.items() ):
               logger.info(f'''  {key} = {value}''' )
               writer.write(f'''{key} = {value}\n''' )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir							,			"""trainer_state.json""" ) )

    # Evaluation
   SCREAMING_SNAKE_CASE_: List[str]     			={}
   if training_args.do_eval:
      logger.info("""*** Evaluate ***""" )

      SCREAMING_SNAKE_CASE_: List[Any]     			=trainer.evaluate()

      SCREAMING_SNAKE_CASE_: Dict     			=math.exp(eval_output["""eval_loss"""] )
      SCREAMING_SNAKE_CASE_: Tuple     			=perplexity

      SCREAMING_SNAKE_CASE_: List[str]     			=os.path.join(training_args.output_dir							,			"""eval_results_mlm_wwm.txt""" )
      if trainer.is_world_process_zero():
         with open(lowercase							,			"""w""" ) as writer:
            logger.info("""***** Eval results *****""" )
            for key, value in sorted(results.items() ):
               logger.info(f'''  {key} = {value}''' )
               writer.write(f'''{key} = {value}\n''' )

   return results





def      __magic_name__				(							lowercase ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   if upper_limit < 0:
      raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )

   SCREAMING_SNAKE_CASE_: Tuple     			=[0] * (upper_limit + 1)

   # Base case: C(0) = C(1) = 1
   SCREAMING_SNAKE_CASE_: Any     			=1
   if upper_limit > 0:
      SCREAMING_SNAKE_CASE_: List[str]     			=1

   # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
   for i in range(2							,			upper_limit + 1 ):
      for j in range(lowercase ):
         catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

   return catalan_list


if __name__ == "__main__":
   print("""\n********* Catalan Numbers Using Dynamic Programming ************\n""")
   print("""\n*** Enter -1 at any time to quit ***""")
   print("""\nEnter the upper limit (≥ 0) for the Catalan number sequence: """, end="""""")
   try:
      while True:
         _UpperCAmelCase     =			int(input().strip())
         if N < 0:
            print("""\n********* Goodbye!! ************""")
            break
         else:
            print(f"""The Catalan numbers from 0 through {N} are:""")
            print(catalan_numbers(N))
            print("""Try another upper limit for the sequence: """, end="""""")
   except (NameError, ValueError):
      print("""\n********* Invalid input, goodbye! ************\n""")

   import doctest

   doctest.testmod()



