





def     lowerCamelCase__  (  UpperCamelCase__      :							Optional[int]							,     UpperCamelCase__      :							List[Any]    )      ->			int:



					'''simple docstring'''




					_snake_case						=					1.0
					for i in range(1							,     r + 1    ):
										ret *= (n + 1 - i) / i
					return round(A_    )





def     lowerCamelCase__  (  UpperCamelCase__      :							Tuple    )      ->			int:



					'''simple docstring'''




					return choose(8 + n							,     A_    ) + choose(9 + n							,     A_    ) - 10





def     lowerCamelCase__  (  UpperCamelCase__      :							List[Any]    )      ->			int:



					'''simple docstring'''




					return sum(non_bouncy_exact(A_    ) for i in range(1							,     n + 1    )    )





def     lowerCamelCase__  (  UpperCamelCase__      :							Any = 100    )      ->			int:



					'''simple docstring'''




					return non_bouncy_upto(A_    )


if __name__ == "__main__":
					print(F"{solution() = }")







"""simple docstring"""







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__lowercase          = logging.getLogger(__name__)






@dataclass
class 		_A    :







    """simple docstring"""




    UpperCAmelCase			:				str      =			field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
    UpperCAmelCase			:				bool      =			field(default=_a       ,metadata={"""help""": """Set this flag to use fast tokenization."""}    )
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,)






def 	lowercase     (							)-> Dict:
  '''simple docstring'''







  a		:						Tuple 				=     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.
    a				,						a				,						a		:						Any 				=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
  else:
    a				,						a				,						a		:						List[str] 				=     parser.parse_args_into_dataclasses()

  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"    ,  A_			)

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

  a		:						str 				=     AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,  cache_dir=model_args.cache_dir    ,  )
  a		:						str 				=     AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,  cache_dir=model_args.cache_dir    ,  use_fast=A_    ,  )
  a		:						List[Any] 				=     AutoModelForQuestionAnswering.from_pretrained(
      model_args.model_name_or_path    ,  from_tf=bool(".ckpt" in model_args.model_name_or_path			)    ,  config=A_    ,  cache_dir=model_args.cache_dir    ,  )

  # Get datasets
  a		:						int 				=     hasattr(model.config    ,  "lang2id"			)
  a		:						Any 				=     (
      SquadDataset(
          A_    ,  tokenizer=A_    ,  is_language_sensitive=A_    ,  cache_dir=model_args.cache_dir			)
      if training_args.do_train
      else None
  )
  a		:						Tuple 				=     (
      SquadDataset(
          A_    ,  tokenizer=A_    ,  mode="dev"    ,  is_language_sensitive=A_    ,  cache_dir=model_args.cache_dir    ,  )
      if training_args.do_eval
      else None
  )

  # Data collator
  a		:						str 				=     DataCollatorWithPadding(A_    ,  pad_to_multiple_of=8			) if training_args.fpaa else None

  # Initialize our Trainer
  a		:						Tuple 				=     Trainer(
      model=A_    ,  args=A_    ,  train_dataset=A_    ,  eval_dataset=A_    ,  data_collator=A_    ,  )

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
    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			)


def 	lowercase     (							A_			)-> int:
  '''simple docstring'''







  main()


if __name__ == "__main__":
 main()






