code: 
  
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  import datetime
  import json
  import time
  import warnings
  from logging import getLogger
  from pathlib import Path
  from typing import Dict, List
  
  import torch
  from tqdm import tqdm
  
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
  from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
  
  
  SCREAMING_SNAKE_CASE__		:       Any   							=							getLogger(__name__)
  
  
  SCREAMING_SNAKE_CASE__		:       Tuple   							=							"""cuda""" if torch.cuda.is_available() else """cpu"""
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase = 8   ,       __lowerCamelCase = DEFAULT_DEVICE   ,       __lowerCamelCase=False   ,       __lowerCamelCase="summarization"   ,       __lowerCamelCase=None   ,       **__lowerCamelCase   ,       )   -> Dict:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[Any]										= Path(__lowerCamelCase					).open("""w"""   ,       encoding="""utf-8"""					)
       UpperCAmelCase__			:     Union[str, Any]										= str(__lowerCamelCase					)
       UpperCAmelCase__			:     Dict										= AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase					).to(__lowerCamelCase					)
       if fpaa:
            UpperCAmelCase__			:     List[Any]										= model.half()
  
       UpperCAmelCase__			:     int										= AutoTokenizer.from_pretrained(__lowerCamelCase					)
       logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"					)  # if this is wrong, check config.model_type.
  
       UpperCAmelCase__			:     List[Any]										= time.time()
       # update config with task specific params
       use_task_specific_params(__lowerCamelCase   ,       __lowerCamelCase					)
       if prefix is None:
            UpperCAmelCase__			:     Optional[Any]										= prefix or getattr(model.config   ,       """prefix"""   ,       """"""					) or """"""
       for examples_chunk in tqdm(list(chunks(__lowerCamelCase   ,       __lowerCamelCase					)					)					):
            UpperCAmelCase__			:     str										= [prefix + text for text in examples_chunk]
            UpperCAmelCase__			:     Any										= tokenizer(__lowerCamelCase   ,       return_tensors="""pt"""   ,       truncation=__lowerCamelCase   ,       padding="""longest"""					).to(__lowerCamelCase					)
            UpperCAmelCase__			:     Optional[Any]										= model.generate(
                input_ids=batch.input_ids   ,       attention_mask=batch.attention_mask   ,       **__lowerCamelCase   ,       )
            UpperCAmelCase__			:     Any										= tokenizer.batch_decode(__lowerCamelCase   ,       skip_special_tokens=__lowerCamelCase   ,       clean_up_tokenization_spaces=__lowerCamelCase					)
            for hypothesis in dec:
                 fout.write(hypothesis + """\n"""					)
                 fout.flush()
       fout.close()
       UpperCAmelCase__			:     Union[str, Any]										= int(time.time() - start_time					)  # seconds
       UpperCAmelCase__			:     List[str]										= len(__lowerCamelCase					)
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs   ,       4					)}
  
  
  
  
  
  def    _lowerCamelCase	(	)   -> str:
  
       '''simple docstring'''
       return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""					)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase=True					)   -> Dict:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[Any]										= argparse.ArgumentParser()
       parser.add_argument("""model_name"""   ,       type=__lowerCamelCase   ,       help="""like facebook/bart-large-cnn,t5-base, etc."""					)
       parser.add_argument("""input_path"""   ,       type=__lowerCamelCase   ,       help="""like cnn_dm/test.source"""					)
       parser.add_argument("""save_path"""   ,       type=__lowerCamelCase   ,       help="""where to save summaries"""					)
       parser.add_argument("""--reference_path"""   ,       type=__lowerCamelCase   ,       required=__lowerCamelCase   ,       help="""like cnn_dm/test.target"""					)
       parser.add_argument("""--score_path"""   ,       type=__lowerCamelCase   ,       required=__lowerCamelCase   ,       default="""metrics.json"""   ,       help="""where to save metrics"""					)
       parser.add_argument("""--device"""   ,       type=__lowerCamelCase   ,       required=__lowerCamelCase   ,       default=__lowerCamelCase   ,       help="""cuda, cuda:1, cpu etc."""					)
       parser.add_argument(
           """--prefix"""   ,       type=__lowerCamelCase   ,       required=__lowerCamelCase   ,       default=__lowerCamelCase   ,       help="""will be added to the begininng of src examples"""					)
       parser.add_argument("""--task"""   ,       type=__lowerCamelCase   ,       default="""summarization"""   ,       help="""used for task_specific_params + metrics"""					)
       parser.add_argument("""--bs"""   ,       type=__lowerCamelCase   ,       default=8   ,       required=__lowerCamelCase   ,       help="""batch size"""					)
       parser.add_argument(
           """--n_obs"""   ,       type=__lowerCamelCase   ,       default=-1   ,       required=__lowerCamelCase   ,       help="""How many observations. Defaults to all."""					)
       parser.add_argument("""--fp16"""   ,       action="""store_true"""					)
       parser.add_argument("""--dump-args"""   ,       action="""store_true"""   ,       help="""print the custom hparams with the results"""					)
       parser.add_argument(
           """--info"""   ,       nargs="""?"""   ,       type=__lowerCamelCase   ,       const=datetime_now()   ,       help=(
               """use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."""
               """ lang=en-ru. If no value is passed, the current datetime string will be used."""
           )   ,       )
       # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
       UpperCAmelCase__			:     List[str]										= parser.parse_known_args()
       UpperCAmelCase__			:     Any										= parse_numeric_n_bool_cl_kwargs(__lowerCamelCase					)
       if parsed_args and verbose:
            print(F"parsed the following generate kwargs: {parsed_args}"					)
       with open(args.input_path					) as f:
            UpperCAmelCase__			:     Any										= [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
            UpperCAmelCase__			:     Tuple										= examples[: args.n_obs]
       Path(args.save_path					).parent.mkdir(exist_ok=__lowerCamelCase					)
       if args.reference_path is None and Path(args.score_path					).exists():
            warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c."					)
       UpperCAmelCase__			:     Union[str, Any]										= generate_summaries_or_translations(
           __lowerCamelCase   ,       args.save_path   ,       args.model_name   ,       batch_size=args.bs   ,       device=args.device   ,       fpaa=args.fpaa   ,       task=args.task   ,       prefix=args.prefix   ,       **__lowerCamelCase   ,       )
  
       if args.reference_path is None:
            return {}
  
       # Compute scores
       UpperCAmelCase__			:     Optional[Any]										= calculate_bleu if """translation""" in args.task else calculate_rouge
       UpperCAmelCase__			:     int										= [x.rstrip() for x in open(args.save_path					).readlines()]
       UpperCAmelCase__			:     Any										= [x.rstrip() for x in open(args.reference_path					).readlines()][: len(__lowerCamelCase					)]
       UpperCAmelCase__			:     dict										= score_fn(__lowerCamelCase   ,       __lowerCamelCase					)
       scores.update(__lowerCamelCase					)
  
       if args.dump_args:
            scores.update(__lowerCamelCase					)
       if args.info:
            UpperCAmelCase__			:     Union[str, Any]										= args.info
  
       if verbose:
            print(__lowerCamelCase					)
  
       if args.score_path is not None:
            json.dump(__lowerCamelCase   ,       open(args.score_path   ,       """w"""					)					)
  
       return scores
  
  
  if __name__ == "__main__":
        # Usage for MT:
        # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
        run_generate(verbose=True)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import random
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     Union[str, Any]										= x
            UpperCAmelCase__			:     Union[str, Any]										= y
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            return (self.x**2 + self.y**2) <= 1
  
  
  
  
       @classmethod
       def      __UpperCAmelCase     (							cls		):
            return cls(x=random.random()  ,	y=random.random()		)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> float:
  
       '''simple docstring'''
       if number_of_simulations < 1:
            raise ValueError("""At least one simulation is necessary to estimate PI."""					)
  
       UpperCAmelCase__			:     Dict										= 0
       for _ in range(__lowerCamelCase					):
            UpperCAmelCase__			:     Union[str, Any]										= Point.random_unit_square()
  
            if random_point.is_in_unit_circle():
                 number_in_unit_circle += 1
  
       return 4 * number_in_unit_circle / number_of_simulations
  
  
  if __name__ == "__main__":
        # import doctest
  
        # doctest.testmod()
        from math import pi
  
        SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""Please enter the desired number of Monte Carlo simulations: """
        SCREAMING_SNAKE_CASE__		:       int   							=							estimate_pi(int(input(prompt).strip()))
        print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')
  
  
  
  
  
  
  