






'''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)














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)}''')






