code: 
  
  
  from __future__ import annotations
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if not list_of_ints:
            return []
  
       if min(_A       ) < 0:
            raise ValueError('''All numbers must be positive'''       )
  
       snake_case					=       max(len(bin(_A       )[2:]       ) for x in list_of_ints       )
       return _msd_radix_sort(_A							,_A       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if bit_position == 0 or len(_A       ) in [0, 1]:
            return list_of_ints
  
       snake_case					=       []
       snake_case					=       []
       # Split numbers based on bit at bit_position from the right
       for number in list_of_ints:
            if (number >> (bit_position - 1)) & 1:
                 # number has a one at bit bit_position
                 ones.append(_A       )
            else:
                 # number has a zero at bit bit_position
                 zeros.append(_A       )
  
            # recursively split both lists further
       snake_case					=       _msd_radix_sort(_A							,bit_position - 1       )
       snake_case					=       _msd_radix_sort(_A							,bit_position - 1       )
  
       # recombine lists
       snake_case					=       zeros
       res.extend(_A       )
  
       return res
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       len(_A       )
       if not list_of_ints or length == 1:
            return
  
       if min(_A       ) < 0:
            raise ValueError('''All numbers must be positive'''       )
  
       snake_case					=       max(len(bin(_A       )[2:]       ) for x in list_of_ints       )
       _msd_radix_sort_inplace(_A							,_A							,0							,_A       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if bit_position == 0 or end_index - begin_index <= 1:
            return
  
       bit_position -= 1
  
       snake_case					=       begin_index
       snake_case					=       end_index - 1
       while i <= j:
            snake_case					=       False
            if not (list_of_ints[i] >> bit_position) & 1:
                 # found zero at the beginning
                 i += 1
                 snake_case					=       True
            if (list_of_ints[j] >> bit_position) & 1:
                 # found one at the end
                 j -= 1
                 snake_case					=       True
  
            if changed:
                 continue
  
            snake_case				,						snake_case					=       list_of_ints[j], list_of_ints[i]
            j -= 1
            if j != i:
                 i += 1
  
       _msd_radix_sort_inplace(_A							,_A							,_A							,_A       )
       _msd_radix_sort_inplace(_A							,_A							,_A							,_A       )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
style_context: 
  
  
  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    :				str       =					getLogger(__name__)
  
  
  _SCREAMING_SNAKE_CASE    :				int       =					"cuda" if torch.cuda.is_available() else "cpu"
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_ = 8							,UpperCamelCase_ = DEFAULT_DEVICE							,UpperCamelCase_=False							,UpperCamelCase_="summarization"							,UpperCamelCase_=None							,**UpperCamelCase_							,):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       Path(UpperCamelCase_       ).open('''w'''							,encoding='''utf-8'''       )
       snake_case					=       str(UpperCamelCase_       )
       snake_case					=       AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_       ).to(UpperCamelCase_       )
       if fpaa:
            snake_case					=       model.half()
  
       snake_case					=       AutoTokenizer.from_pretrained(UpperCamelCase_       )
       logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''       )  # if this is wrong, check config.model_type.
  
       snake_case					=       time.time()
       # update config with task specific params
       use_task_specific_params(UpperCamelCase_							,UpperCamelCase_       )
       if prefix is None:
            snake_case					=       prefix or getattr(model.config							,'''prefix'''							,''''''       ) or ''''''
       for examples_chunk in tqdm(list(chunks(UpperCamelCase_							,UpperCamelCase_       )       )       ):
            snake_case					=       [prefix + text for text in examples_chunk]
            snake_case					=       tokenizer(UpperCamelCase_							,return_tensors='''pt'''							,truncation=UpperCamelCase_							,padding='''longest'''       ).to(UpperCamelCase_       )
            snake_case					=       model.generate(
                input_ids=batch.input_ids							,attention_mask=batch.attention_mask							,**UpperCamelCase_							,)
            snake_case					=       tokenizer.batch_decode(UpperCamelCase_							,skip_special_tokens=UpperCamelCase_							,clean_up_tokenization_spaces=UpperCamelCase_       )
            for hypothesis in dec:
                 fout.write(hypothesis + '''\n'''       )
                 fout.flush()
       fout.close()
       snake_case					=       int(time.time() - start_time       )  # seconds
       snake_case					=       len(UpperCamelCase_       )
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,4       )}
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''       )
  def     UpperCAmelCase__		(UpperCamelCase_=True       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       argparse.ArgumentParser()
       parser.add_argument('''model_name'''							,type=UpperCamelCase_							,help='''like facebook/bart-large-cnn,t5-base, etc.'''       )
       parser.add_argument('''input_path'''							,type=UpperCamelCase_							,help='''like cnn_dm/test.source'''       )
       parser.add_argument('''save_path'''							,type=UpperCamelCase_							,help='''where to save summaries'''       )
       parser.add_argument('''--reference_path'''							,type=UpperCamelCase_							,required=UpperCamelCase_							,help='''like cnn_dm/test.target'''       )
       parser.add_argument('''--score_path'''							,type=UpperCamelCase_							,required=UpperCamelCase_							,default='''metrics.json'''							,help='''where to save metrics'''       )
       parser.add_argument('''--device'''							,type=UpperCamelCase_							,required=UpperCamelCase_							,default=UpperCamelCase_							,help='''cuda, cuda:1, cpu etc.'''       )
       parser.add_argument(
           '''--prefix'''							,type=UpperCamelCase_							,required=UpperCamelCase_							,default=UpperCamelCase_							,help='''will be added to the begininng of src examples'''       )
       parser.add_argument('''--task'''							,type=UpperCamelCase_							,default='''summarization'''							,help='''used for task_specific_params + metrics'''       )
       parser.add_argument('''--bs'''							,type=UpperCamelCase_							,default=8							,required=UpperCamelCase_							,help='''batch size'''       )
       parser.add_argument(
           '''--n_obs'''							,type=UpperCamelCase_							,default=-1							,required=UpperCamelCase_							,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=UpperCamelCase_							,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
       snake_case				,						snake_case					=       parser.parse_known_args()
       snake_case					=       parse_numeric_n_bool_cl_kwargs(UpperCamelCase_       )
       if parsed_args and verbose:
            print(F'''parsed the following generate kwargs: {parsed_args}'''       )
       with open(args.input_path       ) as f:
            snake_case					=       [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
            snake_case					=       examples[: args.n_obs]
       Path(args.save_path       ).parent.mkdir(exist_ok=UpperCamelCase_       )
       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.'''       )
       snake_case					=       generate_summaries_or_translations(
           UpperCamelCase_							,args.save_path							,args.model_name							,batch_size=args.bs							,device=args.device							,fpaa=args.fpaa							,task=args.task							,prefix=args.prefix							,**UpperCamelCase_							,)
  
       if args.reference_path is None:
            return {}
  
       # Compute scores
       snake_case					=       calculate_bleu if '''translation''' in args.task else calculate_rouge
       snake_case					=       [x.rstrip() for x in open(args.save_path       ).readlines()]
       snake_case					=       [x.rstrip() for x in open(args.reference_path       ).readlines()][: len(UpperCamelCase_       )]
       snake_case					=       score_fn(UpperCamelCase_							,UpperCamelCase_       )
       scores.update(UpperCamelCase_       )
  
       if args.dump_args:
            scores.update(UpperCamelCase_       )
       if args.info:
            snake_case					=       args.info
  
       if verbose:
            print(UpperCamelCase_       )
  
       if args.score_path is not None:
            json.dump(UpperCamelCase_							,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)
  
  
  
  
  