

'''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


lowerCAmelCase_    	=							getLogger(__name__)


lowerCAmelCase_    	=							"cuda" if torch.cuda.is_available() else "cpu"







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase = 8			,UpperCAmelCase = DEFAULT_DEVICE			,UpperCAmelCase=False			,UpperCAmelCase="summarization"			,UpperCAmelCase=None			,**UpperCAmelCase			,):






   '''simple docstring'''







   A__         =    Path(snake_case_	).open('w'			,encoding='utf-8'	)
   A__         =    str(snake_case_	)
   A__         =    AutoModelForSeqaSeqLM.from_pretrained(snake_case_	).to(snake_case_	)
   if fpaa:
      A__         =    model.half()

   A__         =    AutoTokenizer.from_pretrained(snake_case_	)
   logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""	)  # if this is wrong, check config.model_type.

   A__         =    time.time()
   # update config with task specific params
   use_task_specific_params(snake_case_			,snake_case_	)
   if prefix is None:
      A__         =    prefix or getattr(model.config			,'prefix'			,''	) or ''''''
   for examples_chunk in tqdm(list(chunks(snake_case_			,snake_case_	)	)	):
      A__         =    [prefix + text for text in examples_chunk]
      A__         =    tokenizer(snake_case_			,return_tensors='pt'			,truncation=snake_case_			,padding='longest'	).to(snake_case_	)
      A__         =    model.generate(
          input_ids=batch.input_ids			,attention_mask=batch.attention_mask			,**snake_case_			,)
      A__         =    tokenizer.batch_decode(snake_case_			,skip_special_tokens=snake_case_			,clean_up_tokenization_spaces=snake_case_	)
      for hypothesis in dec:
         fout.write(hypothesis + '\n'	)
         fout.flush()
   fout.close()
   A__         =    int(time.time() - start_time	)  # seconds
   A__         =    len(snake_case_	)
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			,4	)}







def 	_A			(   ):






   '''simple docstring'''







   return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'	)







def 	_A			(   UpperCAmelCase=True	):






   '''simple docstring'''







   A__         =    argparse.ArgumentParser()
   parser.add_argument('model_name'			,type=snake_case_			,help='like facebook/bart-large-cnn,t5-base, etc.'	)
   parser.add_argument('input_path'			,type=snake_case_			,help='like cnn_dm/test.source'	)
   parser.add_argument('save_path'			,type=snake_case_			,help='where to save summaries'	)
   parser.add_argument('--reference_path'			,type=snake_case_			,required=snake_case_			,help='like cnn_dm/test.target'	)
   parser.add_argument('--score_path'			,type=snake_case_			,required=snake_case_			,default='metrics.json'			,help='where to save metrics'	)
   parser.add_argument('--device'			,type=snake_case_			,required=snake_case_			,default=snake_case_			,help='cuda, cuda:1, cpu etc.'	)
   parser.add_argument(
       '--prefix'			,type=snake_case_			,required=snake_case_			,default=snake_case_			,help='will be added to the begininng of src examples'	)
   parser.add_argument('--task'			,type=snake_case_			,default='summarization'			,help='used for task_specific_params + metrics'	)
   parser.add_argument('--bs'			,type=snake_case_			,default=8			,required=snake_case_			,help='batch size'	)
   parser.add_argument(
       '--n_obs'			,type=snake_case_			,default=-1			,required=snake_case_			,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=snake_case_			,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
   A__         =    parser.parse_known_args()
   A__         =    parse_numeric_n_bool_cl_kwargs(snake_case_	)
   if parsed_args and verbose:
      print(F"""parsed the following generate kwargs: {parsed_args}"""	)
   with open(args.input_path	) as f:
      A__         =    [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
   if args.n_obs > 0:
      A__         =    examples[: args.n_obs]
   Path(args.save_path	).parent.mkdir(exist_ok=snake_case_	)
   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."""	)
   A__         =    generate_summaries_or_translations(
       snake_case_			,args.save_path			,args.model_name			,batch_size=args.bs			,device=args.device			,fpaa=args.fpaa			,task=args.task			,prefix=args.prefix			,**snake_case_			,)

   if args.reference_path is None:
      return {}

   # Compute scores
   A__         =    calculate_bleu if '''translation''' in args.task else calculate_rouge
   A__         =    [x.rstrip() for x in open(args.save_path	).readlines()]
   A__         =    [x.rstrip() for x in open(args.reference_path	).readlines()][: len(snake_case_	)]
   A__         =    score_fn(snake_case_			,snake_case_	)
   scores.update(snake_case_	)

   if args.dump_args:
      scores.update(snake_case_	)
   if args.info:
      A__         =    args.info

   if verbose:
      print(snake_case_	)

   if args.score_path is not None:
      json.dump(snake_case_			,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)








'''simple docstring'''







import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCAmelCase_    	=							2 << 2_0  # 2mb
lowerCAmelCase_    	=							get_logger(__name__)

class   _snake_case( UpperCAmelCase			):






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






    """simple docstring"""


    A__         =    self._options.get_all_options().get('runner'						)
    return runner in [None, "DirectRunner", "PortableRunner"]







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase=False	):






   '''simple docstring'''







   A__         =    FileSystems
   if fs.exists(UpperCAmelCase	):
      if force_upload:
         logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""	)
      else:
         logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""	)
         return
   with fs.create(UpperCAmelCase	) as remote_file:
      with open(UpperCAmelCase			,'rb'	) as local_file:
         A__         =    local_file.read(UpperCAmelCase	)
         while chunk:
            remote_file.write(UpperCAmelCase	)
            A__         =    local_file.read(UpperCAmelCase	)







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase=False	):






   '''simple docstring'''







   A__         =    FileSystems
   if os.path.exists(UpperCAmelCase	):
      if force_download:
         logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""	)
      else:
         logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""	)
         return
   with fs.open(UpperCAmelCase	) as remote_file:
      with open(UpperCAmelCase			,'wb'	) as local_file:
         A__         =    remote_file.read(UpperCAmelCase	)
         while chunk:
            local_file.write(UpperCAmelCase	)
            A__         =    remote_file.read(UpperCAmelCase	)


