


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__							:						int							     =   getLogger(__name__)


lowerCamelCase__							:						Optional[int]							     =   """cuda""" if torch.cuda.is_available() else """cpu"""







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase = 8							,   __lowerCAmelCase = DEFAULT_DEVICE							,   __lowerCAmelCase=False							,   __lowerCAmelCase="summarization"							,   __lowerCAmelCase=None							,   **__lowerCAmelCase							,   )    ->				Dict:
  snake_case__						= Path(__lowerCAmelCase				).open('''w'''							,   encoding='''utf-8'''				)
  snake_case__						= str(__lowerCAmelCase				)
  snake_case__						= AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase				).to(__lowerCAmelCase				)
  if fpaa:
    snake_case__						= model.half()

  snake_case__						= AutoTokenizer.from_pretrained(__lowerCAmelCase				)
  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(__lowerCAmelCase							,   __lowerCAmelCase				)
  if prefix is None:
    snake_case__						= prefix or getattr(model.config							,   '''prefix'''							,   ''''''				) or ''''''
  for examples_chunk in tqdm(list(chunks(__lowerCAmelCase							,   __lowerCAmelCase				)				)				):
    snake_case__						= [prefix + text for text in examples_chunk]
    snake_case__						= tokenizer(__lowerCAmelCase							,   return_tensors='''pt'''							,   truncation=__lowerCAmelCase							,   padding='''longest'''				).to(__lowerCAmelCase				)
    snake_case__						= model.generate(
        input_ids=batch.input_ids							,   attention_mask=batch.attention_mask							,   **__lowerCAmelCase							,   )
    snake_case__						= 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()
  snake_case__						= int(time.time() - start_time				)  # seconds
  snake_case__						= len(__lowerCAmelCase				)
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,   4				)}







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







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase=True				)    ->				str:
  snake_case__						= 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
  snake_case__				,				snake_case__						= parser.parse_known_args()
  snake_case__						= parse_numeric_n_bool_cl_kwargs(__lowerCAmelCase				)
  if parsed_args and verbose:
    print(F"""parsed the following generate kwargs: {parsed_args}"""				)
  snake_case__						= [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
  if args.n_obs > 0:
    snake_case__						= 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."""				)

  if args.device == "cpu" and args.fpaa:
    # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
    raise ValueError('''Can\'t mix --fp16 and --device cpu'''				)

  snake_case__						= 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
  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(__lowerCAmelCase				)]
  snake_case__						= score_fn(__lowerCAmelCase							,   __lowerCAmelCase				)
  scores.update(__lowerCAmelCase				)

  if args.dump_args:
    scores.update(__lowerCAmelCase				)
  if args.info:
    snake_case__						= 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)



"""simple docstring"""




def     __magic_name__      ( __snake_case							:			Any	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Union[str, Any]=False    )					->							Optional[Any]:

       if isinstance(__snake_case	,					__snake_case    ) and isinstance(__snake_case	,					__snake_case    ):
              lowercase       :							Tuple									=				len(set_a.intersection(__snake_case    )    )

              if alternative_union:
                     lowercase       :							Union[str, Any]									=				len(__snake_case    ) + len(__snake_case    )
              else:
                     lowercase       :							Optional[Any]									=				len(set_a.union(__snake_case    )    )

              return intersection / union

       if isinstance(__snake_case	,					(list, tuple)    ) and isinstance(__snake_case	,					(list, tuple)    ):
              lowercase       :							Tuple									=				[element for element in set_a if element in set_b]

              if alternative_union:
                     lowercase       :							List[str]									=				len(__snake_case    ) + len(__snake_case    )
                     return len(__snake_case    ) / union
              else:
                     lowercase       :							Dict									=				set_a + [element for element in set_b if element not in set_a]
                     return len(__snake_case    ) / len(__snake_case    )

              return len(__snake_case    ) / len(__snake_case    )
       return None


if __name__ == "__main__":
   _A     :			List[str]  						=			{"""a""", """b""", """c""", """d""", """e"""}
   _A     :			Dict  						=			{"""c""", """d""", """e""", """f""", """h""", """i"""}
   print(jaccard_similarity(set_a, set_b))


