






'''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__											=     getLogger(__name__)


SCREAMING_SNAKE_CASE__											=     'cuda' if torch.cuda.is_available() else 'cpu'







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

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

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







def 					lowercase__				(   )->				Dict:
      return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""					)







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

      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"""					)

      UpperCamelCase       						=      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
      UpperCamelCase       						=      calculate_bleu if """translation""" in args.task else calculate_rouge
      UpperCamelCase       						=      [x.rstrip() for x in open(args.save_path					).readlines()]
      UpperCamelCase       						=      [x.rstrip() for x in open(args.reference_path					).readlines()][: len(__UpperCamelCase					)]
      UpperCamelCase       						=      score_fn(__UpperCamelCase	,		__UpperCamelCase					)
      scores.update(__UpperCamelCase					)

      if args.dump_args:
            scores.update(__UpperCamelCase					)
      if args.info:
            UpperCamelCase       						=      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)








'''simple docstring'''

from __future__ import annotations

from typing import Any






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE = 6   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      None
        UpperCamelCase       						=      None
        self.create_linked_list(_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      Node()
        UpperCamelCase       						=      current_node
        UpperCamelCase       						=      current_node
        UpperCamelCase       						=      current_node
        for _ in range(1    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      Node()
              UpperCamelCase       						=      current_node
              UpperCamelCase       						=      previous_node
              UpperCamelCase       						=      current_node
        UpperCamelCase       						=      self.front
        UpperCamelCase       						=      previous_node

  def 						A__		(   self   )						->   bool:



        """simple docstring"""






        return (
            self.front == self.rear
            and self.front is not None
            and self.front.data is None
        )

  def 						A__		(   self   )						->   Any | None:



        """simple docstring"""






        self.check_can_perform_operation()
        return self.front.data if self.front else None

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        if self.rear is None:
              return

        self.check_is_full()
        if not self.is_empty():
              UpperCamelCase       						=      self.rear.next
        if self.rear:
              UpperCamelCase       						=      data

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        self.check_can_perform_operation()
        if self.rear is None or self.front is None:
              return None
        if self.front == self.rear:
              UpperCamelCase       						=      self.front.data
              UpperCamelCase       						=      None
              return data

        UpperCamelCase       						=      self.front
        UpperCamelCase       						=      old_front.next
        UpperCamelCase       						=      old_front.data
        UpperCamelCase       						=      None
        return data

  def 						A__		(   self   )						->   None:



        """simple docstring"""






        if self.is_empty():
              raise Exception("""Empty Queue"""   )


  def 						A__		(   self   )						->   None:



        """simple docstring"""






        if self.rear and self.rear.next == self.front:
              raise Exception("""Full Queue"""   )






class        a_		:

  def __init__(   self   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      None
        UpperCamelCase       						=      None
        UpperCamelCase       						=      None


if __name__ == "__main__":
     import doctest

     doctest.testmod()
