

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     :   Union[str, Any]						     =  getLogger(__name__)


lowerCAmelCase     :   int						     =  'cuda' if torch.cuda.is_available() else 'cpu'

def    A_  (							a					,   a					,   a					,   a = 8					,   a = DEFAULT_DEVICE					,   a=False					,   a="summarization"					,   a=None					,   **a					,   ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  List[str]           =     Path(a  ).open('w'					,   encoding='utf-8'  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     str(a  )
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     AutoModelForSeqaSeqLM.from_pretrained(a  ).to(a  )
      if fpaa:
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     model.half()

      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     AutoTokenizer.from_pretrained(a  )
      logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"  )  # if this is wrong, check config.model_type.

      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     time.time()
      # update config with task specific params
      use_task_specific_params(a					,   a  )
      if prefix is None:
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     prefix or getattr(model.config					,   'prefix'					,   ''  ) or ''
      for examples_chunk in tqdm(list(chunks(a					,   a  )  )  ):
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     [prefix + text for text in examples_chunk]
            SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer(a					,   return_tensors='pt'					,   truncation=a					,   padding='longest'  ).to(a  )
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     model.generate(
                input_ids=batch.input_ids					,   attention_mask=batch.attention_mask					,   **a					,   )
            SCREAMING_SNAKE_CASE_				:  str           =     tokenizer.batch_decode(a					,   skip_special_tokens=a					,   clean_up_tokenization_spaces=a  )
            for hypothesis in dec:
                  fout.write(hypothesis + '\n'  )
                  fout.flush()
      fout.close()
      SCREAMING_SNAKE_CASE_				:  int           =     int(time.time() - start_time  )  # seconds
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     len(a  )
      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_  (							a=True  ):

      """simple docstring"""



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

      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'  )

      SCREAMING_SNAKE_CASE_				:  Tuple           =     generate_summaries_or_translations(
          a					,   args.save_path					,   args.model_name					,   batch_size=args.bs					,   device=args.device					,   fpaa=args.fpaa					,   task=args.task					,   prefix=args.prefix					,   **a					,   )

      if args.reference_path is None:
            return {}

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

      if args.dump_args:
            scores.update(a  )
      if args.info:
            SCREAMING_SNAKE_CASE_				:  Any           =     args.info

      if verbose:
            print(a  )

      if args.score_path is not None:
            json.dump(a					,   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 copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__)
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      str       =  field(default='''automatic-speech-recognition'''   ,     metadata={'''include_in_asdict_even_if_is_default''': True})
  SCREAMING_SNAKE_CASE    :      ClassVar[Features]       =  Features({'''audio''': Audio()})
  SCREAMING_SNAKE_CASE    :      ClassVar[Features]       =  Features({'''transcription''': Value('''string''')})
  SCREAMING_SNAKE_CASE    :      str       =  "audio"
  SCREAMING_SNAKE_CASE    :      str       =  "transcription"

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if self.audio_column not in features:
              raise ValueError(f"Column {self.audio_column} is not present in features."					)
        if not isinstance(features[self.audio_column] ,					_SCREAMING_SNAKE_CASE					):
              raise ValueError(f"Column {self.audio_column} is not an Audio type."					)
        SCREAMING_SNAKE_CASE_				:  int           =     copy.deepcopy(self					)
        SCREAMING_SNAKE_CASE_				:  int           =     self.input_schema.copy()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     features[self.audio_column]
        SCREAMING_SNAKE_CASE_				:  List[str]           =     input_schema
        return task_template



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return {self.audio_column: "audio", self.transcription_column: "transcription"}






