
"""simple docstring"""
from jiwer import compute_measures

import datasets


lowercase_       :						Optional[Any]     					=	"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

lowercase_       :						Union[str, Any]     					=	"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

lowercase_       :						Optional[Any]     					=	"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,       _KWARGS_DESCRIPTION  )
class 			UpperCamelCase      (							datasets.Metric  ):
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return datasets.MetricInfo(
           description=_DESCRIPTION  ,     citation=_CITATION  ,     inputs_description=_KWARGS_DESCRIPTION  ,     features=datasets.Features(
               {
                   "predictions": datasets.Value("string"  ,     id="sequence"				),
                   "references": datasets.Value("string"  ,     id="sequence"				),
               }				)  ,     codebase_urls=["https://github.com/jitsi/jiwer/"]  ,     reference_urls=[
               "https://en.wikipedia.org/wiki/Word_error_rate",
           ]  ,     )




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__=False				):




       """simple docstring"""


       if concatenate_texts:
         return compute_measures(snake_case__  ,     snake_case__				)["wer"]
       else:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = 0
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 0
         for prediction, reference in zip(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = compute_measures(snake_case__  ,     snake_case__				)
           incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
           total += measures["substitutions"] + measures["deletions"] + measures["hits"]
         return incorrect / total





"""simple docstring"""
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Any=1_0_2_4				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = [], []

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = list(zip(lowerCamelCase__,						lowerCamelCase__				)				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = sorted_examples[0]

  def is_too_big(lowerCamelCase__							:							List[Any]				):
    return tok(lowerCamelCase__,						return_tensors="pt"				).input_ids.shape[1] > max_tokens

  for src, tgt in tqdm(sorted_examples[1:]				):
    _SCREAMING_SNAKE_CASE   :				int							    = new_src + " " + src
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_tgt + " " + tgt
    if is_too_big(lowerCamelCase__				) or is_too_big(lowerCamelCase__				):  # cant fit, finalize example
      finished_src.append(lowerCamelCase__				)
      finished_tgt.append(lowerCamelCase__				)
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = src, tgt
    else:  # can fit, keep adding
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = cand_src, cand_tgt

    # cleanup
  if new_src:
    assert new_tgt
    finished_src.append(lowerCamelCase__				)
    finished_tgt.append(lowerCamelCase__				)
  return finished_src, finished_tgt





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Path,						lowerCamelCase__							:							Dict,						lowerCamelCase__							:							List[str]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				int							    = Path(lowerCamelCase__				)
  save_path.mkdir(exist_ok=lowerCamelCase__				)
  for split in ["train"]:
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
    _SCREAMING_SNAKE_CASE   :				Any							    = [x.rstrip() for x in Path(lowerCamelCase__				).open().readlines()]
    _SCREAMING_SNAKE_CASE   :				int							    = [x.rstrip() for x in Path(lowerCamelCase__				).open().readlines()]
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = pack_examples(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
    print(f'''packed {split} split from {len(lowerCamelCase__				)} examples -> {len(lowerCamelCase__				)}.'''				)
    Path(save_path / f'''{split}.source'''				).open("w"				).write("\n".join(lowerCamelCase__				)				)
    Path(save_path / f'''{split}.target'''				).open("w"				).write("\n".join(lowerCamelCase__				)				)
  for split in ["val", "test"]:
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
    shutil.copyfile(lowerCamelCase__,						save_path / f'''{split}.source'''				)
    shutil.copyfile(lowerCamelCase__,						save_path / f'''{split}.target'''				)





def    _lowerCAmelCase    (     )  ->    int:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = argparse.ArgumentParser()
  parser.add_argument("--tok_name",						type=lowerCamelCase__,						help="like facebook/bart-large-cnn,t5-base, etc."				)
  parser.add_argument("--max_seq_len",						type=lowerCamelCase__,						default=1_2_8				)
  parser.add_argument("--data_dir",						type=lowerCamelCase__				)
  parser.add_argument("--save_path",						type=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = parser.parse_args()
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoTokenizer.from_pretrained(args.tok_name				)
  return pack_data_dir(lowerCamelCase__,						Path(args.data_dir				),						args.max_seq_len,						args.save_path				)


if __name__ == "__main__":
      packer_cli()



