




import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:str							=  args.log_outputs
  lowercase			:Union[str, Any]							=  "_".join(args.dataset.split("/"      ) + [args.config, args.split]      )

  # load metric
  lowercase			:Optional[int]							=  load_metric("wer"      )
  lowercase			:List[Any]							=  load_metric("cer"      )

  # compute metrics
  lowercase			:int							=  wer.compute(references=result["target"],	predictions=result["prediction"]      )
  lowercase			:List[str]							=  cer.compute(references=result["target"],	predictions=result["prediction"]      )

  # print & log results
  lowercase			:int							=  F"WER: {wer_result}\nCER: {cer_result}"
  print(lowerCamelCase      )

  with open(F"{dataset_id}_eval_results.txt",	"w"      ) as f:
    f.write(lowerCamelCase      )

  # log all results in text file. Possibly interesting for analysis
  if log_outputs is not None:
    lowercase			:int							=  F"log_{dataset_id}_predictions.txt"
    lowercase			:int							=  F"log_{dataset_id}_targets.txt"

    with open(lowerCamelCase,	"w"      ) as p, open(lowerCamelCase,	"w"      ) as t:
      # mapping function to write output
      def write_to_file(lowerCamelCase,	lowerCamelCase      ):
        p.write(F"{i}" + "\n"      )
        p.write(batch["prediction"] + "\n"      )
        t.write(F"{i}" + "\n"      )
        t.write(batch["target"] + "\n"      )

      result.map(lowerCamelCase,	with_indices=lowerCamelCase      )



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:Tuple							=  "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

  lowercase			:int							=  re.sub(lowerCamelCase,	"",	text.lower()      )

  # In addition, we can normalize the target text, e.g. removing new lines characters etc...
  # note that order is important here!
  lowercase			:List[str]							=  ["\n\n", "\n", "   ", "  "]

  for t in token_sequences_to_ignore:
    lowercase			:Optional[int]							=  " ".join(text.split(lowerCamelCase      )      )

  return text



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  # load dataset
  lowercase			:Optional[Any]							=  load_dataset(args.dataset,	args.config,	split=args.split,	use_auth_token=lowerCamelCase      )

  # for testing: only process the first two examples as a test
  # dataset = dataset.select(range(10))

  # load processor
  lowercase			:Any							=  AutoFeatureExtractor.from_pretrained(args.model_id      )
  lowercase			:Optional[int]							=  feature_extractor.sampling_rate

  # resample audio
  lowercase			:Optional[int]							=  dataset.cast_column("audio",	Audio(sampling_rate=lowerCamelCase      )      )

  # load eval pipeline
  if args.device is None:
    lowercase			:Tuple							=  0 if torch.cuda.is_available() else -1
  lowercase			:List[Any]							=  pipeline("automatic-speech-recognition",	model=args.model_id,	device=args.device      )

  # map function to decode audio
  def map_to_pred(lowerCamelCase      ):
    lowercase			:Tuple							=  asr(
        batch["audio"]["array"],	chunk_length_s=args.chunk_length_s,	stride_length_s=args.stride_length_s      )

    lowercase			:Tuple							=  prediction["text"]
    lowercase			:Optional[Any]							=  normalize_text(batch["sentence"]      )
    return batch

  # run inference on all examples
  lowercase			:List[str]							=  dataset.map(lowerCamelCase,	remove_columns=dataset.column_names      )

  # compute and log_results
  # do not change function below
  log_results(lowerCamelCase,	lowerCamelCase      )


if __name__ == "__main__":
     _UpperCAmelCase		:  Any	=   argparse.ArgumentParser()

     parser.add_argument(
         "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
     )
     parser.add_argument(
         "--dataset",
         type=str,
         required=True,
         help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
     )
     parser.add_argument(
         "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
     )
     parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
     parser.add_argument(
         "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
     )
     parser.add_argument(
         "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
     )
     parser.add_argument(
         "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
     )
     parser.add_argument(
         "--device",
         type=int,
         default=None,
         help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
     )
     _UpperCAmelCase		:  Tuple	=   parser.parse_args()

     main(args)









from typing import Union

import fire
import torch
from tqdm import tqdm



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase = "cpu",	lowerCamelCase = None      ):
  lowercase			:Optional[Any]							=  torch.load(lowerCamelCase,	map_location=lowerCamelCase      )
  for k, v in tqdm(state_dict.items()      ):
    if not isinstance(lowerCamelCase,	torch.Tensor      ):
      raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"      )
    lowercase			:List[Any]							=  v.half()
  if save_path is None:  # overwrite src_path
    lowercase			:Optional[Any]							=  src_path
  torch.save(lowerCamelCase,	lowerCamelCase      )


if __name__ == "__main__":
     fire.Fire(convert)



