


"""simple docstring"""







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 					a__			(				SCREAMING_SNAKE_CASE    :    Dataset				,      SCREAMING_SNAKE_CASE    :    Dict[str, str]				):


  '''simple docstring'''







  lowerCAmelCase     :     Dict								=							args.log_outputs
  lowerCAmelCase     :     List[str]								=							"_".join(args.dataset.split("/"				) + [args.config, args.split]				)

  # load metric
  lowerCAmelCase     :     int								=							load_metric("wer"				)
  lowerCAmelCase     :     str								=							load_metric("cer"				)

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

  # print & log results
  lowerCAmelCase     :     List[str]								=							f"""WER: {wer_result}\nCER: {cer_result}"""
  print(SCREAMING_SNAKE_CASE				)

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

  # log all results in text file. Possibly interesting for analysis
  if log_outputs is not None:
    lowerCAmelCase     :     Optional[Any]								=							f"""log_{dataset_id}_predictions.txt"""
    lowerCAmelCase     :     Tuple								=							f"""log_{dataset_id}_targets.txt"""

    with open(SCREAMING_SNAKE_CASE				,      "w"				) as p, open(SCREAMING_SNAKE_CASE				,      "w"				) as t:
      # mapping function to write output
      def write_to_file(SCREAMING_SNAKE_CASE    :    Optional[int]				,      SCREAMING_SNAKE_CASE    :    List[str]				):
        p.write(f"""{i}""" + "\n"				)
        p.write(batch["prediction"] + "\n"				)
        t.write(f"""{i}""" + "\n"				)
        t.write(batch["target"] + "\n"				)

      result.map(SCREAMING_SNAKE_CASE				,      with_indices=SCREAMING_SNAKE_CASE				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				):


  '''simple docstring'''







  lowerCAmelCase     :     List[str]								=							"[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

  lowerCAmelCase     :     List[str]								=							re.sub(SCREAMING_SNAKE_CASE				,      ""				,      text.lower()				)

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

  for t in token_sequences_to_ignore:
    lowerCAmelCase     :     List[str]								=							" ".join(text.split(SCREAMING_SNAKE_CASE				)				)

  return text
def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				):


  '''simple docstring'''







  lowerCAmelCase     :     Any								=							load_dataset(args.dataset				,      args.config				,      split=args.split				,      use_auth_token=SCREAMING_SNAKE_CASE				)

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

  # load processor
  lowerCAmelCase     :     Tuple								=							AutoFeatureExtractor.from_pretrained(args.model_id				)
  lowerCAmelCase     :     Dict								=							feature_extractor.sampling_rate

  # resample audio
  lowerCAmelCase     :     Any								=							dataset.cast_column("audio"				,      Audio(sampling_rate=SCREAMING_SNAKE_CASE				)				)

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

  # map function to decode audio
  def map_to_pred(SCREAMING_SNAKE_CASE    :    Optional[int]				):
    lowerCAmelCase     :     Union[str, Any]								=							asr(
        batch["audio"]["array"]				,      chunk_length_s=args.chunk_length_s				,      stride_length_s=args.stride_length_s				)

    lowerCAmelCase     :     List[Any]								=							prediction["text"]
    lowerCAmelCase     :     int								=							normalize_text(batch["sentence"]				)
    return batch

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

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


if __name__ == "__main__":
     lowerCAmelCase__      				=   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.''',
     )
     lowerCAmelCase__      				=   parser.parse_args()

     main(args)









"""simple docstring"""







import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   '''T5Config'''
def 					a__			(				SCREAMING_SNAKE_CASE    :    jnp.array				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     List[str]								=							jnp.zeros_like(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Optional[int]								=							shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]				)
  lowerCAmelCase     :     List[str]								=							shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     str								=							jnp.where(shifted_input_ids == -1_0_0				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  return shifted_input_ids


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				List[Any]   ="mt5"
       a  :				Tuple   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Union[str, Any]   ="mt5"
       a  :				Optional[Any]   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="mt5"
       a  :				Dict   =MTaConfig





