




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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dataset	,							lowercase	:       Dict[str, str]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    args.log_outputs
      lowerCamelCase_		     =    '_'.join(args.dataset.split('/'     ) + [args.config, args.split]     )

      # load metric
      lowerCamelCase_		     =    load_metric('wer'     )
      lowerCamelCase_		     =    load_metric('cer'     )

      # compute metrics
      lowerCamelCase_		     =    wer.compute(references=result['target']	,							predictions=result['prediction']     )
      lowerCamelCase_		     =    cer.compute(references=result['target']	,							predictions=result['prediction']     )

      # print & log results
      lowerCamelCase_		     =    f"""WER: {wer_result}\nCER: {cer_result}"""
      print(lowercase     )

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

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

            with open(lowercase	,							'w'     ) as p, open(lowercase	,							'w'     ) as t:
                  # mapping function to write output
                  def write_to_file(lowercase	:       Optional[Any]	,							lowercase	:       Dict     ):
                        p.write(f"""{i}""" + '\n'     )
                        p.write(batch['prediction'] + '\n'     )
                        t.write(f"""{i}""" + '\n'     )
                        t.write(batch['target'] + '\n'     )

                  result.map(lowercase	,							with_indices=lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str     ):


      '''simple docstring'''



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

      lowerCamelCase_		     =    re.sub(lowercase	,							''	,							text.lower()     )

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

      for t in token_sequences_to_ignore:
            lowerCamelCase_		     =    ' '.join(text.split(lowercase     )     )

      return text

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str     ):


      '''simple docstring'''



      lowerCamelCase_		     =    load_dataset(args.dataset	,							args.config	,							split=args.split	,							use_auth_token=lowercase     )

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

      # load processor
      lowerCamelCase_		     =    AutoFeatureExtractor.from_pretrained(args.model_id     )
      lowerCamelCase_		     =    feature_extractor.sampling_rate

      # resample audio
      lowerCamelCase_		     =    dataset.cast_column('audio'	,							Audio(sampling_rate=lowercase     )     )

      # load eval pipeline
      if args.device is None:
            lowerCamelCase_		     =    0 if torch.cuda.is_available() else -1
      lowerCamelCase_		     =    pipeline('automatic-speech-recognition'	,							model=args.model_id	,							device=args.device     )

      # map function to decode audio
      def map_to_pred(lowercase	:       Optional[int]     ):
            lowerCamelCase_		     =    asr(
                batch['audio']['array']	,							chunk_length_s=args.chunk_length_s	,							stride_length_s=args.stride_length_s     )

            lowerCamelCase_		     =    prediction['text']
            lowerCamelCase_		     =    normalize_text(batch['sentence']     )
            return batch

      # run inference on all examples
      lowerCamelCase_		     =    dataset.map(lowercase	,							remove_columns=dataset.column_names     )

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


if __name__ == "__main__":
 lowerCamelCase				:		Union[str, 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.",
 )
 lowerCamelCase				:		Optional[int]     =   parser.parse_args()

 main(args)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:		Optional[Any]     =   {
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   ["WhisperTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "WhisperForConditionalGeneration",
     "WhisperModel",
     "WhisperPreTrainedModel",
     "WhisperForAudioClassification",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFWhisperForConditionalGeneration",
     "TFWhisperModel",
     "TFWhisperPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "FlaxWhisperForConditionalGeneration",
     "FlaxWhisperModel",
     "FlaxWhisperPreTrainedModel",
     "FlaxWhisperForAudioClassification",
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:		Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



