


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				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :int		)   ->  Any:




     '''simple docstring'''



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

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

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

     # print & log results
     _a			      =							f'WER: {wer_result}\nCER: {cer_result}'
     print(UpperCAmelCase__		)

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

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

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

               result.map(UpperCAmelCase__				,						with_indices=UpperCAmelCase__		)






def 	_A				(lowerCAmelCase__  :Dict		)   ->  List[Any]:




     '''simple docstring'''



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

     _a			      =							re.sub(UpperCAmelCase__				,						''				,						text.lower()		)

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

     for t in token_sequences_to_ignore:
          _a			      =							' '.join(text.split(UpperCAmelCase__		)		)

     return text






def 	_A				(lowerCAmelCase__  :Dict		)   ->  Union[str, Any]:




     '''simple docstring'''



     _a			      =							load_dataset(args.dataset				,						args.config				,						split=args.split				,						use_auth_token=UpperCAmelCase__		)

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

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

     # resample audio
     _a			      =							dataset.cast_column('audio'				,						Audio(sampling_rate=UpperCAmelCase__		)		)

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

     # map function to decode audio
     def map_to_pred(lowerCAmelCase__  :List[str]		):
          _a			      =							asr(
              batch['audio']['array']				,						chunk_length_s=args.chunk_length_s				,						stride_length_s=args.stride_length_s		)

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

     # run inference on all examples
     _a			      =							dataset.map(UpperCAmelCase__				,						remove_columns=dataset.column_names		)

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


if __name__ == "__main__":
 a_					:    int          =    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.",
 )
 a_					:    Tuple          =    parser.parse_args()

 main(args)





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_					:    str          =    {
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[str]          =    [
     "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecAudioForAudioFrameClassification",
     "Data2VecAudioForCTC",
     "Data2VecAudioForSequenceClassification",
     "Data2VecAudioForXVector",
     "Data2VecAudioModel",
     "Data2VecAudioPreTrainedModel",
 ]
 a_					:    str          =    [
     "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecTextForCausalLM",
     "Data2VecTextForMaskedLM",
     "Data2VecTextForMultipleChoice",
     "Data2VecTextForQuestionAnswering",
     "Data2VecTextForSequenceClassification",
     "Data2VecTextForTokenClassification",
     "Data2VecTextModel",
     "Data2VecTextPreTrainedModel",
 ]
 a_					:    Tuple          =    [
     "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecVisionForImageClassification",
     "Data2VecVisionForMaskedImageModeling",
     "Data2VecVisionForSemanticSegmentation",
     "Data2VecVisionModel",
     "Data2VecVisionPreTrainedModel",
 ]

if is_tf_available():
 a_					:    Dict          =    [
     "TFData2VecVisionForImageClassification",
     "TFData2VecVisionForSemanticSegmentation",
     "TFData2VecVisionModel",
     "TFData2VecVisionPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
 from .configuration_dataavec_text import (
     DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecTextConfig,
     DataaVecTextOnnxConfig,
 )
 from .configuration_dataavec_vision import (
     DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecVisionConfig,
     DataaVecVisionOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dataavec_audio import (
      DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecAudioForAudioFrameClassification,
      DataaVecAudioForCTC,
      DataaVecAudioForSequenceClassification,
      DataaVecAudioForXVector,
      DataaVecAudioModel,
      DataaVecAudioPreTrainedModel,
  )
  from .modeling_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecTextForCausalLM,
      DataaVecTextForMaskedLM,
      DataaVecTextForMultipleChoice,
      DataaVecTextForQuestionAnswering,
      DataaVecTextForSequenceClassification,
      DataaVecTextForTokenClassification,
      DataaVecTextModel,
      DataaVecTextPreTrainedModel,
  )
  from .modeling_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecVisionForImageClassification,
      DataaVecVisionForMaskedImageModeling,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
      DataaVecVisionPreTrainedModel,
  )
 if is_tf_available():
  from .modeling_tf_dataavec_vision import (
      TFDataaVecVisionForImageClassification,
      TFDataaVecVisionForSemanticSegmentation,
      TFDataaVecVisionModel,
      TFDataaVecVisionPreTrainedModel,
  )

else:
 import sys

 a_					:    List[Any]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

