



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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """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(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:
            lowerCamelCase             =		F"""log_{dataset_id}_predictions.txt"""
            lowerCamelCase             =		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(UpperCAmelCase__ , UpperCAmelCase__			):
                        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      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



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

      lowerCamelCase             =		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!
      lowerCamelCase             =		["\n\n", "\n", "   ", "  "]

      for t in token_sequences_to_ignore:
            lowerCamelCase             =		" ".join(text.split(UpperCAmelCase__			)			)

      return text

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		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
      lowerCamelCase             =		AutoFeatureExtractor.from_pretrained(args.model_id			)
      lowerCamelCase             =		feature_extractor.sampling_rate

      # resample audio
      lowerCamelCase             =		dataset.cast_column("audio" , Audio(sampling_rate=UpperCAmelCase__			)			)

      # 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(UpperCAmelCase__			):
            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(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_    :  str						  =		parser.parse_args()

     main(args)




from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
     import tensorflow as tf


a_    :  Any						  =		logging.get_logger(__name__)





@dataclass
class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      [
         'no_inference',
         'no_cuda',
         'no_tpu',
         'no_speed',
         'no_memory',
         'no_env_print',
         'no_multi_process',
     ]







     def __init__(self					,      **__a     ):






           '''simple docstring'''



           for deprecated_arg in self.deprecated_args:
                 if deprecated_arg in kwargs:
                       lowerCamelCase             =		deprecated_arg[3:]
                       lowerCamelCase             =		not kwargs.pop(__a     )
                       logger.warning(
                           F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
                           F""" {positive_arg}={kwargs[positive_arg]}"""     )
           lowerCamelCase             =		kwargs.pop("tpu_name"					,      self.tpu_name     )
           lowerCamelCase             =		kwargs.pop("device_idx"					,      self.device_idx     )
           lowerCamelCase             =		kwargs.pop("eager_mode"					,      self.eager_mode     )
           lowerCamelCase             =		kwargs.pop("use_xla"					,      self.use_xla     )
           super().__init__(**__a     )

     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Name of TPU'}							,     )
     _A      =      field(
         default=0							,     metadata={'help': 'CPU / GPU device index. Defaults to 0.'}							,     )
     _A      =      field(default=UpperCAmelCase_							,     metadata={'help': 'Benchmark models in eager model.'})
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={
             'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
         }							,     )







     @cached_property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           lowerCamelCase             =		None
           if self.tpu:
                 try:
                       if self.tpu_name:
                             lowerCamelCase             =		tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name     )
                       else:
                             lowerCamelCase             =		tf.distribute.cluster_resolver.TPUClusterResolver()
                 except ValueError:
                       lowerCamelCase             =		None
           return tpu







     @cached_property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           if self.is_tpu:
                 tf.config.experimental_connect_to_cluster(self._setup_tpu     )
                 tf.tpu.experimental.initialize_tpu_system(self._setup_tpu     )

                 lowerCamelCase             =		tf.distribute.TPUStrategy(self._setup_tpu     )
           else:
                 # currently no multi gpu is allowed
                 if self.is_gpu:
                       # TODO: Currently only single GPU is supported
                       tf.config.set_visible_devices(self.gpu_list[self.device_idx]					,      "GPU"     )
                       lowerCamelCase             =		tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""     )
                 else:
                       tf.config.set_visible_devices([]					,      "GPU"     )  # disable GPU
                       lowerCamelCase             =		tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""     )

           return strategy







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return self._setup_tpu is not None







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return self._setup_strategy







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return tf.config.list_physical_devices("GPU"     )







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           if self.cuda:
                 return len(self.gpu_list     )
           return 0







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self.n_gpu > 0