





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						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							args.log_outputs
   lowerCamelCase__       :						Optional[Any]        =							'''_'''.join(args.dataset.split('''/'''					) + [args.config, args.split]					)

   # load metric
   lowerCamelCase__       :						Tuple        =							load_metric('''wer'''					)
   lowerCamelCase__       :						Any        =							load_metric('''cer'''					)

   # compute metrics
   lowerCamelCase__       :						Optional[int]        =							wer.compute(references=result['''target''']      ,  predictions=result['''prediction''']					)
   lowerCamelCase__       :						Tuple        =							cer.compute(references=result['''target''']      ,  predictions=result['''prediction''']					)

   # print & log results
   lowerCamelCase__       :						Any        =							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__       :						Optional[int]        =							f"log_{dataset_id}_predictions.txt"
      lowerCamelCase__       :						Union[str, Any]        =							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 	_a						(   UpperCAmelCase					)    ->    str:







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

   lowerCamelCase__       :						Dict        =							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__       :						Optional[int]        =							['''\n\n''', '''\n''', '''   ''', '''  ''']

   for t in token_sequences_to_ignore:
      lowerCamelCase__       :						Union[str, Any]        =							''' '''.join(text.split(UpperCAmelCase					)					)

   return text



def 	_a						(   UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   # load dataset
   lowerCamelCase__       :						Optional[Any]        =							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__       :						Dict        =							AutoFeatureExtractor.from_pretrained(args.model_id					)
   lowerCamelCase__       :						Union[str, Any]        =							feature_extractor.sampling_rate

   # resample audio
   lowerCamelCase__       :						Any        =							dataset.cast_column('''audio'''      ,  Audio(sampling_rate=UpperCAmelCase					)					)

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

   # map function to decode audio
   def map_to_pred(UpperCAmelCase					):
      lowerCamelCase__       :						Any        =							asr(
          batch['''audio''']['''array''']      ,  chunk_length_s=args.chunk_length_s      ,  stride_length_s=args.stride_length_s					)

      lowerCamelCase__       :						Tuple        =							prediction['''text''']
      lowerCamelCase__       :						int        =							normalize_text(batch['''sentence''']					)
      return batch

   # run inference on all examples
   lowerCamelCase__       :						Tuple        =							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 :   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.',
       )
       _A :   List[Any]  			=  parser.parse_args()

       main(args)










from math import ceil, sqrt



def 	_a						(   UpperCAmelCase = 1000000					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Any        =							0

   for outer_width in range(3      ,  (limit // 4) + 2					):
      if outer_width**2 > limit:
         lowerCamelCase__       :						List[Any]        =							max(ceil(sqrt(outer_width**2 - limit					)					)      ,  1					)
      else:
         lowerCamelCase__       :						Union[str, Any]        =							1
      if (outer_width - hole_width_lower_bound) % 2:
         hole_width_lower_bound += 1

      answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

   return answer


if __name__ == "__main__":
       print(F'''{solution() = }''')



