"""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_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							args.log_outputs
				UpperCAmelCase__             =							'_'.join(args.dataset.split('/'       ) + [args.config, args.split]       )

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

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

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

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

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

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

												result.map(lowerCamelCase					,			with_indices=lowerCamelCase       )



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

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

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

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

				return text



def   a_						(					lowerCamelCase       ):
				# load dataset
				UpperCAmelCase__             =							load_dataset(args.dataset					,			args.config					,			split=args.split					,			use_auth_token=lowerCamelCase       )

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

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

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

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

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

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

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

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


if __name__ == "__main__":
						lowerCAmelCase__  :  Dict						     =	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__  :  Dict						     =	parser.parse_args()

						main(args)





import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowercase__       ={
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
lowercase__       ='ETAOINSHRDLCUMWFGYPBVKJXQZ'
lowercase__       ='ABCDEFGHIJKLMNOPQRSTUVWXYZ'




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  List[Any]						=  {letter: 0 for letter in string.ascii_uppercase}
 for letter in message.upper():
  if letter in LETTERS:
   letter_count[letter] += 1

 return letter_count




def    __UpperCamelCase     (				lowerCAmelCase__							:				tuple  ):
 return x[0]




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  Optional[Any]						=  get_letter_count(lowerCAmelCase__  )
 __a :  dict[int, list[str]]						=  {
     freq: [] for letter, freq in letter_to_freq.items()
 }
 for letter in LETTERS:
  freq_to_letter[letter_to_freq[letter]].append(lowerCAmelCase__  )

 __a :  dict[int, str]						=  {}

 for freq in freq_to_letter:
  freq_to_letter[freq].sort(key=ETAOIN.find  ,   reverse=lowerCAmelCase__  )
  __a :  int						=  ''''''.join(freq_to_letter[freq]  )

 __a :  Any						=  list(freq_to_letter_str.items()  )
 freq_pairs.sort(key=lowerCAmelCase__  ,   reverse=lowerCAmelCase__  )

 __a :  list[str]						=  [freq_pair[1] for freq_pair in freq_pairs]

 return "".join(lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  int						=  get_frequency_order(lowerCAmelCase__  )
 __a :  str						=  0
 for common_letter in ETAOIN[:6]:
  if common_letter in freq_order[:6]:
   match_score += 1

 for uncommon_letter in ETAOIN[-6:]:
  if uncommon_letter in freq_order[-6:]:
   match_score += 1

 return match_score


if __name__ == "__main__":
      import doctest

      doctest.testmod()
