

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        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




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

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

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

     # print & log results
     SCREAMING_SNAKE_CASE__					=					F'''WER: {wer_result}\nCER: {cer_result}'''
     print(_A       )

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

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

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

               result.map(_A   ,	with_indices=_A       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE__					=					re.sub(_A   ,	''''''   ,	text.lower()       )

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

     for t in token_sequences_to_ignore:
          SCREAMING_SNAKE_CASE__					=					''' '''.join(text.split(_A       )       )

     return text
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					load_dataset(args.dataset   ,	args.config   ,	split=args.split   ,	use_auth_token=_A       )

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

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

     # resample audio
     SCREAMING_SNAKE_CASE__					=					dataset.cast_column('''audio'''   ,	Audio(sampling_rate=_A       )       )

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

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

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

     # run inference on all examples
     SCREAMING_SNAKE_CASE__					=					dataset.map(_A   ,	remove_columns=dataset.column_names       )

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


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE  :						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.''',
      )
      _SCREAMING_SNAKE_CASE  :						List[Any]         =    parser.parse_args()

      main(args)





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						int         =    {'''vocab_file''': '''spiece.model'''}

_SCREAMING_SNAKE_CASE  :						int         =    {
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

_SCREAMING_SNAKE_CASE  :						List[Any]         =    {
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
_SCREAMING_SNAKE_CASE  :						Optional[int]         =    0
_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    1
_SCREAMING_SNAKE_CASE  :						int         =    2
_SCREAMING_SNAKE_CASE  :						str         =    3
_SCREAMING_SNAKE_CASE  :						str         =    4






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      VOCAB_FILES_NAMES
  a     =      PRETRAINED_VOCAB_FILES_MAP
  a     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     =      "left"






  def __init__(							self		:	Optional[int]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	List[str]=False			,     __lowerCamelCase		:	Optional[Any]=True			,     __lowerCamelCase		:	Optional[Any]=False			,     __lowerCamelCase		:	List[str]="<s>"			,     __lowerCamelCase		:	Any="</s>"			,     __lowerCamelCase		:	Tuple="<unk>"			,     __lowerCamelCase		:	List[str]="<sep>"			,     __lowerCamelCase		:	List[str]="<pad>"			,     __lowerCamelCase		:	int="<cls>"			,     __lowerCamelCase		:	Any="<mask>"			,     __lowerCamelCase		:	Dict=["<eop>", "<eod>"]			,     __lowerCamelCase		:	Optional[Dict[str, Any]] = None			,     **__lowerCamelCase		:	int			,     )	->							None:
       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE__					=					AddedToken(__lowerCamelCase			,     lstrip=__lowerCamelCase			,     rstrip=__lowerCamelCase      ) if isinstance(__lowerCamelCase			,     __lowerCamelCase      ) else mask_token

       SCREAMING_SNAKE_CASE__					=					{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=__lowerCamelCase			,     remove_space=__lowerCamelCase			,     keep_accents=__lowerCamelCase			,     bos_token=__lowerCamelCase			,     eos_token=__lowerCamelCase			,     unk_token=__lowerCamelCase			,     sep_token=__lowerCamelCase			,     pad_token=__lowerCamelCase			,     cls_token=__lowerCamelCase			,     mask_token=__lowerCamelCase			,     additional_special_tokens=__lowerCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__lowerCamelCase			,     )

       SCREAMING_SNAKE_CASE__					=					3

       SCREAMING_SNAKE_CASE__					=					do_lower_case
       SCREAMING_SNAKE_CASE__					=					remove_space
       SCREAMING_SNAKE_CASE__					=					keep_accents
       SCREAMING_SNAKE_CASE__					=					vocab_file

       SCREAMING_SNAKE_CASE__					=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(__lowerCamelCase      )






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							Union[str, Any]:
       return len(self.sp_model      )






  def    lowercase_	(							self		:	Tuple      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					{self.convert_ids_to_tokens(__lowerCamelCase      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab






  def __getstate__(							self		:	List[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.__dict__.copy()
       SCREAMING_SNAKE_CASE__					=					None
       return state






  def __setstate__(							self		:	Any			,     __lowerCamelCase		:	Tuple      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					d

       # for backward compatibility
       if not hasattr(self			,     '''sp_model_kwargs'''      ):
            SCREAMING_SNAKE_CASE__					=					{}

       SCREAMING_SNAKE_CASE__					=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(self.vocab_file      )






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Union[str, Any]      )	->							List[str]:
       if self.remove_space:
            SCREAMING_SNAKE_CASE__					=					''' '''.join(inputs.strip().split()      )
       else:
            SCREAMING_SNAKE_CASE__					=					inputs
       SCREAMING_SNAKE_CASE__					=					outputs.replace('''``'''			,     '''"'''      ).replace('''\'\''''			,     '''"'''      )

       if not self.keep_accents:
            SCREAMING_SNAKE_CASE__					=					unicodedata.normalize('''NFKD'''			,     __lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					''''''.join([c for c in outputs if not unicodedata.combining(__lowerCamelCase      )]      )
       if self.do_lower_case:
            SCREAMING_SNAKE_CASE__					=					outputs.lower()

       return outputs






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	str      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					self.preprocess_text(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.sp_model.encode(__lowerCamelCase			,     out_type=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					[]
       for piece in pieces:
            if len(__lowerCamelCase      ) > 1 and piece[-1] == str(''','''      ) and piece[-2].isdigit():
                 SCREAMING_SNAKE_CASE__					=					self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase			,     ''''''      )      )
                 if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                      if len(cur_pieces[0]      ) == 1:
                           SCREAMING_SNAKE_CASE__					=					cur_pieces[1:]
                      else:
                           SCREAMING_SNAKE_CASE__					=					cur_pieces[0][1:]
                 cur_pieces.append(piece[-1]      )
                 new_pieces.extend(__lowerCamelCase      )
            else:
                 new_pieces.append(__lowerCamelCase      )

       return new_pieces






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	Optional[int]      )	->							Optional[Any]:
       return self.sp_model.PieceToId(__lowerCamelCase      )






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Tuple      )	->							List[Any]:
       return self.sp_model.IdToPiece(__lowerCamelCase      )






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Tuple      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					''''''.join(__lowerCamelCase      ).replace(__lowerCamelCase			,     ''' '''      ).strip()
       return out_string






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	bool = True			,     **__lowerCamelCase		:	int			,     )	->							str:
       SCREAMING_SNAKE_CASE__					=					kwargs.pop('''use_source_tokenizer'''			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.convert_ids_to_tokens(__lowerCamelCase			,     skip_special_tokens=__lowerCamelCase      )

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       SCREAMING_SNAKE_CASE__					=					[]
       SCREAMING_SNAKE_CASE__					=					[]
       for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                 continue
            if token in self.added_tokens_encoder:
                 if current_sub_text:
                      sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase      )      )
                      SCREAMING_SNAKE_CASE__					=					[]
                 sub_texts.append(__lowerCamelCase      )
            else:
                 current_sub_text.append(__lowerCamelCase      )
       if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase      )      )

       # Mimic the behavior of the Rust tokenizer:
       # By default, there are no spaces between special tokens
       SCREAMING_SNAKE_CASE__					=					''''''.join(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
            SCREAMING_SNAKE_CASE__					=					self.clean_up_tokenization(__lowerCamelCase      )
            return clean_text
       else:
            return text






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       SCREAMING_SNAKE_CASE__					=					[self.sep_token_id]
       SCREAMING_SNAKE_CASE__					=					[self.cls_token_id]
       if token_ids_a is None:
            return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None			,     __lowerCamelCase		:	bool = False      )	->							List[int]:

       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__lowerCamelCase			,     token_ids_a=__lowerCamelCase			,     already_has_special_tokens=__lowerCamelCase      )

       if token_ids_a is not None:
            return ([0] * len(__lowerCamelCase      )) + [1] + ([0] * len(__lowerCamelCase      )) + [1, 1]
       return ([0] * len(__lowerCamelCase      )) + [1, 1]






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       SCREAMING_SNAKE_CASE__					=					[self.sep_token_id]
       SCREAMING_SNAKE_CASE__					=					[2]

       if token_ids_a is None:
            return len(token_ids_a + sep      ) * [0] + cls_segment_id
       return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1] + cls_segment_id







  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[str] = None      )	->							Tuple[str]:
       if not os.path.isdir(__lowerCamelCase      ):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
            return
       SCREAMING_SNAKE_CASE__					=					os.path.join(
           __lowerCamelCase			,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

       if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCamelCase      ) and os.path.isfile(self.vocab_file      ):
            copyfile(self.vocab_file			,     __lowerCamelCase      )
       elif not os.path.isfile(self.vocab_file      ):
            with open(__lowerCamelCase			,     '''wb'''      ) as fi:
                 SCREAMING_SNAKE_CASE__					=					self.sp_model.serialized_model_proto()
                 fi.write(__lowerCamelCase      )

       return (out_vocab_file,)


