


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       _lowerCAmelCase    (				lowerCAmelCase_				:Dataset		,    lowerCAmelCase_				:Dict[str, str]				)->Union[str, Any]:
   '''simple docstring'''






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

   # load metric
   snake_case_            =       load_metric("wer"				)
   snake_case_            =       load_metric("cer"				)

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

   # print & log results
   snake_case_            =       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:
      snake_case_            =       F'''log_{dataset_id}_predictions.txt'''
      snake_case_            =       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_				:Optional[int]		,    lowerCAmelCase_				:str				):
            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       _lowerCAmelCase    (				lowerCAmelCase_				:str				)->str:
   '''simple docstring'''






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

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

   for t in token_sequences_to_ignore:
      snake_case_            =       " ".join(text.split(lowerCAmelCase_				)				)

   return text



def       _lowerCAmelCase    (				lowerCAmelCase_				:int				)->Tuple:
   '''simple docstring'''






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

   # resample audio
   snake_case_            =       dataset.cast_column("audio"		,    Audio(sampling_rate=lowerCAmelCase_				)				)

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

   # map function to decode audio
   def map_to_pred(lowerCAmelCase_				:Any				):
      snake_case_            =       asr(
          batch["audio"]["array"]		,    chunk_length_s=args.chunk_length_s		,    stride_length_s=args.stride_length_s				)

      snake_case_            =       prediction["text"]
      snake_case_            =       normalize_text(batch["sentence"]				)
      return batch

   # run inference on all examples
   snake_case_            =       dataset.map(lowerCAmelCase_		,    remove_columns=dataset.column_names				)

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:List[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.''',
   )
   SCREAMING_SNAKE_CASE						:Optional[int]			=  parser.parse_args()

   main(args)








import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


SCREAMING_SNAKE_CASE						:str			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Dict			=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE						:str			=  {
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

SCREAMING_SNAKE_CASE						:Tuple			=  {
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE		     =							['input_ids', 'attention_mask']
  _SCREAMING_SNAKE_CASE		     =							RobertaTokenizer



  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							Union[str, Any]=None     ,					_lowerCAmelCase							:							Tuple=None     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							Optional[Any]="replace"     ,					_lowerCAmelCase							:							str="<s>"     ,					_lowerCAmelCase							:							Tuple="</s>"     ,					_lowerCAmelCase							:							Any="</s>"     ,					_lowerCAmelCase							:							Tuple="<s>"     ,					_lowerCAmelCase							:							Any="<unk>"     ,					_lowerCAmelCase							:							Tuple="<pad>"     ,					_lowerCAmelCase							:							List[str]="<mask>"     ,					_lowerCAmelCase							:							str=False     ,					_lowerCAmelCase							:							Union[str, Any]=True     ,					**_lowerCAmelCase							:							int     ,					) ->			str:




     """simple docstring"""
     super().__init__(
         _lowerCAmelCase     ,					_lowerCAmelCase     ,					tokenizer_file=_lowerCAmelCase     ,					errors=_lowerCAmelCase     ,					bos_token=_lowerCAmelCase     ,					eos_token=_lowerCAmelCase     ,					sep_token=_lowerCAmelCase     ,					cls_token=_lowerCAmelCase     ,					unk_token=_lowerCAmelCase     ,					pad_token=_lowerCAmelCase     ,					mask_token=_lowerCAmelCase     ,					add_prefix_space=_lowerCAmelCase     ,					trim_offsets=_lowerCAmelCase     ,					**_lowerCAmelCase     ,					)

     snake_case_            =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
     if pre_tok_state.get("add_prefix_space"     ,					_lowerCAmelCase	) != add_prefix_space:
        snake_case_            =       getattr(_lowerCAmelCase     ,					pre_tok_state.pop("type"	)	)
        snake_case_            =       add_prefix_space
        snake_case_            =       pre_tok_class(**_lowerCAmelCase	)

     snake_case_            =       add_prefix_space

     snake_case_            =       "post_processor"
     snake_case_            =       getattr(self.backend_tokenizer     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
     if tokenizer_component_instance:
        snake_case_            =       json.loads(tokenizer_component_instance.__getstate__()	)

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
           snake_case_            =       tuple(state["sep"]	)
        if "cls" in state:
           snake_case_            =       tuple(state["cls"]	)

        snake_case_            =       False

        if state.get("add_prefix_space"     ,					_lowerCAmelCase	) != add_prefix_space:
           snake_case_            =       add_prefix_space
           snake_case_            =       True

        if state.get("trim_offsets"     ,					_lowerCAmelCase	) != trim_offsets:
           snake_case_            =       trim_offsets
           snake_case_            =       True

        if changes_to_apply:
           snake_case_            =       getattr(_lowerCAmelCase     ,					state.pop("type"	)	)
           snake_case_            =       component_class(**_lowerCAmelCase	)
           setattr(self.backend_tokenizer     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)



  @property
  def        lowerCAmelCase__	(  self							:							Dict	) ->			str:




     """simple docstring"""
     if self._mask_token is None:
        if self.verbose:
           logger.error("Using mask_token, but it is not set yet."	)
        return None
     return str(self._mask_token	)



  @mask_token.setter
  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Tuple	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       AddedToken(_lowerCAmelCase     ,					lstrip=_lowerCAmelCase     ,					rstrip=_lowerCAmelCase	) if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) else value
     snake_case_            =       value



  def        lowerCAmelCase__	(  self							:							Dict     ,					*_lowerCAmelCase							:							Optional[int]     ,					**_lowerCAmelCase							:							Optional[int]	) ->			BatchEncoding:




     """simple docstring"""
     snake_case_            =       kwargs.get("is_split_into_words"     ,					_lowerCAmelCase	)
     assert self.add_prefix_space or not is_split_into_words, (
         F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         "to use it with pretokenized inputs."
     )

     return super()._batch_encode_plus(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					*_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Any	) ->			BatchEncoding:




     """simple docstring"""
     snake_case_            =       kwargs.get("is_split_into_words"     ,					_lowerCAmelCase	)

     assert self.add_prefix_space or not is_split_into_words, (
         F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         "to use it with pretokenized inputs."
     )

     return super()._encode_plus(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[str] = None	) ->			Tuple[str]:




     """simple docstring"""
     snake_case_            =       self._tokenizer.model.save(_lowerCAmelCase     ,					name=_lowerCAmelCase	)
     return tuple(_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							List[Any]=None	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
        return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[int]     ,					_lowerCAmelCase							:							Optional[List[int]] = None	) ->			List[int]:




     """simple docstring"""
     snake_case_            =       [self.sep_token_id]
     snake_case_            =       [self.cls_token_id]

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




