






'''simple docstring'''

import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase							=logging.get_logger(__name__)


UpperCamelCase							={
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

UpperCamelCase							={
    "vocab_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"
        ),
    },
    "tokenizer_config_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"
        ),
    },
    "merges_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"
        ),
    },
}

UpperCamelCase							="</w>"
UpperCamelCase							="@@ "



def 							snake_case					(     a_							: Tuple	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							set()
      UpperCamelCase_				:   Union[str, Any]					    =							word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)	)
            UpperCamelCase_				:   List[Any]					    =							char
      return pairs


# Speech2Text2 has no max input length
UpperCamelCase							={"facebook/s2t-wav2vec2-large-en-de": 1024}




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Optional[int]      =						VOCAB_FILES_NAMES
 __a			:						int      =						PRETRAINED_VOCAB_FILES_MAP
 __a			:						List[str]      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __a			:						Tuple      =						['''input_ids''', '''attention_mask''']
 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase="<s>"			,     __lowerCAmelCase="<pad>"			,     __lowerCAmelCase="</s>"			,     __lowerCAmelCase="<unk>"			,     __lowerCAmelCase=False			,     __lowerCAmelCase=None			,     **__lowerCAmelCase			,     ):
       super().__init__(
           unk_token=__lowerCAmelCase			,     bos_token=__lowerCAmelCase			,     eos_token=__lowerCAmelCase			,     pad_token=__lowerCAmelCase			,     do_lower_case=__lowerCAmelCase			,     **__lowerCAmelCase			,     )

       UpperCamelCase_				:   List[Any]					    =							do_lower_case

       with open(__lowerCAmelCase			,     encoding="""utf-8""" ) as vocab_handle:
             UpperCamelCase_				:   Any					    =							json.load(__lowerCAmelCase )
       UpperCamelCase_				:   List[Any]					    =							{v: k for k, v in self.encoder.items()}

       if merges_file is None:
             logger.info(F"No merges files provided. {self.__class__.__name__} can only be used for decoding." )

             UpperCamelCase_				:   Union[str, Any]					    =							None
             UpperCamelCase_				:   Any					    =							None
       else:
             with open(__lowerCAmelCase			,     encoding="""utf-8""" ) as merges_handle:
                   UpperCamelCase_				:   Any					    =							merges_handle.read().split("""\n""" )[:-1]

             UpperCamelCase_				:   str					    =							[tuple(merge.split()[:2] ) for merge in merges]
             UpperCamelCase_				:   Tuple					    =							dict(zip(__lowerCAmelCase			,     range(len(__lowerCAmelCase ) ) ) )
             UpperCamelCase_				:   List[str]					    =							{}
 @property
 def   _UpperCAmelCase       (							self ):
       return len(self.decoder )
 def   _UpperCAmelCase       (							self ):
       return dict(self.encoder			,     **self.added_tokens_encoder )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[Any]					    =							tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
       if token in self.cache:
             return self.cache[token]
       UpperCamelCase_				:   List[str]					    =							get_pairs(__lowerCAmelCase )

       if not pairs:
             return token

       while True:
             UpperCamelCase_				:   str					    =							min(__lowerCAmelCase			,     key=lambda __lowerCAmelCase : self.bpe_ranks.get(__lowerCAmelCase			,     float("""inf""" ) ) )
             if bigram not in self.bpe_ranks:
                   break
             UpperCamelCase_    ,			UpperCamelCase_				:   List[Any]					    =							bigram
             UpperCamelCase_				:   List[str]					    =							[]
             UpperCamelCase_				:   List[str]					    =							0
             while i < len(__lowerCAmelCase ):
                   try:
                         UpperCamelCase_				:   Tuple					    =							word.index(__lowerCAmelCase			,     __lowerCAmelCase )
                   except ValueError:
                         new_word.extend(word[i:] )
                         break
                   else:
                         new_word.extend(word[i:j] )
                         UpperCamelCase_				:   Optional[int]					    =							j

                   if word[i] == first and i < len(__lowerCAmelCase ) - 1 and word[i + 1] == second:
                         new_word.append(first + second )
                         i += 2
                   else:
                         new_word.append(word[i] )
                         i += 1
             UpperCamelCase_				:   int					    =							tuple(__lowerCAmelCase )
             UpperCamelCase_				:   int					    =							new_word
             if len(__lowerCAmelCase ) == 1:
                   break
             else:
                   UpperCamelCase_				:   Optional[Any]					    =							get_pairs(__lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							""" """.join(__lowerCAmelCase )
       if word == "\n  " + BPE_TOKEN_MERGES:
             UpperCamelCase_				:   List[str]					    =							"""\n""" + BPE_TOKEN_MERGES

       if word.endswith(__lowerCAmelCase ):
             UpperCamelCase_				:   Any					    =							word.replace(__lowerCAmelCase			,     """""" )

       UpperCamelCase_				:   Union[str, Any]					    =							word.replace(""" """			,     __lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							word
       return word
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):

       if self.bpe_ranks is None:
             raise ValueError(
                 """This tokenizer was instantiated without a `merges.txt` file, so"""
                 """ that it can only be used for decoding, not for encoding."""
                 """Make sure to provide `merges.txt` file at instantiation to enable """
                 """encoding.""" )

       if self.do_lower_case:
             UpperCamelCase_				:   Tuple					    =							text.lower()

       UpperCamelCase_				:   Any					    =							text.split()

       UpperCamelCase_				:   Optional[int]					    =							[]
       for token in text:
             if token:
                   split_tokens.extend(list(self.bpe(__lowerCAmelCase ).split(""" """ ) ) )

       return split_tokens
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       return self.encoder.get(__lowerCAmelCase			,     self.encoder.get(self.unk_token ) )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   int					    =							self.decoder.get(__lowerCAmelCase			,     self.unk_token )
       return result
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							""" """.join(__lowerCAmelCase )

       # make sure @@ tokens are concatenated
       UpperCamelCase_				:   Tuple					    =							"""""".join(string.split(__lowerCAmelCase ) )

       return string





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       if not os.path.isdir(__lowerCAmelCase ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
       UpperCamelCase_				:   Tuple					    =							os.path.join(
           __lowerCAmelCase			,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
       UpperCamelCase_				:   List[Any]					    =							os.path.join(
           __lowerCAmelCase			,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )

       with open(__lowerCAmelCase			,     """w"""			,     encoding="""utf-8""" ) as f:
             f.write(json.dumps(self.encoder			,     indent=2			,     sort_keys=__lowerCAmelCase			,     ensure_ascii=__lowerCAmelCase ) + """\n""" )

       UpperCamelCase_				:   List[Any]					    =							0
       if self.bpe_ranks is None:
             return (vocab_file,)

       with open(__lowerCAmelCase			,     """w"""			,     encoding="""utf-8""" ) as writer:
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,     key=lambda __lowerCAmelCase : kv[1] ):
                   if index != token_index:
                         logger.warning(
                             F"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."
                             """ Please check that the tokenizer is not corrupted!""" )
                         UpperCamelCase_				:   List[Any]					    =							token_index
                   writer.write(""" """.join(__lowerCAmelCase ) + """\n""" )
                   index += 1

       return (vocab_file, merges_file)














'''simple docstring'''

from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
      import importlib_metadata
else:
      import importlib.metadata as importlib_metadata


UpperCamelCase							=""


if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):




      class     A    (      tr.AbstractTransform						):






       """simple docstring"""





       def __init__(							self			,     __lowerCAmelCase = " " ):
             UpperCamelCase_				:   str					    =							sentence_delimiter
       def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
             return list(__lowerCAmelCase )





       def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
             UpperCamelCase_				:   List[Any]					    =							[]
             for sent_idx, sentence in enumerate(__lowerCAmelCase ):
                   chars.extend(self.process_string(__lowerCAmelCase ) )
                   if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowerCAmelCase ) - 1:
                         chars.append(self.sentence_delimiter )
             return chars






      UpperCamelCase							=tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
else:
      UpperCamelCase							=tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )


UpperCamelCase							="\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

UpperCamelCase							="\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

UpperCamelCase							="\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,   _KWARGS_DESCRIPTION						)
class     A    (      datasets.Metric						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       return datasets.MetricInfo(
           description=_DESCRIPTION			,     citation=_CITATION			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
               {
                   """predictions""": datasets.Value("""string"""			,     id="""sequence""" ),
                   """references""": datasets.Value("""string"""			,     id="""sequence""" ),
               } )			,     codebase_urls=["""https://github.com/jitsi/jiwer/"""]			,     reference_urls=[
               """https://en.wikipedia.org/wiki/Word_error_rate""",
               """https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates""",
           ]			,     )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=False ):
       if concatenate_texts:
             return jiwer.compute_measures(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     truth_transform=__lowerCAmelCase			,     hypothesis_transform=__lowerCAmelCase			,     )["wer"]

       UpperCamelCase_				:   Optional[Any]					    =							0
       UpperCamelCase_				:   str					    =							0
       for prediction, reference in zip(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   Optional[int]					    =							jiwer.compute_measures(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     truth_transform=__lowerCAmelCase			,     hypothesis_transform=__lowerCAmelCase			,     )
             incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
             total += measures["substitutions"] + measures["deletions"] + measures["hits"]

       return incorrect / total






