




'''simple docstring'''


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

import sentencepiece as spm

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


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      "▁"

UpperCAmelCase_ 		=      {"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase_ 		=      {
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCAmelCase_ 		=      {
    "facebook/nllb-200-distilled-600M": 10_24,
}

# fmt: off
UpperCAmelCase_ 		=      ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      ["""input_ids""", """attention_mask"""]

   _a							=      []
   _a							=      []







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase="<s>"      ,							UpperCamelCase="</s>"      ,							UpperCamelCase="</s>"      ,							UpperCamelCase="<s>"      ,							UpperCamelCase="<unk>"      ,							UpperCamelCase="<pad>"      ,							UpperCamelCase="<mask>"      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase = None      ,							UpperCamelCase=None      ,							UpperCamelCase=False      ,							**UpperCamelCase      ,							)				->    Tuple:
     # Mask token behave like a normal word, i.e. include the space before it
     __a			=					AddedToken(UpperCamelCase      ,							lstrip=UpperCamelCase      ,							rstrip=UpperCamelCase      ) if isinstance(UpperCamelCase      ,							UpperCamelCase      ) else mask_token

     __a			=					{} if sp_model_kwargs is None else sp_model_kwargs
     __a			=					legacy_behaviour

     super().__init__(
         bos_token=UpperCamelCase      ,							eos_token=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							src_lang=UpperCamelCase      ,							tgt_lang=UpperCamelCase      ,							additional_special_tokens=UpperCamelCase      ,							sp_model_kwargs=self.sp_model_kwargs      ,							legacy_behaviour=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(str(UpperCamelCase      )      )
     __a			=					vocab_file
     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
     # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
     # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

     # Mimic fairseq token-to-id alignment for the first 4 token
     __a			=					{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     __a			=					1

     __a			=					len(self.sp_model      )
     __a			=					{
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase      )
     }
     __a			=					{v: k for k, v in self.lang_code_to_id.items()}
     __a			=					len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset

     self.fairseq_tokens_to_ids.update(self.lang_code_to_id      )
     __a			=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
     __a			=					list(self.lang_code_to_id.keys()      )

     if additional_special_tokens is not None:
       # Only add those special tokens if they are not already there.
       self._additional_special_tokens.extend(
           [t for t in additional_special_tokens if t not in self._additional_special_tokens]      )

     __a			=					src_lang if src_lang is not None else 'eng_Latn'
     __a			=					self.lang_code_to_id[self._src_lang]
     __a			=					tgt_lang
     self.set_src_lang_special_tokens(self._src_lang      )







   def __getstate__(	self      )				->    Union[str, Any]:
     __a			=					self.__dict__.copy()
     __a			=					None
     __a			=					self.sp_model.serialized_model_proto()
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    str:
     __a			=					d

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

     __a			=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )







   @property
   def 		UpperCamelCase__			(	self      )				->    str:
     return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token







   @property
   def 		UpperCamelCase__			(	self      )				->    str:
     return self._src_lang







   @src_lang.setter
   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					new_src_lang
     self.set_src_lang_special_tokens(self._src_lang      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = False      )				->    List[int]:

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

     __a			=					[1] * len(self.prefix_tokens      )
     __a			=					[1] * len(self.suffix_tokens      )
     if token_ids_a is None:
       return prefix_ones + ([0] * len(UpperCamelCase      )) + suffix_ones
     return prefix_ones + ([0] * len(UpperCamelCase      )) + ([0] * len(UpperCamelCase      )) + suffix_ones







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + self.suffix_tokens
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[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]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    Dict:
     if src_lang is None or tgt_lang is None:
       raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'      )
     __a			=					src_lang
     __a			=					self(UpperCamelCase      ,							add_special_tokens=UpperCamelCase      ,							return_tensors=UpperCamelCase      ,							**UpperCamelCase      )
     __a			=					self.convert_tokens_to_ids(UpperCamelCase      )
     __a			=					tgt_lang_id
     return inputs







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					{self.convert_ids_to_tokens(UpperCamelCase      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[str]:
     return self.sp_model.encode(UpperCamelCase      ,							out_type=UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Optional[Any]:
     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     __a			=					self.sp_model.PieceToId(UpperCamelCase      )

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    str:
     __a			=					''.join(UpperCamelCase      ).replace(UpperCamelCase      ,							' '      ).strip()
     return out_string







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

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

     return (out_vocab_file,)







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = "eng_Latn"      ,							UpperCamelCase = None      ,							UpperCamelCase = "fra_Latn"      ,							**UpperCamelCase      ,							)				->    BatchEncoding:
     __a			=					src_lang
     __a			=					tgt_lang
     return super().prepare_seqaseq_batch(UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return self.set_src_lang_special_tokens(self.src_lang      )







   def 		UpperCamelCase__			(	self      )				->    int:
     return self.set_tgt_lang_special_tokens(self.tgt_lang      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					self.lang_code_to_id[src_lang]
     if self.legacy_behaviour:
       __a			=					[]
       __a			=					[self.eos_token_id, self.cur_lang_code]
     else:
       __a			=					[self.cur_lang_code]
       __a			=					[self.eos_token_id]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					self.lang_code_to_id[lang]
     if self.legacy_behaviour:
       __a			=					[]
       __a			=					[self.eos_token_id, self.cur_lang_code]
     else:
       __a			=					[self.cur_lang_code]
       __a			=					[self.eos_token_id]









'''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    __lowercase						(  tr.AbstractTransform ):







     def __init__(	self      ,							UpperCamelCase = " "      )				->    Tuple:
       __a			=					sentence_delimiter







     def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Union[str, Any]:
       return list(UpperCamelCase      )







     def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    int:
       __a			=					[]
       for sent_idx, sentence in enumerate(UpperCamelCase      ):
         chars.extend(self.process_string(UpperCamelCase      )      )
         if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCamelCase      ) - 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    __lowercase						(  datasets.Metric ):







   def 		UpperCamelCase__			(	self      )				->    Any:
     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      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase=False      )				->    int:
     if concatenate_texts:
       return jiwer.compute_measures(
           UpperCamelCase      ,							UpperCamelCase      ,							truth_transform=UpperCamelCase      ,							hypothesis_transform=UpperCamelCase      ,							)["wer"]

     __a			=					0
     __a			=					0
     for prediction, reference in zip(UpperCamelCase      ,							UpperCamelCase      ):
       __a			=					jiwer.compute_measures(
           UpperCamelCase      ,							UpperCamelCase      ,							truth_transform=UpperCamelCase      ,							hypothesis_transform=UpperCamelCase      ,							)
       incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
       total += measures["substitutions"] + measures["deletions"] + measures["hits"]

     return incorrect / total



