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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_nllb import NllbTokenizer
else:
     lowerCamelCase__     :		str        = None


lowerCamelCase__     :		int        = logging.get_logger(__name__)


lowerCamelCase__     :		Optional[int]        = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__     :		List[str]        = {
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCamelCase__     :		Union[str, Any]        = {
    """facebook/nllb-large-en-ro""": 1_0_2_4,
    """facebook/nllb-200-distilled-600M""": 1_0_2_4,
}

# fmt: off
lowerCamelCase__     :		Tuple        = ["""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    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  List[str]	   =      VOCAB_FILES_NAMES
 __lowerCAmelCase	:  Optional[Any]	   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase	:  str	   =      PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase	:  Tuple	   =      ['input_ids', 'attention_mask']
 __lowerCAmelCase	:  List[Any]	   =      NllbTokenizer

 __lowerCAmelCase	:  List[int]	   =      []
 __lowerCAmelCase	:  List[int]	   =      []


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_="<s>"       ,	SCREAMING_SNAKE_CASE_="</s>"       ,	SCREAMING_SNAKE_CASE_="</s>"       ,	SCREAMING_SNAKE_CASE_="<s>"       ,	SCREAMING_SNAKE_CASE_="<unk>"       ,	SCREAMING_SNAKE_CASE_="<pad>"       ,	SCREAMING_SNAKE_CASE_="<mask>"       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=False       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Any     		=   AddedToken(SCREAMING_SNAKE_CASE_       ,	lstrip=SCREAMING_SNAKE_CASE_       ,	rstrip=SCREAMING_SNAKE_CASE_) if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) else mask_token
   lowercase__	: Optional[int]     		=   legacy_behaviour
   super().__init__(
       vocab_file=SCREAMING_SNAKE_CASE_       ,	tokenizer_file=SCREAMING_SNAKE_CASE_       ,	bos_token=SCREAMING_SNAKE_CASE_       ,	eos_token=SCREAMING_SNAKE_CASE_       ,	sep_token=SCREAMING_SNAKE_CASE_       ,	cls_token=SCREAMING_SNAKE_CASE_       ,	unk_token=SCREAMING_SNAKE_CASE_       ,	pad_token=SCREAMING_SNAKE_CASE_       ,	mask_token=SCREAMING_SNAKE_CASE_       ,	src_lang=SCREAMING_SNAKE_CASE_       ,	tgt_lang=SCREAMING_SNAKE_CASE_       ,	additional_special_tokens=SCREAMING_SNAKE_CASE_       ,	legacy_behaviour=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_       ,	)

   lowercase__	: int     		=   vocab_file
   lowercase__	: Any     		=   False if not self.vocab_file else True

   lowercase__	: Union[str, Any]     		=   FAIRSEQ_LANGUAGE_CODES.copy()

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

   self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens})
   lowercase__	: Optional[Any]     		=   {
       lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_) for lang_code in FAIRSEQ_LANGUAGE_CODES
   }

   lowercase__	: int     		=   src_lang if src_lang is not None else """eng_Latn"""
   lowercase__	: Tuple     		=   self.convert_tokens_to_ids(self._src_lang)
   lowercase__	: Dict     		=   tgt_lang
   self.set_src_lang_special_tokens(self._src_lang)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self._src_lang


 @src_lang.setter
 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: int     		=   new_src_lang
   self.set_src_lang_special_tokens(self._src_lang)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   [self.sep_token_id]
   lowercase__	: Any     		=   [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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if src_lang is None or tgt_lang is None:
     raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""")
   lowercase__	: Tuple     		=   src_lang
   lowercase__	: Any     		=   self(SCREAMING_SNAKE_CASE_       ,	add_special_tokens=SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   lowercase__	: str     		=   self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[int]     		=   tgt_lang_id
   return inputs


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = "eng_Latn"       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = "fra_Latn"       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   src_lang
   lowercase__	: List[str]     		=   tgt_lang
   return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   return self.set_src_lang_special_tokens(self.src_lang)


 def   lowercase__  (       self):


   '''simple docstring'''




   return self.set_tgt_lang_special_tokens(self.tgt_lang)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)

   if self.legacy_behaviour:
     lowercase__	: Optional[Any]     		=   []
     lowercase__	: Union[str, Any]     		=   [self.eos_token_id, self.cur_lang_code]
   else:
     lowercase__	: Optional[Any]     		=   [self.cur_lang_code]
     lowercase__	: Tuple     		=   [self.eos_token_id]

   lowercase__	: int     		=   self.convert_ids_to_tokens(self.prefix_tokens)
   lowercase__	: Optional[Any]     		=   self.convert_ids_to_tokens(self.suffix_tokens)

   lowercase__	: str     		=   processors.TemplateProcessing(
       single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,	pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,	self.prefix_tokens + self.suffix_tokens))       ,	)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: int     		=   self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)
   if self.legacy_behaviour:
     lowercase__	: Dict     		=   []
     lowercase__	: Dict     		=   [self.eos_token_id, self.cur_lang_code]
   else:
     lowercase__	: Tuple     		=   [self.cur_lang_code]
     lowercase__	: str     		=   [self.eos_token_id]

   lowercase__	: Dict     		=   self.convert_ids_to_tokens(self.prefix_tokens)
   lowercase__	: int     		=   self.convert_ids_to_tokens(self.suffix_tokens)

   lowercase__	: Optional[Any]     		=   processors.TemplateProcessing(
       single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,	pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,	self.prefix_tokens + self.suffix_tokens))       ,	)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if not self.can_save_slow_tokenizer:
     raise ValueError(
         """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
         """tokenizer.""")

   if not os.path.isdir(SCREAMING_SNAKE_CASE_):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory.')
     return
   lowercase__	: List[Any]     		=   os.path.join(
       SCREAMING_SNAKE_CASE_       ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

   if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE_):
     copyfile(self.vocab_file       ,	SCREAMING_SNAKE_CASE_)

   return (out_vocab_file,)














from jiwer import compute_measures

import datasets


A__      :     Tuple       =					'\\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'

A__      :     Optional[int]       =					'\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (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 words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A__      :     Dict       =					'\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word 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    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION      )
class  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								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''',
								    ],    )




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       str=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       List[str]=False    ):

								'''simple docstring'''
								if concatenate_texts:
												return compute_measures(lowerCamelCase,    lowerCamelCase    )["wer"]
								else:
												lowercase__  					=     0
												lowercase__  					=     0
												for prediction, reference in zip(lowerCamelCase,    lowerCamelCase    ):
																lowercase__  					=     compute_measures(lowerCamelCase,    lowerCamelCase    )
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
												return incorrect / total




