






import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		"""\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"""

SCREAMING_SNAKE_CASE__   :      str				=		"""\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"""

SCREAMING_SNAKE_CASE__   :      str				=		"""\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,					_KWARGS_DESCRIPTION			)
class     lowerCamelCase_					(   datasets.Metric			):







				def       A						(    self  ):
								"""simple docstring"""
								return datasets.MetricInfo(
								    description=_DESCRIPTION							,       citation=_CITATION							,       inputs_description=_KWARGS_DESCRIPTION							,       features=datasets.Features(
								        {
								            '''predictions''': datasets.Sequence(datasets.Value('''string'''							,       id='''token'''  )							,       id='''sequence'''  ),
								            '''references''': datasets.Sequence(
								                datasets.Sequence(datasets.Value('''string'''							,       id='''token'''  )							,       id='''sequence'''  )							,       id='''references'''  ),
								        }  )							,       codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']							,       reference_urls=[
								        '''https://en.wikipedia.org/wiki/BLEU''',
								        '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
								    ]							,       )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase=4							,       __lowerCAmelCase=False  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   compute_bleu(
								    reference_corpus=__UpperCAmelCase							,       translation_corpus=__UpperCAmelCase							,       max_order=__UpperCAmelCase							,       smooth=__UpperCAmelCase  )
								((__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__))		:Tuple   =   score
								return {
								    "bleu": bleu,
								    "precisions": precisions,
								    "brevity_penalty": bp,
								    "length_ratio": ratio,
								    "translation_length": translation_length,
								    "reference_length": reference_length,
								}














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": 1024,
}

# 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_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    VOCAB_FILES_NAMES
      __snake_case	    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case	    =    PRETRAINED_VOCAB_FILES_MAP
      __snake_case	    =    ['''input_ids''', '''attention_mask''']

      __snake_case	    =    []
      __snake_case	    =    []







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




       """simple docstring"""


       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 :		str    )       ->Any:




       """simple docstring"""


       a						    = self.__dict__.copy()
       a						    = None
       a						    = self.sp_model.serialized_model_proto()
       return state







      def __setstate__( self :		List[str]					,   __UpperCAmelCase :		Optional[Any]    )       ->int:




       """simple docstring"""


       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  __lowerCAmelCase   ( self :		Tuple    )       ->Optional[Any]:




       """simple docstring"""


       return len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset + 1  # Plus 1 for the mask token







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->str:




       """simple docstring"""


       return self._src_lang







      @src_lang.setter
      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		str    )       ->None:




       """simple docstring"""


       a						    = new_src_lang
       self.set_src_lang_special_tokens(self._src_lang    )







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




       """simple docstring"""


       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  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		List[int]					,   __UpperCAmelCase :		Optional[List[int]] = None    )       ->List[int]:




       """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  __lowerCAmelCase   ( self :		int					,   __UpperCAmelCase :		List[int]					,   __UpperCAmelCase :		Optional[List[int]] = None    )       ->List[int]:




       """simple docstring"""


       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  __lowerCAmelCase   ( self :		str					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[str]					,   __UpperCAmelCase :		Optional[str]					,   **__UpperCAmelCase :		Tuple    )       ->Tuple:




       """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'''    )
       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  __lowerCAmelCase   ( self :		Optional[int]    )       ->Union[str, Any]:




       """simple docstring"""


       a						    = {self.convert_ids_to_tokens(__UpperCAmelCase    ): i for i in range(self.vocab_size    )}
       vocab.update(self.added_tokens_encoder    )
       return vocab







      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		str    )       ->List[str]:




       """simple docstring"""


       return self.sp_model.encode(__UpperCAmelCase					,   out_type=__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Optional[int]					,   __UpperCAmelCase :		str    )       ->List[Any]:




       """simple docstring"""


       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  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		int    )       ->Optional[Any]:




       """simple docstring"""


       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  __lowerCAmelCase   ( self :		Tuple					,   __UpperCAmelCase :		List[str]    )       ->List[Any]:




       """simple docstring"""


       a						    = ''''''.join(__UpperCAmelCase    ).replace(__UpperCAmelCase					,   ''' '''    ).strip()
       return out_string







      def  __lowerCAmelCase   ( self :		str					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[str] = None    )       ->Tuple[str]:




       """simple docstring"""


       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  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		str = "eng_Latn"					,   __UpperCAmelCase :		Optional[List[str]] = None					,   __UpperCAmelCase :		str = "fra_Latn"					,   **__UpperCAmelCase :		Optional[int]					,   )       ->BatchEncoding:




       """simple docstring"""


       a						    = src_lang
       a						    = tgt_lang
       return super().prepare_seqaseq_batch(__UpperCAmelCase					,   __UpperCAmelCase					,   **__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Dict    )       ->List[str]:




       """simple docstring"""


       return self.set_src_lang_special_tokens(self.src_lang    )







      def  __lowerCAmelCase   ( self :		Any    )       ->Any:




       """simple docstring"""


       return self.set_tgt_lang_special_tokens(self.tgt_lang    )







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		Tuple    )       ->None:




       """simple docstring"""


       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  __lowerCAmelCase   ( self :		List[str]					,   __UpperCAmelCase :		str    )       ->None:




       """simple docstring"""


       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]


