





"""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


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   "▁"

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

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

SCREAMING_SNAKE_CASE__					=   {
    "facebook/nllb-200-distilled-600M": 1_024,
}

# fmt: off
SCREAMING_SNAKE_CASE__					=   ["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					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE					=      ['input_ids', 'attention_mask']

      _SCREAMING_SNAKE_CASE					=      []
      _SCREAMING_SNAKE_CASE					=      []





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

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

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

            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(str(lowercase ) )
            lowerCAmelCase  						=       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
            lowerCAmelCase  						=       {"""<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
            lowerCAmelCase  						=       1

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

            self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
            lowerCAmelCase  						=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
            lowerCAmelCase  						=       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] )

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





      def __getstate__(       self )		->     Tuple:
            lowerCAmelCase  						=       self.__dict__.copy()
            lowerCAmelCase  						=       None
            lowerCAmelCase  						=       self.sp_model.serialized_model_proto()
            return state





      def __setstate__(       self      ,						lowercase )		->     int:
            lowerCAmelCase  						=       d

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

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





      @property
      def     _snake_case			(       self )		->     Optional[int]:
            return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1  # Plus 1 for the mask token





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





      @src_lang.setter
      def     _snake_case			(       self      ,						lowercase )		->     None:
            lowerCAmelCase  						=       new_src_lang
            self.set_src_lang_special_tokens(self._src_lang )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None      ,						lowercase = False )		->     List[int]:

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

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





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = 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     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     List[int]:
            lowerCAmelCase  						=       [self.sep_token_id]
            lowerCAmelCase  						=       [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     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						**lowercase )		->     Optional[Any]:
            if src_lang is None or tgt_lang is None:
                  raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
            lowerCAmelCase  						=       src_lang
            lowerCAmelCase  						=       self(lowercase      ,						add_special_tokens=lowercase      ,						return_tensors=lowercase      ,						**lowercase )
            lowerCAmelCase  						=       self.convert_tokens_to_ids(lowercase )
            lowerCAmelCase  						=       tgt_lang_id
            return inputs





      def     _snake_case			(       self )		->     Dict:
            lowerCAmelCase  						=       {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
            vocab.update(self.added_tokens_encoder )
            return vocab





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





      def     _snake_case			(       self      ,						lowercase )		->     Optional[Any]:
            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            lowerCAmelCase  						=       self.sp_model.PieceToId(lowercase )

            # 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     _snake_case			(       self      ,						lowercase )		->     Tuple:
            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     _snake_case			(       self      ,						lowercase )		->     List[str]:
            lowerCAmelCase  						=       """""".join(lowercase ).replace(lowercase      ,						""" """ ).strip()
            return out_string





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

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

            return (out_vocab_file,)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = "eng_Latn"      ,						lowercase = None      ,						lowercase = "fra_Latn"      ,						**lowercase      ,						)		->     BatchEncoding:
            lowerCAmelCase  						=       src_lang
            lowerCAmelCase  						=       tgt_lang
            return super().prepare_seqaseq_batch(lowercase      ,						lowercase      ,						**lowercase )





      def     _snake_case			(       self )		->     Any:
            return self.set_src_lang_special_tokens(self.src_lang )





      def     _snake_case			(       self )		->     Union[str, Any]:
            return self.set_tgt_lang_special_tokens(self.tgt_lang )





      def     _snake_case			(       self      ,						lowercase )		->     None:
            lowerCAmelCase  						=       self.lang_code_to_id[src_lang]
            if self.legacy_behaviour:
                  lowerCAmelCase  						=       []
                  lowerCAmelCase  						=       [self.eos_token_id, self.cur_lang_code]
            else:
                  lowerCAmelCase  						=       [self.cur_lang_code]
                  lowerCAmelCase  						=       [self.eos_token_id]





      def     _snake_case			(       self      ,						lowercase )		->     None:
            lowerCAmelCase  						=       self.lang_code_to_id[lang]
            if self.legacy_behaviour:
                  lowerCAmelCase  						=       []
                  lowerCAmelCase  						=       [self.eos_token_id, self.cur_lang_code]
            else:
                  lowerCAmelCase  						=       [self.cur_lang_code]
                  lowerCAmelCase  						=       [self.eos_token_id]













"""simple docstring"""





import re
import string

import numpy as np

import datasets


SCREAMING_SNAKE_CASE__					=   "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

SCREAMING_SNAKE_CASE__					=   "\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

SCREAMING_SNAKE_CASE__					=   "\n"

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





      def     _snake_case			(       self )		->     Optional[int]:
            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""" ),
                    } )      ,						reference_urls=[]      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase=None      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						)		->     Optional[Any]:
            if regexes_to_ignore is not None:
                  for s in regexes_to_ignore:
                        lowerCAmelCase  						=       np.array([re.sub(lowercase      ,						""""""      ,						lowercase ) for x in predictions] )
                        lowerCAmelCase  						=       np.array([re.sub(lowercase      ,						""""""      ,						lowercase ) for x in references] )
            else:
                  lowerCAmelCase  						=       np.asarray(lowercase )
                  lowerCAmelCase  						=       np.asarray(lowercase )

            if ignore_case:
                  lowerCAmelCase  						=       np.char.lower(lowercase )
                  lowerCAmelCase  						=       np.char.lower(lowercase )

            if ignore_punctuation:
                  lowerCAmelCase  						=       string.punctuation.maketrans(""""""      ,						""""""      ,						string.punctuation )
                  lowerCAmelCase  						=       np.char.translate(lowercase      ,						table=lowercase )
                  lowerCAmelCase  						=       np.char.translate(lowercase      ,						table=lowercase )

            if ignore_numbers:
                  lowerCAmelCase  						=       string.digits.maketrans(""""""      ,						""""""      ,						string.digits )
                  lowerCAmelCase  						=       np.char.translate(lowercase      ,						table=lowercase )
                  lowerCAmelCase  						=       np.char.translate(lowercase      ,						table=lowercase )

            lowerCAmelCase  						=       predictions == references

            return {"exact_match": np.mean(lowercase ) * 100}






