




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:
			__SCREAMING_SNAKE_CASE 						=None


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE 						={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__SCREAMING_SNAKE_CASE 						={
    '''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'''
        ),
    },
}

__SCREAMING_SNAKE_CASE 						={
    '''facebook/nllb-large-en-ro''': 1_024,
    '''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  __magic_name__						(  a__):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__					:							str	      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__					:							Dict	      =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					NllbTokenizer

				SCREAMING_SNAKE_CASE__					:							List[int]	      =					[]
				SCREAMING_SNAKE_CASE__					:							List[int]	      =					[]


				def __init__(							self:   List[str]		,					_lowerCamelCase:   List[Any]=None		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   List[Any]="<s>"		,					_lowerCamelCase:   List[str]="</s>"		,					_lowerCamelCase:   Tuple="</s>"		,					_lowerCamelCase:   Any="<s>"		,					_lowerCamelCase:   List[str]="<unk>"		,					_lowerCamelCase:   str="<pad>"		,					_lowerCamelCase:   List[Any]="<mask>"		,					_lowerCamelCase:   Any=None		,					_lowerCamelCase:   List[str]=None		,					_lowerCamelCase:   List[Any]=None		,					_lowerCamelCase:   Dict=False		,					**_lowerCamelCase:   str		,					):
											# Mask token behave like a normal word, i.e. include the space before it
											SCREAMING_SNAKE_CASE_   	=    AddedToken(lowerCAmelCase__		,					lstrip=lowerCAmelCase__		,					rstrip=lowerCAmelCase__		) if isinstance(lowerCAmelCase__		,					lowerCAmelCase__		) else mask_token
											SCREAMING_SNAKE_CASE_   	=    legacy_behaviour
											super().__init__(
											    vocab_file=lowerCAmelCase__		,					tokenizer_file=lowerCAmelCase__		,					bos_token=lowerCAmelCase__		,					eos_token=lowerCAmelCase__		,					sep_token=lowerCAmelCase__		,					cls_token=lowerCAmelCase__		,					unk_token=lowerCAmelCase__		,					pad_token=lowerCAmelCase__		,					mask_token=lowerCAmelCase__		,					src_lang=lowerCAmelCase__		,					tgt_lang=lowerCAmelCase__		,					additional_special_tokens=lowerCAmelCase__		,					legacy_behaviour=lowerCAmelCase__		,					**lowerCAmelCase__		,					)

											SCREAMING_SNAKE_CASE_   	=    vocab_file
											SCREAMING_SNAKE_CASE_   	=    False if not self.vocab_file else True

											SCREAMING_SNAKE_CASE_   	=    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}		)
											SCREAMING_SNAKE_CASE_   	=    {
											    lang_code: self.convert_tokens_to_ids(lowerCAmelCase__		) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

											SCREAMING_SNAKE_CASE_   	=    src_lang if src_lang is not None else "eng_Latn"
											SCREAMING_SNAKE_CASE_   	=    self.convert_tokens_to_ids(self._src_lang		)
											SCREAMING_SNAKE_CASE_   	=    tgt_lang
											self.set_src_lang_special_tokens(self._src_lang		)


				@property
				def     _A							(							self:   Tuple		):
											return self._src_lang


				@src_lang.setter
				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    new_src_lang
											self.set_src_lang_special_tokens(self._src_lang		)


				def     _A							(							self:   int		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   List[str] = None		):
											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     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   List[str] = None		):
											SCREAMING_SNAKE_CASE_   	=    [self.sep_token_id]
											SCREAMING_SNAKE_CASE_   	=    [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     _A							(							self:   Dict		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   Dict		,					**_lowerCamelCase:   Union[str, 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'''		)
											SCREAMING_SNAKE_CASE_   	=    src_lang
											SCREAMING_SNAKE_CASE_   	=    self(lowerCAmelCase__		,					add_special_tokens=lowerCAmelCase__		,					return_tensors=lowerCAmelCase__		,					**lowerCAmelCase__		)
											SCREAMING_SNAKE_CASE_   	=    self.convert_tokens_to_ids(lowerCAmelCase__		)
											SCREAMING_SNAKE_CASE_   	=    tgt_lang_id
											return inputs


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Any = "eng_Latn"		,					_lowerCamelCase:   List[Any] = None		,					_lowerCamelCase:   Union[str, Any] = "fra_Latn"		,					**_lowerCamelCase:   int		,					):
											SCREAMING_SNAKE_CASE_   	=    src_lang
											SCREAMING_SNAKE_CASE_   	=    tgt_lang
											return super().prepare_seqaseq_batch(lowerCAmelCase__		,					lowerCAmelCase__		,					**lowerCAmelCase__		)


				def     _A							(							self:   Any		):
											return self.set_src_lang_special_tokens(self.src_lang		)


				def     _A							(							self:   int		):
											return self.set_tgt_lang_special_tokens(self.tgt_lang		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.convert_tokens_to_ids(lowerCAmelCase__		)

											if self.legacy_behaviour:
																		SCREAMING_SNAKE_CASE_   	=    []
																		SCREAMING_SNAKE_CASE_   	=    [self.eos_token_id, self.cur_lang_code]
											else:
																		SCREAMING_SNAKE_CASE_   	=    [self.cur_lang_code]
																		SCREAMING_SNAKE_CASE_   	=    [self.eos_token_id]

											SCREAMING_SNAKE_CASE_   	=    self.convert_ids_to_tokens(self.prefix_tokens		)
											SCREAMING_SNAKE_CASE_   	=    self.convert_ids_to_tokens(self.suffix_tokens		)

											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   Any		,					_lowerCamelCase:   str		):
											SCREAMING_SNAKE_CASE_   	=    self.convert_tokens_to_ids(lowerCAmelCase__		)
											if self.legacy_behaviour:
																		SCREAMING_SNAKE_CASE_   	=    []
																		SCREAMING_SNAKE_CASE_   	=    [self.eos_token_id, self.cur_lang_code]
											else:
																		SCREAMING_SNAKE_CASE_   	=    [self.cur_lang_code]
																		SCREAMING_SNAKE_CASE_   	=    [self.eos_token_id]

											SCREAMING_SNAKE_CASE_   	=    self.convert_ids_to_tokens(self.prefix_tokens		)
											SCREAMING_SNAKE_CASE_   	=    self.convert_ids_to_tokens(self.suffix_tokens		)

											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   Optional[int]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Optional[int] = None		):
											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(lowerCAmelCase__		):
																		logger.error(f"Vocabulary path ({save_directory}) should be a directory."		)
																		return
											SCREAMING_SNAKE_CASE_   	=    os.path.join(
											    lowerCAmelCase__		,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

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

											return (out_vocab_file,)








import re
import string

import numpy as np

import datasets


__SCREAMING_SNAKE_CASE 						="""
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""

__SCREAMING_SNAKE_CASE 						="""
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    25.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    50.0


    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    75.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results[\"exact_match\"], 1))
    100.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    33.3

"""

__SCREAMING_SNAKE_CASE 						="""
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,  _KWARGS_DESCRIPTION)
class  __magic_name__						(  datasets.Metric):
				'''simple docstring'''







				def     _A							(							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     _A							(							self:   Tuple		,					_lowerCamelCase:   int		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   str=False		,					_lowerCamelCase:   List[str]=False		,					_lowerCamelCase:   List[str]=False		,					):
											if regexes_to_ignore is not None:
																		for s in regexes_to_ignore:
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in predictions]		)
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in references]		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)

											if ignore_case:
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)

											if ignore_punctuation:
																		SCREAMING_SNAKE_CASE_   	=    string.punctuation.maketrans(''''''		,					''''''		,					string.punctuation		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)

											if ignore_numbers:
																		SCREAMING_SNAKE_CASE_   	=    string.digits.maketrans(''''''		,					''''''		,					string.digits		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    predictions == references

											return {"exact_match": np.mean(_lowerCamelCase		) * 1_00}


