



'''simple docstring'''
import comet  # From: unbabel-comet
import torch

import datasets


snake_case_			:     int  	=  datasets.logging.get_logger(__name__)

snake_case_			:     Optional[int]  	=  '\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

snake_case_			:     str  	=  '\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

snake_case_			:     Tuple  	=  '\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'






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





	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							return datasets.MetricInfo(
							    description=_DESCRIPTION			,citation=_CITATION			,homepage='https://unbabel.github.io/COMET/html/index.html'			,inputs_description=_KWARGS_DESCRIPTION			,features=datasets.Features(
							        {
							            'sources': datasets.Value('string'			,id='sequence'			),
							            'predictions': datasets.Value('string'			,id='sequence'			),
							            'references': datasets.Value('string'			,id='sequence'			),
							        }			)			,codebase_urls=['https://github.com/Unbabel/COMET']			,reference_urls=[
							        'https://github.com/Unbabel/COMET',
							        'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
							        'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
							    ]			,)





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    str			):







							'''simple docstring'''
							if self.config_name == "default":
													_UpperCamelCase :		Optional[Any]			    =       comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'			)			)
							else:
													_UpperCamelCase :		Dict			    =       comet.load_from_checkpoint(comet.download_model(self.config_name			)			)





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    Any			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    Dict=None			,lowerCamelCase__ :    int=False			):







							'''simple docstring'''
							if gpus is None:
													_UpperCamelCase :		int			    =       1 if torch.cuda.is_available() else 0
							_UpperCamelCase :		int			    =       {'src': sources, 'mt': predictions, 'ref': references}
							_UpperCamelCase :		List[str]			    =       [dict(zip(lowerCamelCase__			,lowerCamelCase__			)			) for t in zip(*data.values()			)]
							_UpperCamelCase    ,      _UpperCamelCase :		int			    =       self.scorer.predict(lowerCamelCase__			,gpus=lowerCamelCase__			,progress_bar=lowerCamelCase__			)
							return {"mean_score": mean_score, "scores": scores}







'''simple docstring'''
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:
					snake_case_			:     List[Any]  	=  None


snake_case_			:     Dict  	=  logging.get_logger(__name__)


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

snake_case_			:     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'
        ),
    },
}

snake_case_			:     str  	=  {
    'facebook/nllb-large-en-ro': 1024,
    'facebook/nllb-200-distilled-600M': 1024,
}

# fmt: off
snake_case_			:     Optional[Any]  	=  ['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       ):
	lowercase__      =      VOCAB_FILES_NAMES
	lowercase__      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__      =      PRETRAINED_VOCAB_FILES_MAP
	lowercase__      =      ["""input_ids""", """attention_mask"""]
	lowercase__      =      NllbTokenizer

	lowercase__      =      []
	lowercase__      =      []





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







							'''simple docstring'''
							# Mask token behave like a normal word, i.e. include the space before it
							_UpperCamelCase :		Optional[int]			    =       AddedToken(lowerCamelCase__			,lstrip=lowerCamelCase__			,rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__			,lowerCamelCase__			) else mask_token
							_UpperCamelCase :		Union[str, Any]			    =       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__			,)

							_UpperCamelCase :		int			    =       vocab_file
							_UpperCamelCase :		int			    =       False if not self.vocab_file else True

							_UpperCamelCase :		Dict			    =       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}			)
							_UpperCamelCase :		List[str]			    =       {
							    lang_code: self.convert_tokens_to_ids(lowerCamelCase__			) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

							_UpperCamelCase :		List[str]			    =       src_lang if src_lang is not None else 'eng_Latn'
							_UpperCamelCase :		int			    =       self.convert_tokens_to_ids(self._src_lang			)
							_UpperCamelCase :		Dict			    =       tgt_lang
							self.set_src_lang_special_tokens(self._src_lang			)





	@property
	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							return self._src_lang





	@src_lang.setter
	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    str			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       new_src_lang
							self.set_src_lang_special_tokens(self._src_lang			)





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    List[int]			,lowerCamelCase__ :    Optional[List[int]] = 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      UpperCamelCase_  (			self :    Dict			,lowerCamelCase__ :    List[int]			,lowerCamelCase__ :    Optional[List[int]] = None			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       [self.sep_token_id]
							_UpperCamelCase :		List[str]			    =       [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 :    Union[str, Any]			,lowerCamelCase__ :    str			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Optional[str]			,lowerCamelCase__ :    Optional[str]			,**lowerCamelCase__ :    Dict			):







							'''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'			)
							_UpperCamelCase :		Tuple			    =       src_lang
							_UpperCamelCase :		Optional[Any]			    =       self(lowerCamelCase__			,add_special_tokens=lowerCamelCase__			,return_tensors=lowerCamelCase__			,**lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       self.convert_tokens_to_ids(lowerCamelCase__			)
							_UpperCamelCase :		str			    =       tgt_lang_id
							return inputs





	def      UpperCamelCase_  (			self :    int			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    str = "eng_Latn"			,lowerCamelCase__ :    Optional[List[str]] = None			,lowerCamelCase__ :    str = "fra_Latn"			,**lowerCamelCase__ :    Union[str, Any]			,):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       src_lang
							_UpperCamelCase :		List[str]			    =       tgt_lang
							return super().prepare_seqaseq_batch(lowerCamelCase__			,lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							return self.set_src_lang_special_tokens(self.src_lang			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							return self.set_tgt_lang_special_tokens(self.tgt_lang			)





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    List[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       self.convert_tokens_to_ids(lowerCamelCase__			)

							if self.legacy_behaviour:
													_UpperCamelCase :		Optional[Any]			    =       []
													_UpperCamelCase :		int			    =       [self.eos_token_id, self.cur_lang_code]
							else:
													_UpperCamelCase :		List[Any]			    =       [self.cur_lang_code]
													_UpperCamelCase :		List[Any]			    =       [self.eos_token_id]

							_UpperCamelCase :		Union[str, Any]			    =       self.convert_ids_to_tokens(self.prefix_tokens			)
							_UpperCamelCase :		int			    =       self.convert_ids_to_tokens(self.suffix_tokens			)

							_UpperCamelCase :		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      UpperCamelCase_  (			self :    int			,lowerCamelCase__ :    str			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       self.convert_tokens_to_ids(lowerCamelCase__			)
							if self.legacy_behaviour:
													_UpperCamelCase :		Tuple			    =       []
													_UpperCamelCase :		str			    =       [self.eos_token_id, self.cur_lang_code]
							else:
													_UpperCamelCase :		Tuple			    =       [self.cur_lang_code]
													_UpperCamelCase :		Optional[Any]			    =       [self.eos_token_id]

							_UpperCamelCase :		int			    =       self.convert_ids_to_tokens(self.prefix_tokens			)
							_UpperCamelCase :		List[Any]			    =       self.convert_ids_to_tokens(self.suffix_tokens			)

							_UpperCamelCase :		Optional[int]			    =       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      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Optional[str] = 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(lowerCamelCase__			):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory.'			)
													return
							_UpperCamelCase :		List[Any]			    =       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,)


