


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_mbart import MBartTokenizer
else:
			lowerCAmelCase	=							None


lowerCAmelCase	=							logging.get_logger(__name__)


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

lowerCAmelCase	=							{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase	=							{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
lowerCAmelCase	=							['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      Union[str, Any]  				=			VOCAB_FILES_NAMES
							_lowercase     :      Tuple  				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowercase     :      Union[str, Any]  				=			PRETRAINED_VOCAB_FILES_MAP
							_lowercase     :      Optional[Any]  				=			['''input_ids''', '''attention_mask''']
							_lowercase     :      str  				=			MBartTokenizer

							_lowercase     :      List[int]  				=			[]
							_lowercase     :      List[int]  				=			[]

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





									"""simple docstring"""







									lowercase__						=				AddedToken(UpperCamelCase_   ,						lstrip=UpperCamelCase_   ,						rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_   ,						UpperCamelCase_							) else mask_token

									super().__init__(
									    vocab_file=UpperCamelCase_   ,						tokenizer_file=UpperCamelCase_   ,						bos_token=UpperCamelCase_   ,						eos_token=UpperCamelCase_   ,						sep_token=UpperCamelCase_   ,						cls_token=UpperCamelCase_   ,						unk_token=UpperCamelCase_   ,						pad_token=UpperCamelCase_   ,						mask_token=UpperCamelCase_   ,						src_lang=UpperCamelCase_   ,						tgt_lang=UpperCamelCase_   ,						additional_special_tokens=UpperCamelCase_   ,						**UpperCamelCase_   ,						)

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

									lowercase__						=				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__						=				{
									    lang_code: self.convert_tokens_to_ids(UpperCamelCase_							) for lang_code in FAIRSEQ_LANGUAGE_CODES
									}

									lowercase__						=				src_lang if src_lang is not None else '''en_XX'''
									lowercase__						=				self.convert_tokens_to_ids(self._src_lang							)
									lowercase__						=				tgt_lang
									self.set_src_lang_special_tokens(self._src_lang							)

							@property
							def 				lowerCamelCase_			(							self:     Tuple							)      ->       str:





									"""simple docstring"""







									return self._src_lang

							@src_lang.setter
							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     str							)      ->       None:





									"""simple docstring"""







									lowercase__						=				new_src_lang
									self.set_src_lang_special_tokens(self._src_lang							)

							def 				lowerCamelCase_			(							self:     Union[str, 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:     Union[str, Any]   ,						UpperCamelCase_:     List[int]   ,						UpperCamelCase_:     Optional[List[int]] = None							)      ->       List[int]:





									"""simple docstring"""







									lowercase__						=				[self.sep_token_id]
									lowercase__						=				[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:     Union[str, Any]   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Optional[str]   ,						UpperCamelCase_:     Optional[str]   ,						**UpperCamelCase_:     List[str]							)      ->       List[Any]:





									"""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__						=				src_lang
									lowercase__						=				self(UpperCamelCase_   ,						add_special_tokens=UpperCamelCase_   ,						return_tensors=UpperCamelCase_   ,						**UpperCamelCase_							)
									lowercase__						=				self.convert_tokens_to_ids(UpperCamelCase_							)
									lowercase__						=				tgt_lang_id
									return inputs

							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     str = "en_XX"   ,						UpperCamelCase_:     Optional[List[str]] = None   ,						UpperCamelCase_:     str = "ro_RO"   ,						**UpperCamelCase_:     Any   ,						)      ->       BatchEncoding:





									"""simple docstring"""







									lowercase__						=				src_lang
									lowercase__						=				tgt_lang
									return super().prepare_seqaseq_batch(UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       List[Any]:





									"""simple docstring"""







									return self.set_src_lang_special_tokens(self.src_lang							)

							def 				lowerCamelCase_			(							self:     int							)      ->       Tuple:





									"""simple docstring"""







									return self.set_tgt_lang_special_tokens(self.tgt_lang							)

							def 				lowerCamelCase_			(							self:     List[Any]   ,						UpperCamelCase_:     str							)      ->       None:





									"""simple docstring"""







									lowercase__						=				self.convert_tokens_to_ids(UpperCamelCase_							)
									lowercase__						=				[]
									lowercase__						=				[self.eos_token_id, self.cur_lang_code]

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

									lowercase__						=				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 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     str							)      ->       None:





									"""simple docstring"""







									lowercase__						=				self.convert_tokens_to_ids(UpperCamelCase_							)
									lowercase__						=				[]
									lowercase__						=				[self.eos_token_id, self.cur_lang_code]

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

									lowercase__						=				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 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Optional[str] = None							)      ->       Tuple[str]:





									"""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(UpperCamelCase_							):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory.'							)
											return
									lowercase__						=				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_							):
											copyfile(self.vocab_file   ,						UpperCamelCase_							)

									return (out_vocab_file,)





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase	=							version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
			from nltk import word_tokenize


lowerCAmelCase	=							'\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

lowerCAmelCase	=							'\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

lowerCAmelCase	=							'\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'


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

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py''']   ,						reference_urls=[
									        '''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
									        '''https://en.wikipedia.org/wiki/METEOR''',
									    ]   ,						)

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     List[str]							)      ->       Union[str, Any]:





									"""simple docstring"""







									import nltk

									nltk.download('''wordnet'''							)
									if NLTK_VERSION >= version.Version('''3.6.5'''							):
											nltk.download('''punkt'''							)
									if NLTK_VERSION >= version.Version('''3.6.6'''							):
											nltk.download('''omw-1.4'''							)


							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Dict=0.9   ,						UpperCamelCase_:     Union[str, Any]=3   ,						UpperCamelCase_:     Optional[int]=0.5							)      ->       Dict:





									"""simple docstring"""







									if NLTK_VERSION >= version.Version('''3.6.5'''							):
											lowercase__						=				[
											    meteor_score.single_meteor_score(
											        word_tokenize(UpperCamelCase_							)   ,						word_tokenize(UpperCamelCase_							)   ,						alpha=UpperCamelCase_   ,						beta=UpperCamelCase_   ,						gamma=UpperCamelCase_							)
											    for ref, pred in zip(UpperCamelCase_   ,						UpperCamelCase_							)
											]
									else:
											lowercase__						=				[
											    meteor_score.single_meteor_score(UpperCamelCase_   ,						UpperCamelCase_   ,						alpha=UpperCamelCase_   ,						beta=UpperCamelCase_   ,						gamma=UpperCamelCase_							)
											    for ref, pred in zip(UpperCamelCase_   ,						UpperCamelCase_							)
											]

									return {"meteor": np.mean(UpperCamelCase_							)}


