
'''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_mbart import MBartTokenizer
else:
					lowercase					:							List[str]   =      None


lowercase					:							Any   =      logging.get_logger(__name__)


lowercase					:							int   =      {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowercase					:							int   =      {
    "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",
    },
}

lowercase					:							str   =      {
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
lowercase					:							Dict   =      ["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 		__UpperCAmelCase   (      _lowerCamelCase		):
							__lowercase   	= VOCAB_FILES_NAMES
							__lowercase   	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowercase   	= PRETRAINED_VOCAB_FILES_MAP
							__lowercase   	= ["""input_ids""", """attention_mask"""]
							__lowercase   	= MBartTokenizer

							__lowercase   	= []
							__lowercase   	= []


							def __init__( self					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     lowerCAmelCase_="<s>"					,     lowerCAmelCase_="</s>"					,     lowerCAmelCase_="</s>"					,     lowerCAmelCase_="<s>"					,     lowerCAmelCase_="<unk>"					,     lowerCAmelCase_="<pad>"					,     lowerCAmelCase_="<mask>"					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	AddedToken(lowerCAmelCase_					,     lstrip=lowerCAmelCase_					,     rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_					,     lowerCAmelCase_						) else mask_token

								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_					,     **lowerCAmelCase_					,     )

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

								_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}						)
								_snake_case     		=	{
								    lang_code: self.convert_tokens_to_ids(lowerCAmelCase_						) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

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


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self._src_lang


							@src_lang.setter
							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	new_src_lang
								self.set_src_lang_special_tokens(self._src_lang						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_ = 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 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_ = None						):





								"""simple docstring"""






								_snake_case     		=	[self.sep_token_id]
								_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 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""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'						)
								_snake_case     		=	src_lang
								_snake_case     		=	self(lowerCAmelCase_					,     add_special_tokens=lowerCAmelCase_					,     return_tensors=lowerCAmelCase_					,     **lowerCAmelCase_						)
								_snake_case     		=	self.convert_tokens_to_ids(lowerCAmelCase_						)
								_snake_case     		=	tgt_lang_id
								return inputs


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_ = "en_XX"					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = "ro_RO"					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	src_lang
								_snake_case     		=	tgt_lang
								return super().prepare_seqaseq_batch(lowerCAmelCase_					,     lowerCAmelCase_					,     **lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.set_src_lang_special_tokens(self.src_lang						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.set_tgt_lang_special_tokens(self.tgt_lang						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.convert_tokens_to_ids(lowerCAmelCase_						)
								_snake_case     		=	[]
								_snake_case     		=	[self.eos_token_id, self.cur_lang_code]

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

								_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 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.convert_tokens_to_ids(lowerCAmelCase_						)
								_snake_case     		=	[]
								_snake_case     		=	[self.eos_token_id, self.cur_lang_code]

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

								_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 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_ = 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
								_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,)



'''simple docstring'''







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase					:							str   =      version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
					from nltk import word_tokenize


lowercase					:							int   =      "\\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"

lowercase					:							Any   =      "\\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"

lowercase					:							Optional[Any]   =      "\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 		__UpperCAmelCase   (      datasets.Metric		):


							def 				lowerCamelCase       ( self						):





								"""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					,     lowerCAmelCase_						):





								"""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					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_=0.9					,     lowerCAmelCase_=3					,     lowerCAmelCase_=0.5						):





								"""simple docstring"""






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

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

