




import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


__A						:							Dict							=  """\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

__A						:							Tuple							=  """\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

__A						:							Union[str, Any]							=  """
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""




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




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					int:
										return datasets.MetricInfo(
										    description=_DESCRIPTION       ,     citation=_CITATION       ,     inputs_description=_KWARGS_DESCRIPTION       ,     features=datasets.Features(
										        {
										            """predictions""": datasets.Sequence(datasets.Value("""string"""       ,     id="""token"""			)       ,     id="""sequence"""			),
										            """references""": datasets.Sequence(
										                datasets.Sequence(datasets.Value("""string"""       ,     id="""token"""			)       ,     id="""sequence"""			)       ,     id="""references"""			),
										        }			)       ,     codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]       ,     reference_urls=[
										        """https://en.wikipedia.org/wiki/BLEU""",
										        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
										    ]       ,     )







							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	List[Any]       ,     a       :	str       ,     a       :	Any=4       ,     a       :	Dict=False			)			->					str:
										SCREAMING_SNAKE_CASE           =					compute_bleu(
										    reference_corpus=a       ,     translation_corpus=a       ,     max_order=a       ,     smooth=a			)
										((SCREAMING_SNAKE_CASE)     ,  (SCREAMING_SNAKE_CASE)     ,  (SCREAMING_SNAKE_CASE)     ,  (SCREAMING_SNAKE_CASE)     ,  (SCREAMING_SNAKE_CASE)     ,  (SCREAMING_SNAKE_CASE))           =					score
										return {
										    "bleu": bleu,
										    "precisions": precisions,
										    "brevity_penalty": bp,
										    "length_ratio": ratio,
										    "translation_length": translation_length,
										    "reference_length": reference_length,
										}












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:
		__A						:							Tuple							=  None


__A						:							Any							=  logging.get_logger(__name__)


__A						:							Union[str, Any]							=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__A						:							List[Any]							=  {
    """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""",
    },
}

__A						:							int							=  {
    """facebook/mbart-large-en-ro""": 1_0_2_4,
    """facebook/mbart-large-cc25""": 1_0_2_4,
}

# fmt: off
__A						:							List[Any]							=  ["""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_      (   A       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     ['''input_ids''', '''attention_mask''']
							a__						  =     MBartTokenizer

							a__						  =     []
							a__						  =     []





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

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

										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(a			) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

										SCREAMING_SNAKE_CASE           =					src_lang if src_lang is not None else """en_XX"""
										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    _UpperCAmelCase     (					self       :	int			)			->					str:
										return self._src_lang





							@src_lang.setter
							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					new_src_lang
										self.set_src_lang_special_tokens(self._src_lang			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = 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    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										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    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	Optional[int]       ,     a       :	str       ,     a       :	Optional[str]       ,     a       :	Optional[str]       ,     **a       :	Optional[Any]			)			->					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"""			)
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					self(a       ,     add_special_tokens=a       ,     return_tensors=a       ,     **a			)
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					tgt_lang_id
										return inputs





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	List[str]       ,     a       :	str = "en_XX"       ,     a       :	Optional[List[str]] = None       ,     a       :	str = "ro_RO"       ,     **a       :	List[str]       ,     )			->					BatchEncoding:
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					tgt_lang
										return super().prepare_seqaseq_batch(a       ,     a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					str:
										return self.set_src_lang_special_tokens(self.src_lang			)





							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										return self.set_tgt_lang_special_tokens(self.tgt_lang			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[str]			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id, self.cur_lang_code]

										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    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id, self.cur_lang_code]

										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    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple[str]:
										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(a			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""			)
													return
										SCREAMING_SNAKE_CASE           =					os.path.join(
										    a       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

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

										return (out_vocab_file,)






