

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCAmelCase_       	=	{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

lowerCAmelCase_       	=	{
    '''camembert-base''': 5_12,
}

lowerCAmelCase_       	=	'''▁'''





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[str]									=				VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE     :   str									=				PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE     :   Tuple									=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE     :   str									=				["input_ids", "attention_mask"]







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

									snake_case_   =	{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_UpperCamelCase							,   eos_token=_UpperCamelCase							,   unk_token=_UpperCamelCase							,   sep_token=_UpperCamelCase							,   cls_token=_UpperCamelCase							,   pad_token=_UpperCamelCase							,   mask_token=_UpperCamelCase							,   additional_special_tokens=_UpperCamelCase							,   sp_model_kwargs=self.sp_model_kwargs							,   **_UpperCamelCase							,   )
									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(str(_UpperCamelCase							)							)
									snake_case_   =	vocab_file
									# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
									# sentencepiece vocabulary (this is the case for <s> and </s>
									snake_case_   =	{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
									snake_case_   =	len(self.fairseq_tokens_to_ids							)
									snake_case_   =	len(self.sp_model							) + len(self.fairseq_tokens_to_ids							)
									snake_case_   =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:

									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									snake_case_   =	[self.cls_token_id]
									snake_case_   =	[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							,   _UpperCamelCase :				bool = False							)      ->List[int]:
									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=_UpperCamelCase							,   token_ids_a=_UpperCamelCase							,   already_has_special_tokens=_UpperCamelCase							)

									if token_ids_a is None:
															return [1] + ([0] * len(_UpperCamelCase							)) + [1]
									return [1] + ([0] * len(_UpperCamelCase							)) + [1, 1] + ([0] * len(_UpperCamelCase							)) + [1]







			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									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]







			@property
			def 				snake_case__(     self :				Any							)      ->Union[str, Any]:
									return len(self.fairseq_tokens_to_ids							) + len(self.sp_model							)







			def 				snake_case__(     self :				Any							)      ->List[Any]:
									snake_case_   =	{self.convert_ids_to_tokens(_UpperCamelCase							): i for i in range(self.vocab_size							)}
									vocab.update(self.added_tokens_encoder							)
									return vocab







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				str							)      ->List[str]:
									return self.sp_model.encode(_UpperCamelCase							,   out_type=_UpperCamelCase							)







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				int							)      ->Union[str, Any]:
									if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
									elif self.sp_model.PieceToId(_UpperCamelCase							) == 0:
															# Convert sentence piece unk token to fairseq unk token index
															return self.unk_token_id
									return self.fairseq_offset + self.sp_model.PieceToId(_UpperCamelCase							)







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				Tuple							)      ->Any:
									if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset							)







			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				str							)      ->Any:
									snake_case_   =	[]
									snake_case_   =	''''''
									snake_case_   =	False
									for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																					if not prev_is_special:
																											out_string += " "
																					out_string += self.sp_model.decode(_UpperCamelCase							) + token
																					snake_case_   =	True
																					snake_case_   =	[]
															else:
																					current_sub_tokens.append(_UpperCamelCase							)
																					snake_case_   =	False
									out_string += self.sp_model.decode(_UpperCamelCase							)
									return out_string.strip()







			def __getstate__(     self :				List[Any]							)      ->str:
									snake_case_   =	self.__dict__.copy()
									snake_case_   =	None
									return state







			def __setstate__(     self :				int							,   _UpperCamelCase :				str							)      ->str:
									snake_case_   =	d

									# for backward compatibility
									if not hasattr(self							,   '''sp_model_kwargs'''							):
															snake_case_   =	{}

									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(self.vocab_file							)







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[str] = None							)      ->Tuple[str]:
									if not os.path.isdir(_UpperCamelCase							):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
															return
									snake_case_   =	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							) and os.path.isfile(self.vocab_file							):
															copyfile(self.vocab_file							,   _UpperCamelCase							)
									elif not os.path.isfile(self.vocab_file							):
															with open(_UpperCamelCase							,   '''wb'''							) as fi:
																					snake_case_   =	self.sp_model.serialized_model_proto()
																					fi.write(_UpperCamelCase							)

									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


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


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

_SCREAMING_SNAKE_CASE					:				int					    =						'''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''

_SCREAMING_SNAKE_CASE					:				List[Any]					    =						'''
Computes METEOR score of translated segments against one or more references.
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    \'meteor\': meteor score.
Examples:

    >>> meteor = datasets.load_metric(\'meteor\')
    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results["meteor"], 4))
    0.6944
'''






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






    def    UpperCamelCase       (       self     :							List[str]					)     ->		List[str]:
      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    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]					)     ->		Dict:
      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    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=0.9       ,						__SCREAMING_SNAKE_CASE     :							Dict=3       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=0.5					)     ->		int:
      if NLTK_VERSION >= version.Version('3.6.5'					):
        lowerCamelCase_         =						[
            meteor_score.single_meteor_score(
                word_tokenize(__SCREAMING_SNAKE_CASE					)       ,						word_tokenize(__SCREAMING_SNAKE_CASE					)       ,						alpha=__SCREAMING_SNAKE_CASE       ,						beta=__SCREAMING_SNAKE_CASE       ,						gamma=__SCREAMING_SNAKE_CASE					)
            for ref, pred in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        ]
      else:
        lowerCamelCase_         =						[
            meteor_score.single_meteor_score(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						alpha=__SCREAMING_SNAKE_CASE       ,						beta=__SCREAMING_SNAKE_CASE       ,						gamma=__SCREAMING_SNAKE_CASE					)
            for ref, pred in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        ]

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




