






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


__snake_case      :      str             =       logging.get_logger(__name__)

__snake_case      :      List[Any]             =       """▁"""

__snake_case      :      Tuple             =       {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

__snake_case      :      List[Any]             =       {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

__snake_case      :      Dict             =       {"""vinai/bartpho-syllable""": 10_24}


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
      _SCREAMING_SNAKE_CASE		:    int						=   VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE		:    Union[str, Any]						=   PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE		:    Optional[int]						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE		:    Tuple						=   ['''input_ids''', '''attention_mask''']






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






            """simple docstring"""
            lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else mask_token

            lowerCAmelCase__        =  {} 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       ,  sp_model_kwargs=self.sp_model_kwargs       ,  **_UpperCamelCase       ,  )

            lowerCAmelCase__        =  vocab_file
            lowerCAmelCase__        =  monolingual_vocab_file
            lowerCAmelCase__        =  spm.SentencePieceProcessor(**self.sp_model_kwargs				)
            self.sp_model.Load(str(_UpperCamelCase				)				)

            # Load the reduced vocab

            # Keep order of special tokens for backward compatibility
            lowerCAmelCase__        =  {}
            lowerCAmelCase__        =  0
            for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
                  if str(_UpperCamelCase				) not in self.fairseq_tokens_to_ids:
                        lowerCAmelCase__        =  cnt
                        cnt += 1
            with open(_UpperCamelCase       ,  'r'       ,  encoding='utf-8'				) as f:
                  for line in f.readlines():
                        lowerCAmelCase__        =  line.strip().split()[0]
                        lowerCAmelCase__        =  len(self.fairseq_tokens_to_ids				)
            if str(_UpperCamelCase				) not in self.fairseq_tokens_to_ids:
                  lowerCAmelCase__        =  len(self.fairseq_tokens_to_ids				)

            lowerCAmelCase__        =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






      def __getstate__(		self				):






            """simple docstring"""
            lowerCAmelCase__        =  self.__dict__.copy()
            lowerCAmelCase__        =  None
            lowerCAmelCase__        =  self.sp_model.serialized_model_proto()
            return state






      def __setstate__(		self       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  d

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

            lowerCAmelCase__        =  spm.SentencePieceProcessor(**self.sp_model_kwargs				)
            self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






            """simple docstring"""

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






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = False				):






            """simple docstring"""

            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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






            """simple docstring"""
            lowerCAmelCase__        =  [self.sep_token_id]
            lowerCAmelCase__        =  [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  UpperCamelCase__ (		self				):






            """simple docstring"""
            return len(self.fairseq_ids_to_tokens				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  {self.convert_ids_to_tokens(_UpperCamelCase				): i for i in range(self.vocab_size				)}
            vocab.update(self.added_tokens_encoder				)
            return vocab






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






            """simple docstring"""
            return self.sp_model.encode(_UpperCamelCase       ,  out_type=_UpperCamelCase				)






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






            """simple docstring"""
            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            else:
                  return self.unk_token_id






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






            """simple docstring"""
            return self.fairseq_ids_to_tokens[index]






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  ''.join(_UpperCamelCase				).replace(_UpperCamelCase       ,  ' '				).strip()
            return out_string







      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






            """simple docstring"""
            if not os.path.isdir(_UpperCamelCase				):
                  logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
                  return
            lowerCAmelCase__        =  os.path.join(
                _UpperCamelCase       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
            lowerCAmelCase__        =  os.path.join(
                _UpperCamelCase       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_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:
                        lowerCAmelCase__        =  self.sp_model.serialized_model_proto()
                        fi.write(_UpperCamelCase				)

            if os.path.abspath(self.monolingual_vocab_file				) != os.path.abspath(
                _UpperCamelCase				) and os.path.isfile(self.monolingual_vocab_file				):
                  copyfile(self.monolingual_vocab_file       ,  _UpperCamelCase				)
            elif not os.path.isfile(self.monolingual_vocab_file				):
                  with open(_UpperCamelCase       ,  'w'       ,  encoding='utf-8'				) as fp:
                        for token in self.fairseq_tokens_to_ids:
                              if token not in self.all_special_tokens:
                                    fp.write(F"{str(_UpperCamelCase				)} \n"				)

            return out_vocab_file, out_monolingual_vocab_file













import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__snake_case      :      Optional[int]             =       """\
@inproceedings{snover-etal-2006-study,
    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
    author = \"Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John\",
    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
    month = aug # \" 8-12\",
    year = \"2006\",
    address = \"Cambridge, Massachusetts, USA\",
    publisher = \"Association for Machine Translation in the Americas\",
    url = \"https://aclanthology.org/2006.amta-papers.25\",
    pages = \"223--231\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__snake_case      :      List[str]             =       """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""

__snake_case      :      Dict             =       """
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""


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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if version.parse(scb.__version__				) < version.parse('1.4.12'				):
																		raise ImportWarning(
																		    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
																		    'You can install it with `pip install "sacrebleu>=1.4.12"`.'				)
												return datasets.MetricInfo(
												    description=_DESCRIPTION       ,  citation=_CITATION       ,  homepage='http://www.cs.umd.edu/~snover/tercom/'       ,  inputs_description=_KWARGS_DESCRIPTION       ,  features=datasets.Features(
												        {
												            'predictions': datasets.Value('string'       ,  id='sequence'				),
												            'references': datasets.Sequence(datasets.Value('string'       ,  id='sequence'				)       ,  id='references'				),
												        }				)       ,  codebase_urls=['https://github.com/mjpost/sacreBLEU#ter']       ,  reference_urls=[
												        'https://github.com/jhclark/tercom',
												    ]       ,  )







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  len(references[0]				)
												if any(len(_UpperCamelCase				) != references_per_prediction for refs in references				):
																		raise ValueError('Sacrebleu requires the same number of references for each prediction'				)
												lowerCAmelCase__        =  [[refs[i] for refs in references] for i in range(_UpperCamelCase				)]

												lowerCAmelCase__        =  TER(
												    normalized=_UpperCamelCase       ,  no_punct=_UpperCamelCase       ,  asian_support=_UpperCamelCase       ,  case_sensitive=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  sb_ter.corpus_score(_UpperCamelCase       ,  _UpperCamelCase				)

												return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}





