
'''simple docstring'''





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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						"▁"

lowercase_	 =						{"vocab_file": "prophetnet.tokenizer"}

lowercase_	 =						{
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

lowercase_	 =						{
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

lowercase_	 =						{
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}




def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						collections.OrderedDict()
     with open(__A						,			'''r'''						,			encoding='''utf-8''') as reader:
          _a											=						reader.readlines()
     for index, token in enumerate(__A):
          _a											=						token.rstrip('''\n''')
          _a											=						index
     return vocab






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Any    = VOCAB_FILES_NAMES
 __lowerCamelCase					: List[Any]    = PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase					: Optional[Any]    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase					: List[Any]    = ['input_ids', 'attention_mask']



 def __init__(self				,     A				,     A="[SEP]"				,     A="[SEP]"				,     A="[SEP]"				,     A="[UNK]"				,     A="[PAD]"				,     A="[CLS]"				,     A="[MASK]"				,     A = None				,     **A				,     )					->  None:


      """simple docstring"""



      _a											=						{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=A				,     eos_token=A				,     sep_token=A				,     unk_token=A				,     pad_token=A				,     cls_token=A				,     mask_token=A				,     sp_model_kwargs=self.sp_model_kwargs				,     **A				,     )

      try:
           import sentencepiece as spm
      except ImportError:
           logger.warning(
               '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
               ''' pip install sentencepiece''' )
           raise

      _a											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
      self.sp_model.Load(str(A ) )
      _a											=						vocab_file

      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

      # put special tokens and [unused] tokens into the vocab
      _a											=						{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

      for i in range(10 ):
           _a											=						f'''[unused{i}]'''
           _a											=						5 + i

      # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
      _a											=						12
      _a											=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
      for k in self.fairseq_tokens_to_ids.keys():
           self.unique_no_split_tokens.append(A )



 def __getstate__(self )					->  int:


      """simple docstring"""



      _a											=						self.__dict__.copy()
      _a											=						None
      return state



 def __setstate__(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						d
      try:
           import sentencepiece as spm
      except ImportError:
           logger.warning(
               '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
               ''' pip install sentencepiece''' )
           raise

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

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



 def       a__						(self				,     A				,     A = None				,     A = False )					->  List[int]:


      """simple docstring"""



      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=A				,     token_ids_a=A				,     already_has_special_tokens=A )

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



 def       a__						(self				,     A				,     A = None )					->  List[int]:


      """simple docstring"""



      _a											=						[self.sep_token_id]

      if token_ids_a is None:
           return len(token_ids_a + sep ) * [0]
      return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]



 @property
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      return len(self.sp_model ) + self.fairseq_offset



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						{self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
      vocab.update(self.added_tokens_encoder )
      return vocab



 def       a__						(self				,     A )					->  str:


      """simple docstring"""



      return self.sp_model.encode(A				,     out_type=A )



 def       a__						(self				,     A )					->  Any:


      """simple docstring"""



      if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
      _a											=						self.sp_model.PieceToId(A )

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



 def       a__						(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



      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       a__						(self				,     A )					->  Optional[Any]:


      """simple docstring"""



      _a											=						''''''.join(A ).replace(A				,     ''' ''' ).strip()
      return out_string



 def       a__						(self				,     A				,     A = None )					->  Tuple[str]:


      """simple docstring"""



      if not os.path.isdir(A ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
           return
      _a											=						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 ) and os.path.isfile(self.vocab_file ):
           copyfile(self.vocab_file				,     A )
      elif not os.path.isfile(self.vocab_file ):
           with open(A				,     '''wb''' ) as fi:
                _a											=						self.sp_model.serialized_model_proto()
                fi.write(A )

      return (out_vocab_file,)



 def       a__						(self				,     A				,     A = None )					->  List[int]:


      """simple docstring"""



      if token_ids_a is None:
           return token_ids_a + [self.sep_token_id]
      _a											=						[self.sep_token_id]
      return token_ids_a + sep + token_ids_a + sep








'''simple docstring'''





import datasets

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


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

lowercase_	 =						"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality 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,\nthe 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\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU'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\nrepresenting 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\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

lowercase_	 =						"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"






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



 '''simple docstring'''







 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      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       a__						(self				,     A				,     A				,     A=4				,     A=False )					->  Tuple:


      """simple docstring"""



      _a											=						compute_bleu(
          reference_corpus=A				,     translation_corpus=A				,     max_order=A				,     smooth=A )
      ((_a)						,					(_a)						,					(_a)						,					(_a)						,					(_a)						,					(_a))											=						score
      return {
          "bleu": bleu,
          "precisions": precisions,
          "brevity_penalty": bp,
          "length_ratio": ratio,
          "translation_length": translation_length,
          "reference_length": reference_length,
      }



