





"""simple docstring"""





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

import datasets


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

SCREAMING_SNAKE_CASE__					=   "\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

SCREAMING_SNAKE_CASE__					=   "\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"

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





      def     _snake_case			(       self )		->     Tuple:
            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     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = False      ,						lowercase = False      ,						lowercase = False      ,						lowercase = False      ,						)		->     Optional[int]:
            lowerCAmelCase  						=       len(references[0] )
            if any(len(lowercase ) != 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(lowercase )]

            lowerCAmelCase  						=       TER(
                normalized=lowercase      ,						no_punct=lowercase      ,						asian_support=lowercase      ,						case_sensitive=lowercase      ,						)
            lowerCAmelCase  						=       sb_ter.corpus_score(lowercase      ,						lowercase )

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











'''simple docstring'''





import json
import os
from typing import Optional, Tuple

import regex as re

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


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_lowerCAmelCase      						=	{
    '''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
    '''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}

_lowerCAmelCase      						=	{
    '''ctrl''': 256,
}

_lowerCAmelCase      						=	{
    '''Pregnancy''': 16_8629,
    '''Christianity''': 7675,
    '''Explain''': 10_6423,
    '''Fitness''': 6_3440,
    '''Saving''': 6_3163,
    '''Ask''': 2_7171,
    '''Ass''': 9_5985,
    '''Joke''': 16_3509,
    '''Questions''': 4_5622,
    '''Thoughts''': 4_9605,
    '''Retail''': 5_2342,
    '''Feminism''': 16_4338,
    '''Writing''': 1_1992,
    '''Atheism''': 19_2263,
    '''Netflix''': 4_8616,
    '''Computing''': 3_9639,
    '''Opinion''': 4_3213,
    '''Alone''': 4_4967,
    '''Funny''': 5_8917,
    '''Gaming''': 4_0358,
    '''Human''': 4088,
    '''India''': 1331,
    '''Joker''': 7_7138,
    '''Diet''': 3_6206,
    '''Legal''': 1_1859,
    '''Norman''': 4939,
    '''Tip''': 7_2689,
    '''Weight''': 5_2343,
    '''Movies''': 4_6273,
    '''Running''': 2_3425,
    '''Science''': 2090,
    '''Horror''': 3_7793,
    '''Confession''': 6_0572,
    '''Finance''': 1_2250,
    '''Politics''': 1_6360,
    '''Scary''': 19_1985,
    '''Support''': 1_2654,
    '''Technologies''': 3_2516,
    '''Teenage''': 6_6160,
    '''Event''': 3_2769,
    '''Learned''': 6_7460,
    '''Notion''': 18_2770,
    '''Wikipedia''': 3_7583,
    '''Books''': 6665,
    '''Extract''': 7_6050,
    '''Confessions''': 10_2701,
    '''Conspiracy''': 7_5932,
    '''Links''': 6_3674,
    '''Narcissus''': 15_0425,
    '''Relationship''': 5_4766,
    '''Relationships''': 13_4796,
    '''Reviews''': 4_1671,
    '''News''': 4256,
    '''Translation''': 2_6820,
    '''multilingual''': 12_8406,
}
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[int]							      =					set()
    lowerCAmelCase__ :  str							      =					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)			)
        lowerCAmelCase__ :  List[Any]							      =					char

    lowerCAmelCase__ :  Optional[Any]							      =					set(UpperCamelCase			)
    return pairs






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Dict				=      VOCAB_FILES_NAMES
  __lowercase							:      Dict				=      PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:      Optional[Any]				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase							:      Any				=      CONTROL_CODES




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase="<unk>"  ,**__UpperCAmelCase      )    ->		Optional[int]:
      super().__init__(unk_token=__UpperCAmelCase  ,**__UpperCAmelCase      )

      with open(__UpperCAmelCase  ,encoding="""utf-8"""      ) as vocab_handle:
          lowerCAmelCase__ :  int							      =					json.load(__UpperCAmelCase      )
      lowerCAmelCase__ :  Any							      =					{v: k for k, v in self.encoder.items()}
      with open(__UpperCAmelCase  ,encoding="""utf-8"""      ) as merges_handle:
          lowerCAmelCase__ :  Union[str, Any]							      =					merges_handle.read().split("""\n"""      )[1:-1]
      lowerCAmelCase__ :  Optional[Any]							      =					[tuple(merge.split()      ) for merge in merges]
      lowerCAmelCase__ :  int							      =					dict(zip(__UpperCAmelCase  ,range(len(__UpperCAmelCase      )      )      )      )
      lowerCAmelCase__ :  List[Any]							      =					{}




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      return len(self.encoder      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      return dict(self.encoder  ,**self.added_tokens_encoder      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		int:
      if token in self.cache:
          return self.cache[token]
      lowerCAmelCase__ :  Optional[Any]							      =					tuple(__UpperCAmelCase      )
      lowerCAmelCase__ :  List[str]							      =					tuple(list(word[:-1]      ) + [word[-1] + """</w>"""]      )
      lowerCAmelCase__ :  Any							      =					get_pairs(__UpperCAmelCase      )

      if not pairs:
          return token

      while True:
          lowerCAmelCase__ :  List[str]							      =					min(__UpperCAmelCase  ,key=lambda __UpperCAmelCase      : self.bpe_ranks.get(__UpperCAmelCase  ,float("""inf"""      )      )      )
          if bigram not in self.bpe_ranks:
              break
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					bigram
          lowerCAmelCase__ :  List[str]							      =					[]
          lowerCAmelCase__ :  Dict							      =					0
          while i < len(__UpperCAmelCase      ):
              try:
                  lowerCAmelCase__ :  Optional[Any]							      =					word.index(__UpperCAmelCase  ,__UpperCAmelCase      )
              except ValueError:
                  new_word.extend(word[i:]      )
                  break
              else:
                  new_word.extend(word[i:j]      )
                  lowerCAmelCase__ :  Dict							      =					j

              if word[i] == first and i < len(__UpperCAmelCase      ) - 1 and word[i + 1] == second:
                  new_word.append(first + second      )
                  i += 2
              else:
                  new_word.append(word[i]      )
                  i += 1
          lowerCAmelCase__ :  Tuple							      =					tuple(__UpperCAmelCase      )
          lowerCAmelCase__ :  Tuple							      =					new_word
          if len(__UpperCAmelCase      ) == 1:
              break
          else:
              lowerCAmelCase__ :  Dict							      =					get_pairs(__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					"""@@ """.join(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[Any]							      =					word[:-4]
      lowerCAmelCase__ :  Optional[Any]							      =					word
      return word




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Dict:
      lowerCAmelCase__ :  Optional[int]							      =					[]

      lowerCAmelCase__ :  Any							      =					re.findall(R"""\S+\n?"""  ,__UpperCAmelCase      )

      for token in words:
          split_tokens.extend(list(self.bpe(__UpperCAmelCase      ).split(""" """      )      )      )
      return split_tokens




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      return self.encoder.get(__UpperCAmelCase  ,self.encoder.get(self.unk_token      )      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Tuple:
      return self.decoder.get(__UpperCAmelCase  ,self.unk_token      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Tuple:
      lowerCAmelCase__ :  Any							      =					""" """.join(__UpperCAmelCase      ).replace("""@@ """  ,""""""      ).strip()
      return out_string






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		Tuple[str]:
      if not os.path.isdir(__UpperCAmelCase      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      lowerCAmelCase__ :  List[Any]							      =					os.path.join(
          __UpperCAmelCase  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )
      lowerCAmelCase__ :  Optional[int]							      =					os.path.join(
          __UpperCAmelCase  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]      )

      with open(__UpperCAmelCase  ,"""w"""  ,encoding="""utf-8"""      ) as f:
          f.write(json.dumps(self.encoder  ,indent=2  ,sort_keys=__UpperCAmelCase  ,ensure_ascii=__UpperCAmelCase      ) + """\n"""      )

      lowerCAmelCase__ :  int							      =					0
      with open(__UpperCAmelCase  ,"""w"""  ,encoding="""utf-8"""      ) as writer:
          writer.write("""#version: 0.2\n"""      )
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,key=lambda __UpperCAmelCase      : kv[1]      ):
              if index != token_index:
                  logger.warning(
                      F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                      """ Please check that the tokenizer is not corrupted!"""      )
                  lowerCAmelCase__ :  Dict							      =					token_index
              writer.write(""" """.join(__UpperCAmelCase      ) + """\n"""      )
              index += 1

      return vocab_file, merge_file

  # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
  #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
  #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
  #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
  #     return ''.join(tokens_generated_so_far)


