
'''simple docstring'''

def 		_UpperCAmelCase (     _lowerCamelCase  :				list[list[int]]     ,					_lowerCamelCase  :				int     ,					_lowerCamelCase  :				int     ,					_lowerCamelCase  :				list[int]						)			->  Union[str, Any]:

  # 1. Validate that path exists between current and next vertices
  if graph[path[curr_ind - 1]][next_ver] == 0:
    return False

  # 2. Validate that next vertex is not already in path
  return not any(vertex == next_ver for vertex in path						)


def 		_UpperCAmelCase (     _lowerCamelCase  :				list[list[int]]     ,					_lowerCamelCase  :				list[int]     ,					_lowerCamelCase  :				int						)			->  Dict:

  # Base Case
  if curr_ind == len(_lowerCamelCase						):
    # return whether path exists between current and starting vertices
    return graph[path[curr_ind - 1]][path[0]] == 1

  # Recursive Step
  for next_ver in range(0     ,					len(_lowerCamelCase						)						):
    if valid_connection(_lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase						):
      # Insert current vertex  into path as next transition
      _lowerCAmelCase				:     Any       =    next_ver
      # Validate created path
      if util_hamilton_cycle(_lowerCamelCase     ,					_lowerCamelCase     ,					curr_ind + 1						):
        return True
      # Backtrack
      _lowerCAmelCase				:     Optional[Any]       =    -1
  return False



def 		_UpperCAmelCase (     _lowerCamelCase  :				list[list[int]]     ,					_lowerCamelCase  :				int = 0						)			->  int:
  _lowerCAmelCase				:     Optional[Any]       =    [-1] * (len(_lowerCamelCase						) + 1)
  # initialize start and end of path with starting index
  _lowerCAmelCase				:     List[Any]       =    start_index
  # evaluate and if we find answer return path either return empty array
  return path if util_hamilton_cycle(_lowerCamelCase     ,					_lowerCamelCase     ,					1						) else []



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

import datasets


UpperCamelCase  							=						'''\
@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",
}
'''

UpperCamelCase  							=						'''\
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.
'''

UpperCamelCase  							=						'''
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   snake_case_       (		datasets.Metric       ):






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						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       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     str	,						lowercase_				:     Optional[int]	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						)					->						Any:
       lowercase__ :   Optional[int]    					=				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"  )
       lowercase__ :   Union[str, Any]    					=				[[refs[i] for refs in references] for i in range(lowercase_  )]

       lowercase__ :   str    					=				TER(
           normalized=lowercase_	,						no_punct=lowercase_	,						asian_support=lowercase_	,						case_sensitive=lowercase_	,						)
       lowercase__ :   List[str]    					=				sb_ter.corpus_score(lowercase_	,						lowercase_  )

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



