code: 
  
  
  
  
  
  '''simple docstring'''
  
  from dataclasses import dataclass
  
  __lowerCAmelCase							:			Tuple				="0.1"
  __lowerCAmelCase							:			Tuple				="Lucia Harcekova"
  
  
  
  
  
  
  @dataclass
  class      UpperCAmelCase			:
   __lowercase     	=       42
   __lowercase     	=       42
   __lowercase     	=       42
   def __repr__(       self   :Optional[int]							)->							str:
       return F"({self.offset}, {self.length}, {self.indicator})"
  
  
  
  
  
  
  class      UpperCAmelCase			:
   def __init__(       self   :List[str]		, lowercase_   :int = 13		, lowercase_   :int = 6							)->							None:
       A__       =    window_size
       A__       =    lookahead_buffer_size
       A__       =    self.window_size - self.lookahead_buffer_size
   def   UpperCAmelCase_							(       self   :List[Any]		, lowercase_   :str							)->							list[Token]:
       A__       =    []
       A__       =    ""
  
       # while there are still characters in text to compress
       while text:
           # find the next encoding phrase
           # - triplet with offset, length, indicator (the next encoding character)
           A__       =    self._find_encoding_token(lowercase_		, lowercase_							)
  
           # update the search buffer:
           # - add new characters from text into it
           # - check if size exceed the max search buffer size, if so, drop the
           #   oldest elements
           search_buffer += text[: token.length + 1]
           if len(lowercase_							) > self.search_buffer_size:
               A__       =    search_buffer[-self.search_buffer_size :]
  
           # update the text
           A__       =    text[token.length + 1 :]
  
           # append the token to output
           output.append(lowercase_							)
  
       return output
   def   UpperCAmelCase_							(       self   :List[Any]		, lowercase_   :list[Token]							)->							str:
       A__       =    ""
  
       for token in tokens:
           for _ in range(token.length							):
               output += output[-token.offset]
           output += token.indicator
  
       return output
   def   UpperCAmelCase_							(       self   :Any		, lowercase_   :str		, lowercase_   :str							)->							Token:
  
       if not text:
           raise ValueError("We need some text to work with."							)
  
       # Initialise result parameters to default values
       A__,			A__       =    0, 0
  
       if not search_buffer:
           return Token(lowercase_		, lowercase_		, text[length]							)
  
       for i, character in enumerate(lowercase_							):
           A__       =    len(lowercase_							) - i
           if character == text[0]:
               A__       =    self._match_length_from_index(lowercase_		, lowercase_		, 0		, lowercase_							)
               # if the found length is bigger than the current or if it's equal,
               # which means it's offset is smaller: update offset and length
               if found_length >= length:
                   A__,			A__       =    found_offset, found_length
  
       return Token(lowercase_		, lowercase_		, text[length]							)
  
  
  
   def   UpperCAmelCase_							(       self   :Any		, lowercase_   :str		, lowercase_   :str		, lowercase_   :int		, lowercase_   :int							)->							int:
       if not text or text[text_index] != window[window_index]:
           return 0
       return 1 + self._match_length_from_index(
           lowercase_		, window + text[text_index]		, text_index + 1		, window_index + 1							)
  
  
  if __name__ == "__main__":
    from doctest import testmod
  
    testmod()
    # Initialize compressor class
    __lowerCAmelCase							:			Dict				=LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
    # Example
    __lowerCAmelCase							:			Tuple				="cabracadabrarrarrad"
    __lowerCAmelCase							:			Union[str, Any]				=lzaa_compressor.compress(TEXT)
    print(lzaa_compressor.compress("ababcbababaa"))
    __lowerCAmelCase							:			Any				=lzaa_compressor.decompress(compressed_text)
    assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import sacrebleu as scb
  from packaging import version
  
  import datasets
  
  
  __lowerCAmelCase							:			Tuple				="\\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"
  
  __lowerCAmelCase							:			Dict				="\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"
  
  __lowerCAmelCase							:			Dict				="\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    , _KWARGS_DESCRIPTION       )
  class      UpperCAmelCase			( datasets.Metric       ):
   def   UpperCAmelCase_							(       self   :int							)->							Union[str, Any]:
       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="https://github.com/mjpost/sacreBLEU"		, 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"]		, reference_urls=[
               "https://github.com/mjpost/sacreBLEU",
               "https://en.wikipedia.org/wiki/BLEU",
               "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
           ]		, )
  
  
  
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int]		, lowercase_   :Optional[Any]		, lowercase_   :List[str]="exp"		, lowercase_   :str=None		, lowercase_   :Tuple=False		, lowercase_   :Any=False		, lowercase_   :Optional[Any]=None		, lowercase_   :Any=False		, )->							Tuple:
       A__       =    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"							)
       A__       =    [[refs[i] for refs in references] for i in range(lowercase_							)]
       A__       =    scb.corpus_bleu(
           lowercase_		, lowercase_		, smooth_method=lowercase_		, smooth_value=lowercase_		, force=lowercase_		, lowercase=lowercase_		, use_effective_order=lowercase_		, **({"tokenize": tokenize} if tokenize else {})		, )
       A__       =    {
           "score": output.score,
           "counts": output.counts,
           "totals": output.totals,
           "precisions": output.precisions,
           "bp": output.bp,
           "sys_len": output.sys_len,
           "ref_len": output.ref_len,
       }
       return output_dict
  
  
  
  