code: 
  
  '''simple docstring'''
  
  
  
  
  
  import sacrebleu as scb
  from packaging import version
  
  import datasets
  
  
  _lowerCAmelCase     :			Dict    ="""\
  @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\",
  }
  """
  
  _lowerCAmelCase     :			List[Any]    ="""\
  SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
  Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
  It also knows all the standard test sets and handles downloading, processing, and tokenization for you.
  
  See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
  """
  
  _lowerCAmelCase     :			Tuple    ="""
  Produces BLEU scores along with its sufficient statistics
  from a source against one or more references.
  
  Args:
      predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
      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).
      smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
          - `'none'`: no smoothing
          - `'floor'`: increment zero counts
          - `'add-k'`: increment num/denom by k for n>1
          - `'exp'`: exponential decay
      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`).
      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:
          - `'none'`: No tokenization.
          - `'zh'`: Chinese tokenization.
          - `'13a'`: mimics the `mteval-v13a` script from Moses.
          - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
          - `'char'`: Language-agnostic character-level tokenization.
          - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
      lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
      force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
      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`.
  
  Returns:
      'score': BLEU score,
      'counts': Counts,
      'totals': Totals,
      'precisions': Precisions,
      'bp': Brevity penalty,
      'sys_len': predictions length,
      'ref_len': reference length,
  
  Examples:
  
      Example 1:
          >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
          >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
          >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
          >>> results = sacrebleu.compute(predictions=predictions, references=references)
          >>> print(list(results.keys()))
          ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
          >>> print(round(results[\"score\"], 1))
          100.0
  
      Example 2:
          >>> predictions = [\"hello there general kenobi\",
          ...                 \"on our way to ankh morpork\"]
          >>> references = [[\"hello there general kenobi\", \"hello there !\"],
          ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
          >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
          >>> results = sacrebleu.compute(predictions=predictions,
          ...                             references=references)
          >>> print(list(results.keys()))
          ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
          >>> print(round(results[\"score\"], 1))
          39.8
  """
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,_KWARGS_DESCRIPTION		)
  class __UpperCamelCase    (							datasets.Metric		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						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							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__="exp"							,			lowerCamelCase__=None							,			lowerCamelCase__=False							,			lowerCamelCase__=False							,			lowerCamelCase__=None							,			lowerCamelCase__=False							,			):
  						UpperCAmelCase__:  List[str]		       =   len(references[0]	)
  						if any(len(lowerCamelCase__	) != references_per_prediction for refs in references	):
  							raise ValueError("Sacrebleu requires the same number of references for each prediction"	)
  						UpperCAmelCase__:  Tuple		       =   [[refs[i] for refs in references] for i in range(lowerCamelCase__	)]
  						UpperCAmelCase__:  int		       =   scb.corpus_bleu(
  						    lowerCamelCase__							,			lowerCamelCase__							,			smooth_method=lowerCamelCase__							,			smooth_value=lowerCamelCase__							,			force=lowerCamelCase__							,			lowercase=lowerCamelCase__							,			use_effective_order=lowerCamelCase__							,			**({"tokenize": tokenize} if tokenize else {})							,			)
  						UpperCAmelCase__:  Optional[Any]		       =   {
  						    "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
style_context: 
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,):
  	UpperCAmelCase__:  Tuple		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Dict		       =   len(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   len(SCREAMING_SNAKE_CASE			)
  
  	UpperCAmelCase__:  Any		       =   [
  	    [0 for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
  	]
  	UpperCAmelCase__:  List[Any]		       =   [
  	    ["0" for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
  	]
  
  	for i in range(1  ,len_source_seq + 1			):
  		UpperCAmelCase__:  List[Any]		       =   i * delete_cost
  		UpperCAmelCase__:  str		       =   f"D{source_seq[i - 1]:c}"
  
  	for i in range(1  ,len_destination_seq + 1			):
  		UpperCAmelCase__:  Union[str, Any]		       =   i * insert_cost
  		UpperCAmelCase__:  Optional[Any]		       =   f"I{destination_seq[i - 1]:c}"
  
  	for i in range(1  ,len_source_seq + 1			):
  		for j in range(1  ,len_destination_seq + 1			):
  			if source_seq[i - 1] == destination_seq[j - 1]:
  				UpperCAmelCase__:  Any		       =   costs[i - 1][j - 1] + copy_cost
  				UpperCAmelCase__:  Tuple		       =   f"C{source_seq[i - 1]:c}"
  			else:
  				UpperCAmelCase__:  Union[str, Any]		       =   costs[i - 1][j - 1] + replace_cost
  				UpperCAmelCase__:  Optional[int]		       =   f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]			)
  
  			if costs[i - 1][j] + delete_cost < costs[i][j]:
  				UpperCAmelCase__:  Optional[int]		       =   costs[i - 1][j] + delete_cost
  				UpperCAmelCase__:  int		       =   f"D{source_seq[i - 1]:c}"
  
  			if costs[i][j - 1] + insert_cost < costs[i][j]:
  				UpperCAmelCase__:  Any		       =   costs[i][j - 1] + insert_cost
  				UpperCAmelCase__:  Tuple		       =   f"I{destination_seq[j - 1]:c}"
  
  	return costs, ops
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if i == 0 and j == 0:
  		return []
  	else:
  		if ops[i][j][0] == "C" or ops[i][j][0] == "R":
  			UpperCAmelCase__:  Union[str, Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  		elif ops[i][j][0] == "D":
  			UpperCAmelCase__:  Optional[Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,SCREAMING_SNAKE_CASE			)
  			seq.append(ops[i][j]			)
  			return seq
  		else:
  			UpperCAmelCase__:  Dict		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase   ,  _lowerCAmelCase     :			Dict    =compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)
  
  					_lowerCAmelCase     :			List[Any]    =len(operations)
  					_lowerCAmelCase     :			Optional[int]    =len(operations[0])
  					_lowerCAmelCase     :			Dict    =assemble_transformation(operations, m - 1, n - 1)
  
  					_lowerCAmelCase     :			int    =list("""Python""")
  					_lowerCAmelCase     :			Optional[int]    =0
  					_lowerCAmelCase     :			List[Any]    =0
  
  					with open("""min_cost.txt""", """w""") as file:
  										for op in sequence:
  															print("""""".join(string))
  
  															if op[0] == "C":
  																				file.write("""%-16s""" % """Copy %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost -= 1
  															elif op[0] == "R":
  																				_lowerCAmelCase     :			Tuple    =op[2]
  
  																				file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
  																				file.write("""\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 1
  															elif op[0] == "D":
  																				string.pop(i)
  
  																				file.write("""%-16s""" % """Delete %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 2
  															else:
  																				string.insert(i, op[1])
  
  																				file.write("""%-16s""" % """Insert %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 2
  
  															i += 1
  
  										print("""""".join(string))
  										print("""Cost: """, cost)
  
  										file.write("""\r\nMinimum cost: """ + str(cost))