




import copy
import re




class 					snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[str]											=      """hp"""
				SCREAMING_SNAKE_CASE_ :							str											=      {}
				SCREAMING_SNAKE_CASE_ :							Dict											=      None





				@classmethod
				def 							__UpperCAmelCase							(   cls       :  Any      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Optional[int]							)      ->  Dict:
							a					=    prefix
							a					=    defaults
							cls.build_naming_info()





				@staticmethod
				def 							__UpperCAmelCase							(   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Union[str, Any]							)      ->  Union[str, Any]:
							if len(__lowerCamelCase							) == 0:
										return ""
							a					=    None
							if any(char.isdigit() for char in word							):
										raise Exception(f"""Parameters should not contain numbers: '{word}' contains a number"""							)
							if word in info["short_word"]:
										return info["short_word"][word]
							for prefix_len in range(1      ,   len(__lowerCamelCase							) + 1							):
										a					=    word[:prefix_len]
										if prefix in info["reverse_short_word"]:
													continue
										else:
													a					=    prefix
													break

							if short_word is None:
										# Paranoid fallback
										def int_to_alphabetic(__lowerCamelCase       :  Union[str, Any]							):
													a					=    ""
													while integer != 0:
																a					=    chr(ord("A"							) + integer % 10							) + s
																integer //= 10
													return s

										a					=    0
										while True:
													a					=    word + "#" + int_to_alphabetic(__lowerCamelCase							)
													if sword in info["reverse_short_word"]:
																continue
													else:
																a					=    sword
																break

							a					=    short_word
							a					=    word
							return short_word





				@staticmethod
				def 							__UpperCAmelCase							(   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Optional[int]							)      ->  List[Any]:
							a					=    param_name.split("_"							)

							a					=    [TrialShortNamer.shortname_for_word(__lowerCamelCase      ,   __lowerCamelCase							) for word in words]

							# We try to create a separatorless short name, but if there is a collision we have to fallback
							# to a separated short name
							a					=    ["", "_"]

							for separator in separators:
										a					=    separator.join(__lowerCamelCase							)
										if shortname not in info["reverse_short_param"]:
													a					=    shortname
													a					=    param_name
													return shortname

							return param_name





				@staticmethod
				def 							__UpperCAmelCase							(   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  str							)      ->  Optional[int]:
							a					=    TrialShortNamer.shortname_for_key(__lowerCamelCase      ,   __lowerCamelCase							)
							a					=    short_name
							a					=    param_name





				@classmethod
				def 							__UpperCAmelCase							(   cls       :  str							)      ->  Union[str, Any]:
							if cls.NAMING_INFO is not None:
										return

							a					=    {
							    "short_word": {},
							    "reverse_short_word": {},
							    "short_param": {},
							    "reverse_short_param": {},
							}

							a					=    list(cls.DEFAULTS.keys()							)

							for k in field_keys:
										cls.add_new_param_name(__lowerCamelCase      ,   __lowerCamelCase							)

							a					=    info





				@classmethod
				def 							__UpperCAmelCase							(   cls       :  List[str]      ,   __lowerCamelCase       :  Any							)      ->  Any:
							cls.build_naming_info()
							assert cls.PREFIX is not None
							a					=    [copy.copy(cls.PREFIX							)]

							for k, v in params.items():
										if k not in cls.DEFAULTS:
													raise Exception(f"""You should provide a default value for the param name {k} with value {v}"""							)
										if v == cls.DEFAULTS[k]:
													# The default value is not added to the name
													continue

										a					=    cls.NAMING_INFO["short_param"][k]

										if isinstance(__lowerCamelCase      ,   __lowerCamelCase							):
													a					=    1 if v else 0

										a					=    "" if isinstance(__lowerCamelCase      ,   (int, float)							) else "-"
										a					=    f"""{key}{sep}{v}"""
										name.append(__lowerCamelCase							)

							return "_".join(__lowerCamelCase							)





				@classmethod
				def 							__UpperCAmelCase							(   cls       :  Dict      ,   __lowerCamelCase       :  Optional[Any]							)      ->  Tuple:
							a					=    repr[len(cls.PREFIX							) + 1 :]
							if repr == "":
										a					=    []
							else:
										a					=    repr.split("_"							)

							a					=    {}

							for value in values:
										if "-" in value:
													a					,	a					=    value.split("-"							)
										else:
													a					=    re.sub("[0-9.]"      ,   ""      ,   __lowerCamelCase							)
													a					=    float(re.sub("[^0-9.]"      ,   ""      ,   __lowerCamelCase							)							)

										a					=    cls.NAMING_INFO["reverse_short_param"][p_k]

										a					=    p_v

							for k in cls.DEFAULTS:
										if k not in parameters:
													a					=    cls.DEFAULTS[k]

							return parameters












from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__lowerCAmelCase	:       Optional[Any]     =	'\\n@misc{wu2016googles,\n      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n'

__lowerCAmelCase	:       str     =	'\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'

__lowerCAmelCase	:       List[Any]     =	'\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    \'google_bleu\': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results["google_bleu"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results["google_bleu"], 2))\n        0.4\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					, _KWARGS_DESCRIPTION     )
class 					snake_case__							(datasets.Metric     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  int							)      ->  MetricInfo:
							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"							),
							        }							)      ,   )





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  List[List[List[str]]]      ,   __lowerCamelCase       :  List[List[str]]      ,   __lowerCamelCase       :  int = 1      ,   __lowerCamelCase       :  int = 4      ,   )      ->  Dict[str, float]:
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=__lowerCamelCase      ,   hypotheses=__lowerCamelCase      ,   min_len=__lowerCamelCase      ,   max_len=__lowerCamelCase							)
							}






