
"""simple docstring"""




import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


UpperCAmelCase__      :    str    							= '\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'

UpperCAmelCase__      :    str    							= '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

UpperCAmelCase__      :    Optional[int]    							= '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'



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


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											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"""    ),
											        }    )   ,       codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]   ,       reference_urls=[
											        """https://en.wikipedia.org/wiki/BLEU""",
											        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
											    ]   ,       )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__=False    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   compute_bleu(
											    reference_corpus=SCREAMING_SNAKE_CASE__   ,       translation_corpus=SCREAMING_SNAKE_CASE__   ,       max_order=SCREAMING_SNAKE_CASE__   ,       smooth=SCREAMING_SNAKE_CASE__    )
											((SCREAMING_SNAKE_CASE__)      ,      (SCREAMING_SNAKE_CASE__)      ,      (SCREAMING_SNAKE_CASE__)      ,      (SCREAMING_SNAKE_CASE__)      ,      (SCREAMING_SNAKE_CASE__)      ,      (SCREAMING_SNAKE_CASE__))						:       Union[str, Any]           =   score
											return {
											    "bleu": bleu,
											    "precisions": precisions,
											    "brevity_penalty": bp,
											    "length_ratio": ratio,
											    "translation_length": translation_length,
											    "reference_length": reference_length,
											}











"""simple docstring"""
class 					_UpperCAmelCase   :


					def __init__(					self			:List[str]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Tuple		):
												A		=  name
												A		=  val


					def __str__(					self			:str		):
												return f"{self.__class__.__name__}({self.name}, {self.val})"


					def __lt__(					self			:List[Any]     ,       __UpperCamelCase			:Union[str, Any]		):
												return self.val < other.val







class 					_UpperCAmelCase   :


					def __init__(					self			:List[str]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  {}
												A		=  {}
												A		=  self.build_heap(__UpperCamelCase		)


					def __getitem__(					self			:int     ,       __UpperCamelCase			:Optional[int]		):
												return self.get_value(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:str		):
												return (idx - 1) // 2


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:Optional[Any]		):
												return idx * 2 + 1


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:Optional[int]		):
												return idx * 2 + 2


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:str		):
												return self.heap_dict[key]


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  len(__UpperCamelCase		) - 1
												A		=  self.get_parent_idx(__UpperCamelCase		)

												for idx, i in enumerate(__UpperCamelCase		):
																			A		=  idx
																			A		=  i.val

												for i in range(__UpperCamelCase     ,       -1     ,       -1		):
																			self.sift_down(__UpperCamelCase     ,       __UpperCamelCase		)
												return array


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:Optional[Any]     ,       __UpperCamelCase			:Dict		):
												while True:
																			A		=  self.get_left_child_idx(__UpperCamelCase		)  # noqa: E741
																			A		=  self.get_right_child_idx(__UpperCamelCase		)

																			A		=  idx
																			if l < len(__UpperCamelCase		) and array[l] < array[idx]:
																										A		=  l
																			if r < len(__UpperCamelCase		) and array[r] < array[smallest]:
																										A		=  r

																			if smallest != idx:
																										A,	A		=  array[smallest], array[idx]
																										(
																										    (
																										    A
																										),	(
																										    A
																										),	
																										)		=  (
																										    self.idx_of_element[array[smallest]],
																										    self.idx_of_element[array[idx]],
																										)
																										A		=  smallest
																			else:
																										break


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:Optional[int]		):
												A		=  self.get_parent_idx(__UpperCamelCase		)
												while p >= 0 and self.heap[p] > self.heap[idx]:
																			A,	A		=  self.heap[idx], self.heap[p]
																			A,	A		=  (
																			    self.idx_of_element[self.heap[idx]],
																			    self.idx_of_element[self.heap[p]],
																			)
																			A		=  p
																			A		=  self.get_parent_idx(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												return self.heap[0]


					def 	lowerCamelCase       (					self			:Tuple		):
												A,	A		=  self.heap[-1], self.heap[0]
												A,	A		=  (
												    self.idx_of_element[self.heap[-1]],
												    self.idx_of_element[self.heap[0]],
												)

												A		=  self.heap.pop()
												del self.idx_of_element[x]
												self.sift_down(0     ,       self.heap		)
												return x


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Optional[int]		):
												self.heap.append(__UpperCamelCase		)
												A		=  len(self.heap		) - 1
												A		=  node.val
												self.sift_up(len(self.heap		) - 1		)


					def 	lowerCamelCase       (					self			:Tuple		):
												return len(self.heap		) == 0


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Dict		):
												assert (
												    self.heap[self.idx_of_element[node]].val > new_value
												), "newValue must be less that current value"
												A		=  new_value
												A		=  new_value
												self.sift_up(self.idx_of_element[node]		)







_snake_case     :		Optional[int]       						= Node('R', -1)
_snake_case     :		Tuple       						= Node('B', 6)
_snake_case     :		Tuple       						= Node('A', 3)
_snake_case     :		Optional[int]       						= Node('X', 1)
_snake_case     :		List[Any]       						= Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
_snake_case     :		Tuple       						= MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
		print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
		print(i)

if __name__ == "__main__":
		import doctest

		doctest.testmod()


