




class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_    ):
											lowerCAmelCase =		val
											# Number of nodes in left subtree
											lowerCAmelCase =		0
											lowerCAmelCase =		None
											lowerCAmelCase =		None
											lowerCAmelCase =		None






				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

											if self.val < other.val:
																		lowerCAmelCase =		self.right
																		lowerCAmelCase =		None
																		if self.right:
																									lowerCAmelCase =		other
																		lowerCAmelCase =		other
																		lowerCAmelCase =		self.left_tree_size * 2 + 1
																		return self
											else:
																		lowerCAmelCase =		other.right
																		lowerCAmelCase =		None
																		if other.right:
																									lowerCAmelCase =		self
																		lowerCAmelCase =		self
																		lowerCAmelCase =		other.left_tree_size * 2 + 1
																		return other




class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_=None							,			UpperCAmelCase_=None							,			UpperCAmelCase_=0    ):
											lowerCAmelCase =		heap_size
											lowerCAmelCase =		bottom_root
											lowerCAmelCase =		min_node



				def    __snake_case		(			self							,			UpperCAmelCase_    ):

											# Empty heaps corner cases
											if other.size == 0:
																		return None
											if self.size == 0:
																		lowerCAmelCase =		other.size
																		lowerCAmelCase =		other.bottom_root
																		lowerCAmelCase =		other.min_node
																		return None
											# Update size
											lowerCAmelCase =		self.size + other.size

											# Update min.node
											if self.min_node.val > other.min_node.val:
																		lowerCAmelCase =		other.min_node
											# Merge

											# Order roots by left_subtree_size
											lowerCAmelCase =		[]
											lowerCAmelCase ,  lowerCAmelCase =		self.bottom_root, other.bottom_root
											while i or j:
																		if i and ((not j) or i.left_tree_size < j.left_tree_size):
																									combined_roots_list.append((i, True)    )
																									lowerCAmelCase =		i.parent
																		else:
																									combined_roots_list.append((j, False)    )
																									lowerCAmelCase =		j.parent
        # Insert links between them
											for i in range(len(UpperCAmelCase_    ) - 1    ):
																		if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
																									lowerCAmelCase =		combined_roots_list[i + 1][0]
																									lowerCAmelCase =		combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
											lowerCAmelCase =		combined_roots_list[0][0]
											while i.parent:
																		if (
																		    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
																		) or (
																		    i.left_tree_size == i.parent.left_tree_size
																		    and i.left_tree_size != i.parent.parent.left_tree_size
																		):
																									# Neighbouring Nodes
																									lowerCAmelCase =		i.left
																									lowerCAmelCase =		i.parent.parent

																									# Merging trees
																									lowerCAmelCase =		i.merge_trees(i.parent    )

																									# Updating links
																									lowerCAmelCase =		previous_node
																									lowerCAmelCase =		next_node
																									if previous_node:
																																lowerCAmelCase =		i
																									if next_node:
																																lowerCAmelCase =		i
																		else:
																									lowerCAmelCase =		i.parent
        # Updating self.bottom_root
											while i.left:
																		lowerCAmelCase =		i.left
											lowerCAmelCase =		i

											# Update other
											lowerCAmelCase =		self.size
											lowerCAmelCase =		self.bottom_root
											lowerCAmelCase =		self.min_node

											# Return the merged heap
											return self



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											if self.size == 0:
																		lowerCAmelCase =		Node(UpperCAmelCase_    )
																		lowerCAmelCase =		1
																		lowerCAmelCase =		self.bottom_root
											else:
																		# Create new node
																		lowerCAmelCase =		Node(UpperCAmelCase_    )

																		# Update size
																		self.size += 1

																		# update min_node
																		if val < self.min_node.val:
																									lowerCAmelCase =		new_node
																		# Put new_node as a bottom_root in heap
																		lowerCAmelCase =		new_node
																		lowerCAmelCase =		self.bottom_root
																		lowerCAmelCase =		new_node

																		# Consecutively merge roots with same left_tree_size
																		while (
																		    self.bottom_root.parent
																		    and self.bottom_root.left_tree_size
																		    == self.bottom_root.parent.left_tree_size
																		):
																									# Next node
																									lowerCAmelCase =		self.bottom_root.parent.parent

																									# Merge
																									lowerCAmelCase =		self.bottom_root.merge_trees(self.bottom_root.parent    )

																									# Update Links
																									lowerCAmelCase =		next_node
																									lowerCAmelCase =		None
																									if next_node:
																																lowerCAmelCase =		self.bottom_root



				def    __snake_case		(			self    ):
											return self.min_node.val



				def    __snake_case		(			self    ):
											return self.size == 0



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.min_node.val

											# Last element in heap corner case
											if self.size == 1:
																		# Update size
																		lowerCAmelCase =		0

																		# Update bottom root
																		lowerCAmelCase =		None

																		# Update min_node
																		lowerCAmelCase =		None

																		return min_value
											# No right subtree corner case
											# The structure of the tree implies that this should be the bottom root
											# and there is at least one other root
											if self.min_node.right is None:
																		# Update size
																		self.size -= 1

																		# Update bottom root
																		lowerCAmelCase =		self.bottom_root.parent
																		lowerCAmelCase =		None

																		# Update min_node
																		lowerCAmelCase =		self.bottom_root
																		lowerCAmelCase =		self.bottom_root.parent
																		while i:
																									if i.val < self.min_node.val:
																																lowerCAmelCase =		i
																									lowerCAmelCase =		i.parent
																		return min_value
											# General case
											# Find the BinomialHeap of the right subtree of min_node
											lowerCAmelCase =		self.min_node.right
											lowerCAmelCase =		None
											lowerCAmelCase =		bottom_of_new
											lowerCAmelCase =		1

											# Size, min_node and bottom_root
											while bottom_of_new.left:
																		lowerCAmelCase =		size_of_new * 2 + 1
																		lowerCAmelCase =		bottom_of_new.left
																		if bottom_of_new.val < min_of_new.val:
																									lowerCAmelCase =		bottom_of_new
        # Corner case of single root on top left path
											if (not self.min_node.left) and (not self.min_node.parent):
																		lowerCAmelCase =		size_of_new
																		lowerCAmelCase =		bottom_of_new
																		lowerCAmelCase =		min_of_new
																		# print("Single root, multiple nodes case")
																		return min_value
											# Remaining cases
											# Construct heap of right subtree
											lowerCAmelCase =		BinomialHeap(
											    bottom_root=UpperCAmelCase_							,			min_node=UpperCAmelCase_							,			heap_size=UpperCAmelCase_    )

											# Update size
											lowerCAmelCase =		self.size - 1 - size_of_new

											# Neighbour nodes
											lowerCAmelCase =		self.min_node.left
											lowerCAmelCase =		self.min_node.parent

											# Initialize new bottom_root and min_node
											lowerCAmelCase =		previous_node or next_node
											lowerCAmelCase =		next_node

											# Update links of previous_node and search below for new min_node and
											# bottom_root
											if previous_node:
																		lowerCAmelCase =		next_node

																		# Update bottom_root and search for min_node below
																		lowerCAmelCase =		previous_node
																		lowerCAmelCase =		previous_node
																		while self.bottom_root.left:
																									lowerCAmelCase =		self.bottom_root.left
																									if self.bottom_root.val < self.min_node.val:
																																lowerCAmelCase =		self.bottom_root
											if next_node:
																		lowerCAmelCase =		previous_node

																		# Search for new min_node above min_node
																		lowerCAmelCase =		next_node
																		while i:
																									if i.val < self.min_node.val:
																																lowerCAmelCase =		i
																									lowerCAmelCase =		i.parent
        # Merge heaps
											self.merge_heaps(UpperCAmelCase_    )

											return min_value



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.bottom_root
											while top_root.parent:
																		lowerCAmelCase =		top_root.parent
											# preorder
											lowerCAmelCase =		[]
											self.__traversal(UpperCAmelCase_							,			UpperCAmelCase_    )
											return heap_pre_order



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_=0    ):
											if curr_node:
																		preorder.append((curr_node.val, level)    )
																		self.__traversal(curr_node.left							,			UpperCAmelCase_							,			level + 1    )
																		self.__traversal(curr_node.right							,			UpperCAmelCase_							,			level + 1    )
											else:
																		preorder.append(('''#''', level)    )






				def __str__(			self    ):
											if self.is_empty():
																		return ""
											lowerCAmelCase =		self.pre_order()

											return "\n".join(('''-''' * level + str(UpperCAmelCase_    )) for value, level in preorder_heap    )


# Unit Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()









import sacrebleu as scb
from packaging import version

import datasets


UpperCAmelCase_    ="""\
@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_    ="""\
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.
"""

UpperCAmelCase_    ="""
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    __snake_case		(			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    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_="exp"							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			UpperCAmelCase_=False							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			):
											lowerCAmelCase =		len(references[0]    )
											if any(len(UpperCAmelCase_    ) != references_per_prediction for refs in references    ):
																		raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
											lowerCAmelCase =		[[refs[i] for refs in references] for i in range(UpperCAmelCase_    )]
											lowerCAmelCase =		scb.corpus_bleu(
											    UpperCAmelCase_							,			UpperCAmelCase_							,			smooth_method=UpperCAmelCase_							,			smooth_value=UpperCAmelCase_							,			force=UpperCAmelCase_							,			lowercase=UpperCAmelCase_							,			use_effective_order=UpperCAmelCase_							,			**({'''tokenize''': tokenize} if tokenize else {})							,			)
											lowerCAmelCase =		{
											    '''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



