





import sacrebleu as scb
from packaging import version

import datasets


_lowercase		:     Optional[Any] 					="""\
@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\",
}
"""

_lowercase		:     List[str] 					="""\
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.
"""

_lowercase		:     Any 					="""
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       ):



					def      __a    (		self    :    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      __a    (		self    :    str					,			lowerCamelCase    :    List[Any]					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    Any="exp"					,			lowerCamelCase    :    str=None					,			lowerCamelCase    :    List[str]=False					,			lowerCamelCase    :    str=False					,			lowerCamelCase    :    Optional[int]=None					,			lowerCamelCase    :    Tuple=False					,			):
							lowerCamelCase_  :    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'	)
							lowerCamelCase_  :    Optional[Any]   	=			[[refs[i] for refs in references] for i in range(lowerCamelCase	)]
							lowerCamelCase_  :    Tuple   	=			scb.corpus_bleu(
							    lowerCamelCase					,			lowerCamelCase					,			smooth_method=lowerCamelCase					,			smooth_value=lowerCamelCase					,			force=lowerCamelCase					,			lowercase=lowerCamelCase					,			use_effective_order=lowerCamelCase					,			**({'tokenize': tokenize} if tokenize else {})					,			)
							lowerCamelCase_  :    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







class 	UpperCamelCase_     :



					def __init__(		self    :    List[Any]					,			lowerCamelCase    :    int	):
							lowerCamelCase_  :    Union[str, Any]   	=			val
							# Number of nodes in left subtree
							lowerCamelCase_  :    Dict   	=			0
							lowerCamelCase_  :    str   	=			None
							lowerCamelCase_  :    int   	=			None
							lowerCamelCase_  :    Dict   	=			None





					def      __a    (		self    :    Optional[int]					,			lowerCamelCase    :    List[Any]	):
							assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

							if self.val < other.val:
									lowerCamelCase_  :    int   	=			self.right
									lowerCamelCase_  :    Any   	=			None
									if self.right:
											lowerCamelCase_  :    Optional[Any]   	=			other
									lowerCamelCase_  :    Union[str, Any]   	=			other
									lowerCamelCase_  :    List[Any]   	=			self.left_tree_size * 2 + 1
									return self
							else:
									lowerCamelCase_  :    str   	=			other.right
									lowerCamelCase_  :    List[Any]   	=			None
									if other.right:
											lowerCamelCase_  :    Union[str, Any]   	=			self
									lowerCamelCase_  :    Optional[int]   	=			self
									lowerCamelCase_  :    Optional[int]   	=			other.left_tree_size * 2 + 1
									return other






class 	UpperCamelCase_     :



					def __init__(		self    :    List[str]					,			lowerCamelCase    :    Optional[int]=None					,			lowerCamelCase    :    str=None					,			lowerCamelCase    :    List[Any]=0	):
							lowerCamelCase_  :    Any   	=			heap_size
							lowerCamelCase_  :    int   	=			bottom_root
							lowerCamelCase_  :    Any   	=			min_node



					def      __a    (		self    :    int					,			lowerCamelCase    :    int	):

							# Empty heaps corner cases
							if other.size == 0:
									return None
							if self.size == 0:
									lowerCamelCase_  :    List[str]   	=			other.size
									lowerCamelCase_  :    Tuple   	=			other.bottom_root
									lowerCamelCase_  :    Tuple   	=			other.min_node
									return None
							# Update size
							lowerCamelCase_  :    Optional[Any]   	=			self.size + other.size

							# Update min.node
							if self.min_node.val > other.min_node.val:
									lowerCamelCase_  :    Optional[Any]   	=			other.min_node
							# Merge

							# Order roots by left_subtree_size
							lowerCamelCase_  :    Dict   	=			[]
							lowerCamelCase_	,			lowerCamelCase_  :    Optional[Any]   	=			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_  :    Tuple   	=			i.parent
									else:
											combined_roots_list.append((j, False)	)
											lowerCamelCase_  :    Optional[Any]   	=			j.parent
        # Insert links between them
							for i in range(len(lowerCamelCase	) - 1	):
									if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
											lowerCamelCase_  :    Dict   	=			combined_roots_list[i + 1][0]
											lowerCamelCase_  :    Optional[int]   	=			combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
							lowerCamelCase_  :    Any   	=			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_  :    Dict   	=			i.left
											lowerCamelCase_  :    int   	=			i.parent.parent

											# Merging trees
											lowerCamelCase_  :    str   	=			i.merge_trees(i.parent	)

											# Updating links
											lowerCamelCase_  :    Union[str, Any]   	=			previous_node
											lowerCamelCase_  :    List[Any]   	=			next_node
											if previous_node:
													lowerCamelCase_  :    List[str]   	=			i
											if next_node:
													lowerCamelCase_  :    Dict   	=			i
									else:
											lowerCamelCase_  :    List[str]   	=			i.parent
        # Updating self.bottom_root
							while i.left:
									lowerCamelCase_  :    Union[str, Any]   	=			i.left
							lowerCamelCase_  :    Any   	=			i

							# Update other
							lowerCamelCase_  :    Optional[int]   	=			self.size
							lowerCamelCase_  :    Optional[Any]   	=			self.bottom_root
							lowerCamelCase_  :    str   	=			self.min_node

							# Return the merged heap
							return self



					def      __a    (		self    :    Any					,			lowerCamelCase    :    Dict	):
							if self.size == 0:
									lowerCamelCase_  :    Tuple   	=			Node(lowerCamelCase	)
									lowerCamelCase_  :    Optional[int]   	=			1
									lowerCamelCase_  :    Optional[int]   	=			self.bottom_root
							else:
									# Create new node
									lowerCamelCase_  :    List[Any]   	=			Node(lowerCamelCase	)

									# Update size
									self.size += 1

									# update min_node
									if val < self.min_node.val:
											lowerCamelCase_  :    List[str]   	=			new_node
									# Put new_node as a bottom_root in heap
									lowerCamelCase_  :    int   	=			new_node
									lowerCamelCase_  :    Tuple   	=			self.bottom_root
									lowerCamelCase_  :    Any   	=			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_  :    Any   	=			self.bottom_root.parent.parent

											# Merge
											lowerCamelCase_  :    int   	=			self.bottom_root.merge_trees(self.bottom_root.parent	)

											# Update Links
											lowerCamelCase_  :    Dict   	=			next_node
											lowerCamelCase_  :    Optional[Any]   	=			None
											if next_node:
													lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root



					def      __a    (		self    :    Dict	):
							return self.min_node.val



					def      __a    (		self    :    int	):
							return self.size == 0



					def      __a    (		self    :    List[Any]	):
							lowerCamelCase_  :    Tuple   	=			self.min_node.val

							# Last element in heap corner case
							if self.size == 1:
									# Update size
									lowerCamelCase_  :    Optional[Any]   	=			0

									# Update bottom root
									lowerCamelCase_  :    Any   	=			None

									# Update min_node
									lowerCamelCase_  :    List[str]   	=			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_  :    Union[str, Any]   	=			self.bottom_root.parent
									lowerCamelCase_  :    Union[str, Any]   	=			None

									# Update min_node
									lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root
									lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root.parent
									while i:
											if i.val < self.min_node.val:
													lowerCamelCase_  :    Union[str, Any]   	=			i
											lowerCamelCase_  :    Optional[int]   	=			i.parent
									return min_value
							# General case
							# Find the BinomialHeap of the right subtree of min_node
							lowerCamelCase_  :    Dict   	=			self.min_node.right
							lowerCamelCase_  :    Dict   	=			None
							lowerCamelCase_  :    Tuple   	=			bottom_of_new
							lowerCamelCase_  :    Dict   	=			1

							# Size, min_node and bottom_root
							while bottom_of_new.left:
									lowerCamelCase_  :    Any   	=			size_of_new * 2 + 1
									lowerCamelCase_  :    Tuple   	=			bottom_of_new.left
									if bottom_of_new.val < min_of_new.val:
											lowerCamelCase_  :    int   	=			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_  :    Union[str, Any]   	=			size_of_new
									lowerCamelCase_  :    Union[str, Any]   	=			bottom_of_new
									lowerCamelCase_  :    Tuple   	=			min_of_new
									# print("Single root, multiple nodes case")
									return min_value
							# Remaining cases
							# Construct heap of right subtree
							lowerCamelCase_  :    Optional[Any]   	=			BinomialHeap(
							    bottom_root=lowerCamelCase					,			min_node=lowerCamelCase					,			heap_size=lowerCamelCase	)

							# Update size
							lowerCamelCase_  :    Union[str, Any]   	=			self.size - 1 - size_of_new

							# Neighbour nodes
							lowerCamelCase_  :    Union[str, Any]   	=			self.min_node.left
							lowerCamelCase_  :    Optional[Any]   	=			self.min_node.parent

							# Initialize new bottom_root and min_node
							lowerCamelCase_  :    Optional[int]   	=			previous_node or next_node
							lowerCamelCase_  :    Tuple   	=			next_node

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

									# Update bottom_root and search for min_node below
									lowerCamelCase_  :    int   	=			previous_node
									lowerCamelCase_  :    Tuple   	=			previous_node
									while self.bottom_root.left:
											lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root.left
											if self.bottom_root.val < self.min_node.val:
													lowerCamelCase_  :    Any   	=			self.bottom_root
							if next_node:
									lowerCamelCase_  :    Any   	=			previous_node

									# Search for new min_node above min_node
									lowerCamelCase_  :    List[str]   	=			next_node
									while i:
											if i.val < self.min_node.val:
													lowerCamelCase_  :    List[Any]   	=			i
											lowerCamelCase_  :    Dict   	=			i.parent
        # Merge heaps
							self.merge_heaps(lowerCamelCase	)

							return min_value



					def      __a    (		self    :    Optional[int]	):
							lowerCamelCase_  :    List[Any]   	=			self.bottom_root
							while top_root.parent:
									lowerCamelCase_  :    Tuple   	=			top_root.parent
							# preorder
							lowerCamelCase_  :    str   	=			[]
							self.__traversal(lowerCamelCase					,			lowerCamelCase	)
							return heap_pre_order



					def      __a    (		self    :    Dict					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    List[str]=0	):
							if curr_node:
									preorder.append((curr_node.val, level)	)
									self.__traversal(curr_node.left					,			lowerCamelCase					,			level + 1	)
									self.__traversal(curr_node.right					,			lowerCamelCase					,			level + 1	)
							else:
									preorder.append(('#', level)	)





					def __str__(		self    :    List[Any]	):
							if self.is_empty():
									return ""
							lowerCamelCase_  :    str   	=			self.pre_order()

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


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

		doctest.testmod()
