





import math
import sys
class SCREAMING_SNAKE_CASE_    :






						def __init__(   self   :   List[str]						)				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      Dict           =						0
							snake_case_		:      List[Any]           =						[]
							snake_case_		:      Optional[int]           =						{}  # To store the pos of node in array






						def UpperCAmelCase_					(   self   :   Dict						)				->     Optional[int]:







							"""simple docstring"""


							return self.cur_size == 0






						def UpperCAmelCase_					(   self   :   int		, _A   :   Tuple						)				->     Optional[int]:







							"""simple docstring"""


							snake_case_		:      str           =						self.left(_A						)
							snake_case_		:      Dict           =						self.right(_A						)
							if lc < self.cur_size and self.array(_A						)[0] < self.array(_A						)[0]:
								snake_case_		:      Tuple           =						lc
							else:
								snake_case_		:      int           =						idx
							if rc < self.cur_size and self.array(_A						)[0] < self.array(_A						)[0]:
								snake_case_		:      Optional[Any]           =						rc
							if smallest != idx:
								self.swap(_A		, _A						)
								self.min_heapify(_A						)






						def UpperCAmelCase_					(   self   :   Any		, _A   :   List[str]						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      Any           =						self.cur_size
							self.cur_size += 1
							self.array.append((sys.maxsize, tup[1])						)
							self.decrease_key((sys.maxsize, tup[1])		, tup[0]						)






						def UpperCAmelCase_					(   self   :   Optional[int]						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_		:      str           =						self.array[0][1]
							snake_case_		:      Union[str, Any]           =						self.array[self.cur_size - 1]
							self.cur_size -= 1
							self.min_heapify(1						)
							del self.pos[min_node]
							return min_node






						def UpperCAmelCase_					(   self   :   Dict		, _A   :   Any						)				->     int:







							"""simple docstring"""


							return 2 * i + 1






						def UpperCAmelCase_					(   self   :   List[str]		, _A   :   Dict						)				->     Optional[Any]:







							"""simple docstring"""


							return 2 * i + 2






						def UpperCAmelCase_					(   self   :   List[str]		, _A   :   Optional[Any]						)				->     Any:







							"""simple docstring"""


							return math.floor(i / 2						)






						def UpperCAmelCase_					(   self   :   Tuple		, _A   :   List[Any]		, _A   :   Dict						)				->     Optional[int]:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						j
							snake_case_		:      Optional[int]           =						i
							snake_case_		:      Optional[Any]           =						self.array[i]
							snake_case_		:      List[Any]           =						self.array[j]
							snake_case_		:      Tuple           =						temp






						def UpperCAmelCase_					(   self   :   str		, _A   :   Optional[int]		, _A   :   Dict						)				->     int:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						self.pos[tup[1]]
							# assuming the new_d is atmost old_d
							snake_case_		:      Optional[Any]           =						(new_d, tup[1])
							while idx > 0 and self.array[self.par(_A						)][0] > self.array[idx][0]:
								self.swap(_A		, self.par(_A						)						)
								snake_case_		:      List[Any]           =						self.par(_A						)
class SCREAMING_SNAKE_CASE_    :






						def __init__(   self   :   Dict		, _A   :   List[Any]						)				->     Any:







							"""simple docstring"""


							snake_case_		:      str           =						{}  # To store graph: u -> (v,w)
							snake_case_		:      List[str]           =						num  # Number of nodes in graph
							# To store the distance from source vertex
							snake_case_		:      Optional[int]           =						[0] * self.num_nodes
							snake_case_		:      Optional[Any]           =						[-1] * self.num_nodes  # To store the path






						def UpperCAmelCase_					(   self   :   Any		, _A   :   List[str]		, _A   :   Dict		, _A   :   Tuple						)				->     Optional[Any]:







							"""simple docstring"""


							if u in self.adjList:
								self.adjList[u].append((v, w)						)
							else:
								snake_case_		:      Optional[Any]           =						[(v, w)]

							# Assuming undirected graph
							if v in self.adjList:
								self.adjList[v].append((u, w)						)
							else:
								snake_case_		:      int           =						[(u, w)]






						def UpperCAmelCase_					(   self   :   Tuple						)				->     Tuple:







							"""simple docstring"""


							for u in self.adjList:
								print(_A		, '->'		, ' -> '.join(str(F"""{v}({w})"""						) for v, w in self.adjList[u]						)						)






						def UpperCAmelCase_					(   self   :   Tuple		, _A   :   List[str]						)				->     List[str]:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						[-1] * self.num_nodes
							# src is the source node
							snake_case_		:      List[Any]           =						0
							snake_case_		:      Optional[Any]           =						PriorityQueue()
							q.insert((0, src)						)  # (dist from src, node)
							for u in self.adjList:
								if u != src:
									snake_case_		:      Tuple           =						sys.maxsize  # Infinity
									snake_case_		:      Optional[int]           =						-1

							while not q.is_empty():
								snake_case_		:      int           =						q.extract_min()  # Returns node with the min dist from source
								# Update the distance of all the neighbours of u and
								# if their prev dist was INFINITY then push them in Q
								for v, w in self.adjList[u]:
									snake_case_		:      Dict           =						self.dist[u] + w
									if self.dist[v] > new_dist:
										if self.dist[v] == sys.maxsize:
											q.insert((new_dist, v)						)
										else:
											q.decrease_key((self.dist[v], v)		, _A						)
										snake_case_		:      int           =						new_dist
										snake_case_		:      Any           =						u

        # Show the shortest distances from src
							self.show_distances(_A						)






						def UpperCAmelCase_					(   self   :   List[Any]		, _A   :   int						)				->     Dict:







							"""simple docstring"""


							print(F"""Distance from node: {src}"""						)
							for u in range(self.num_nodes						):
								print(F"""Node {u} has distance: {self.dist[u]}"""						)






						def UpperCAmelCase_					(   self   :   Optional[int]		, _A   :   str		, _A   :   Dict						)				->     Optional[int]:







							"""simple docstring"""


							snake_case_		:      Dict           =						[]
							snake_case_		:      str           =						0
							snake_case_		:      List[Any]           =						dest
							# Backtracking from dest to src
							while self.par[temp] != -1:
								path.append(_A						)
								if temp != src:
									for v, w in self.adjList[temp]:
										if v == self.par[temp]:
											cost += w
											break
								snake_case_		:      List[Any]           =						self.par[temp]
							path.append(_A						)
							path.reverse()

							print(F"""----Path to reach {dest} from {src}----"""						)
							for u in path:
								print(F"""{u}"""		, end=' '						)
								if u != dest:
									print('-> '		, end=''						)

							print('\nTotal cost of path: '		, _A						)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE   						=		Graph(9)
						graph.add_edge(0, 1, 4)
						graph.add_edge(0, 7, 8)
						graph.add_edge(1, 2, 8)
						graph.add_edge(1, 7, 11)
						graph.add_edge(2, 3, 7)
						graph.add_edge(2, 8, 2)
						graph.add_edge(2, 5, 4)
						graph.add_edge(3, 4, 9)
						graph.add_edge(3, 5, 14)
						graph.add_edge(4, 5, 10)
						graph.add_edge(5, 6, 2)
						graph.add_edge(6, 7, 1)
						graph.add_edge(6, 8, 6)
						graph.add_edge(7, 8, 7)
						graph.show_graph()
						graph.dijkstra(0)
						graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21








import sacrebleu as scb
from packaging import version

import datasets


_SCREAMING_SNAKE_CASE   						=		"""\
@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\",
}
"""

_SCREAMING_SNAKE_CASE   						=		"""\
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.
"""

_SCREAMING_SNAKE_CASE   						=		"""
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 SCREAMING_SNAKE_CASE_    (       datasets.Metric       ):






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







							"""simple docstring"""


							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   :   List[Any]		, _A   :   List[str]		, _A   :   Optional[Any]		, _A   :   Tuple="exp"		, _A   :   Tuple=None		, _A   :   Optional[Any]=False		, _A   :   Dict=False		, _A   :   List[str]=None		, _A   :   Dict=False		, )				->     Optional[Any]:







							"""simple docstring"""


							snake_case_		:      List[str]           =						len(references[0]						)
							if any(len(_A						) != references_per_prediction for refs in references						):
								raise ValueError('Sacrebleu requires the same number of references for each prediction'						)
							snake_case_		:      Optional[Any]           =						[[refs[i] for refs in references] for i in range(_A						)]
							snake_case_		:      Any           =						scb.corpus_bleu(
							    _A		, _A		, smooth_method=_A		, smooth_value=_A		, force=_A		, lowercase=_A		, use_effective_order=_A		, **({'tokenize': tokenize} if tokenize else {})		, )
							snake_case_		:      int           =						{
							    '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

