



class 				__A					:





			def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      List[Any]								=		None
						lowerCamelCase__ :      List[Any]								=		None
						lowerCamelCase__ :      int								=		graph

						self._normalize_graph(__magic_name__							,    __magic_name__				)
						lowerCamelCase__ :      str								=		len(__magic_name__				)
						lowerCamelCase__ :      Dict								=		None





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						if sources is int:
									lowerCamelCase__ :      Any								=		[sources]
						if sinks is int:
									lowerCamelCase__ :      List[str]								=		[sinks]

						if len(__magic_name__				) == 0 or len(__magic_name__				) == 0:
									return

						lowerCamelCase__ :      List[Any]								=		sources[0]
						lowerCamelCase__ :      List[Any]								=		sinks[0]

						# make fake vertex if there are more
						# than one source or sink
						if len(__magic_name__				) > 1 or len(__magic_name__				) > 1:
									lowerCamelCase__ :      Optional[Any]								=		0
									for i in sources:
												max_input_flow += sum(self.graph[i]				)

									lowerCamelCase__ :      Union[str, Any]								=		len(self.graph				) + 1
									for room in self.graph:
												room.insert(0							,    0				)
									self.graph.insert(0							,    [0] * size				)
									for i in sources:
												lowerCamelCase__ :      List[Any]								=		max_input_flow
									lowerCamelCase__ :      List[Any]								=		0

									lowerCamelCase__ :      Any								=		len(self.graph				) + 1
									for room in self.graph:
												room.append(0				)
									self.graph.append([0] * size				)
									for i in sinks:
												lowerCamelCase__ :      List[Any]								=		max_input_flow
									lowerCamelCase__ :      str								=		size - 1





			def        _snake_case    (self				):
						if self.maximum_flow_algorithm is None:
									raise Exception("""You need to set maximum flow algorithm before."""				)
						if self.source_index is None or self.sink_index is None:
									return 0

						self.maximum_flow_algorithm.execute()
						return self.maximum_flow_algorithm.getMaximumFlow()





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      List[str]								=		algorithm(self				)







class 				__A					:





			def __init__(self							,    __magic_name__				):
						lowerCamelCase__ :      Optional[Any]								=		flow_network
						lowerCamelCase__ :      List[Any]								=		flow_network.verticesCount
						lowerCamelCase__ :      str								=		flow_network.sourceIndex
						lowerCamelCase__ :      Union[str, Any]								=		flow_network.sinkIndex
						# it's just a reference, so you shouldn't change
						# it in your algorithms, use deep copy before doing that
						lowerCamelCase__ :      Optional[int]								=		flow_network.graph
						lowerCamelCase__ :      List[str]								=		False





			def        _snake_case    (self				):
						if not self.executed:
									self._algorithm()
									lowerCamelCase__ :      Dict								=		True





			def        _snake_case    (self				):
						pass







class 				__A					( A_      ):





			def __init__(self							,    __magic_name__				):
						super().__init__(__magic_name__				)
						# use this to save your result
						lowerCamelCase__ :      str								=		-1





			def        _snake_case    (self				):
						if not self.executed:
									raise Exception("""You should execute algorithm before using its result!"""				)

						return self.maximum_flow







class 				__A					( A_      ):





			def __init__(self							,    __magic_name__				):
						super().__init__(__magic_name__				)

						lowerCamelCase__ :      Any								=		[[0] * self.verticies_count for i in range(self.verticies_count				)]

						lowerCamelCase__ :      str								=		[0] * self.verticies_count
						lowerCamelCase__ :      int								=		[0] * self.verticies_count





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		self.verticies_count

						# push some substance to graph
						for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]				):
									self.preflow[self.source_index][nextvertex_index] += bandwidth
									self.preflow[nextvertex_index][self.source_index] -= bandwidth
									self.excesses[nextvertex_index] += bandwidth

						# Relabel-to-front selection rule
						lowerCamelCase__ :      Any								=		[
						    i
						    for i in range(self.verticies_count				)
						    if i != self.source_index and i != self.sink_index
						]

						# move through list
						lowerCamelCase__ :      int								=		0
						while i < len(__magic_name__				):
									lowerCamelCase__ :      List[Any]								=		vertices_list[i]
									lowerCamelCase__ :      Any								=		self.heights[vertex_index]
									self.process_vertex(__magic_name__				)
									if self.heights[vertex_index] > previous_height:
												# if it was relabeled, swap elements
												# and start from 0 index
												vertices_list.insert(0							,    vertices_list.pop(__magic_name__				)				)
												lowerCamelCase__ :      Optional[Any]								=		0
									else:
												i += 1

						lowerCamelCase__ :      Tuple								=		sum(self.preflow[self.source_index]				)





			def        _snake_case    (self							,    __magic_name__				):
						while self.excesses[vertex_index] > 0:
									for neighbour_index in range(self.verticies_count				):
												# if it's neighbour and current vertex is higher
												if (
												    self.graph[vertex_index][neighbour_index]
												    - self.preflow[vertex_index][neighbour_index]
												    > 0
												    and self.heights[vertex_index] > self.heights[neighbour_index]
												):
															self.push(__magic_name__							,    __magic_name__				)

									self.relabel(__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Union[str, Any]								=		min(
						    self.excesses[from_index]							,    self.graph[from_index][to_index] - self.preflow[from_index][to_index]							,    )
						self.preflow[from_index][to_index] += preflow_delta
						self.preflow[to_index][from_index] -= preflow_delta
						self.excesses[from_index] -= preflow_delta
						self.excesses[to_index] += preflow_delta





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      Any								=		None
						for to_index in range(self.verticies_count				):
									if (
									    self.graph[vertex_index][to_index]
									    - self.preflow[vertex_index][to_index]
									    > 0
									) and (min_height is None or self.heights[to_index] < min_height):
												lowerCamelCase__ :      Dict								=		self.heights[to_index]

						if min_height is not None:
									lowerCamelCase__ :      str								=		min_height + 1


if __name__ == "__main__":
				_lowercase     							=						[0]
				_lowercase     							=						[3]
				# graph = [
				#     [0, 0, 4, 6, 0, 0],
				#     [0, 0, 5, 2, 0, 0],
				#     [0, 0, 0, 0, 4, 4],
				#     [0, 0, 0, 0, 6, 6],
				#     [0, 0, 0, 0, 0, 0],
				#     [0, 0, 0, 0, 0, 0],
				# ]
				_lowercase     							=						[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

				# prepare our network
				_lowercase     							=						FlowNetwork(graph, entrances, exits)
				# set algorithm
				flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
				# and calculate
				_lowercase     							=						flow_network.find_maximum_flow()

				print(F'''maximum flow is {maximum_flow}''')








import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_lowercase     							=						version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
				from nltk import word_tokenize


_lowercase     							=						'''\
@inproceedings{banarjee2005,
  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
  author    = {Banerjee, Satanjeev  and Lavie, Alon},
  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
  month     = jun,
  year      = {2005},
  address   = {Ann Arbor, Michigan},
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/W05-0909},
  pages     = {65--72},
}
'''

_lowercase     							=						'''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''

_lowercase     							=						'''
Computes METEOR score of translated segments against one or more references.
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    \'meteor\': meteor score.
Examples:

    >>> meteor = datasets.load_metric(\'meteor\')
    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results["meteor"], 4))
    0.6944
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					, _KWARGS_DESCRIPTION      )
class 				__A					( datasets.Metric      ):





			def        _snake_case    (self				):
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
						        {
						            """predictions""": datasets.Value("""string"""							,    id="""sequence"""				),
						            """references""": datasets.Value("""string"""							,    id="""sequence"""				),
						        }				)							,    codebase_urls=["""https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"""]							,    reference_urls=[
						        """https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score""",
						        """https://en.wikipedia.org/wiki/METEOR""",
						    ]							,    )





			def        _snake_case    (self							,    __magic_name__				):
						import nltk

						nltk.download("""wordnet"""				)
						if NLTK_VERSION >= version.Version("""3.6.5"""				):
									nltk.download("""punkt"""				)
						if NLTK_VERSION >= version.Version("""3.6.6"""				):
									nltk.download("""omw-1.4"""				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__=0.9							,    __magic_name__=3							,    __magic_name__=0.5				):
						if NLTK_VERSION >= version.Version("""3.6.5"""				):
									lowerCamelCase__ :      List[str]								=		[
									    meteor_score.single_meteor_score(
									        word_tokenize(__magic_name__				)							,    word_tokenize(__magic_name__				)							,    alpha=__magic_name__							,    beta=__magic_name__							,    gamma=__magic_name__				)
									    for ref, pred in zip(__magic_name__							,    __magic_name__				)
									]
						else:
									lowerCamelCase__ :      int								=		[
									    meteor_score.single_meteor_score(__magic_name__							,    __magic_name__							,    alpha=__magic_name__							,    beta=__magic_name__							,    gamma=__magic_name__				)
									    for ref, pred in zip(__magic_name__							,    __magic_name__				)
									]

						return {"meteor": np.mean(__magic_name__				)}



