

class     a__  :
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase					,					lowercase				)     ->  str:
							'''simple docstring'''






							A__						=	None
							A__						=	None
							A__						=	graph

							self._normalize_graph(lowercase					,					lowercase				)
							A__						=	len(lowercase				)
							A__						=	None


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase				)     ->  Optional[int]:
							'''simple docstring'''






							if sources is int:
											A__						=	[sources]
							if sinks is int:
											A__						=	[sinks]

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

							A__						=	sources[0]
							A__						=	sinks[0]

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

											A__						=	len(self.graph				) + 1
											for room in self.graph:
															room.insert(0					,					0				)
											self.graph.insert(0					,					[0] * size				)
											for i in sources:
															A__						=	max_input_flow
											A__						=	0

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


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							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    UpperCamelCase						(			self					,					lowercase				)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	algorithm(self				)

class     a__  :
			"""simple docstring"""







			def __init__(			self					,					lowercase				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	flow_network
							A__						=	flow_network.verticesCount
							A__						=	flow_network.sourceIndex
							A__						=	flow_network.sinkIndex
							# it's just a reference, so you shouldn't change
							# it in your algorithms, use deep copy before doing that
							A__						=	flow_network.graph
							A__						=	False


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							if not self.executed:
											self._algorithm()
											A__						=	True


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							pass

class     a__  (    snake_case	):
			"""simple docstring"""







			def __init__(			self					,					lowercase				)     ->  List[Any]:
							'''simple docstring'''






							super().__init__(lowercase				)
							# use this to save your result
							A__						=	-1


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






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

							return self.maximum_flow

class     a__  (    snake_case	):
			"""simple docstring"""







			def __init__(			self					,					lowercase				)     ->  Dict:
							'''simple docstring'''






							super().__init__(lowercase				)

							A__						=	[[0] * self.verticies_count for i in range(self.verticies_count				)]

							A__						=	[0] * self.verticies_count
							A__						=	[0] * self.verticies_count


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	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
							A__						=	[
							    i
							    for i in range(self.verticies_count				)
							    if i != self.source_index and i != self.sink_index
							]

							# move through list
							A__						=	0
							while i < len(lowercase				):
											A__						=	vertices_list[i]
											A__						=	self.heights[vertex_index]
											self.process_vertex(lowercase				)
											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(lowercase				)				)
															A__						=	0
											else:
															i += 1

							A__						=	sum(self.preflow[self.source_index]				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  str:
							'''simple docstring'''






							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(lowercase					,					lowercase				)

											self.relabel(lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase				)     ->  Dict:
							'''simple docstring'''






							A__						=	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    UpperCamelCase						(			self					,					lowercase				)     ->  Dict:
							'''simple docstring'''






							A__						=	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):
															A__						=	self.heights[to_index]

							if min_height is not None:
											A__						=	min_height + 1


if __name__ == "__main__":
	lowerCAmelCase__		      =       [0]
	lowerCAmelCase__		      =       [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],
	# ]
	lowerCAmelCase__		      =       [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

	print(f"""maximum flow is {maximum_flow}""")






import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.albert.modeling_flax_albert import (
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForPreTraining,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertModel,
     )






class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__=13,		lowerCamelCase__=7,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=99,		lowerCamelCase__=32,		lowerCamelCase__=5,		lowerCamelCase__=4,		lowerCamelCase__=37,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=16,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=4,		):
        A :       List[str]          =   parent
        A :       Optional[int]          =   batch_size
        A :       Union[str, Any]          =   seq_length
        A :       Any          =   is_training
        A :       List[str]          =   use_attention_mask
        A :       Union[str, Any]          =   use_token_type_ids
        A :       Any          =   use_labels
        A :       str          =   vocab_size
        A :       Union[str, Any]          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       Optional[int]          =   intermediate_size
        A :       Optional[Any]          =   hidden_act
        A :       Dict          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       int          =   type_vocab_size
        A :       str          =   type_sequence_label_size
        A :       List[Any]          =   initializer_range
        A :       str          =   num_choices






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		)

        A :       Union[str, Any]          =   None
        if self.use_attention_mask:
            A :       Optional[int]          =   random_attention_mask([self.batch_size, self.seq_length]		)

        A :       int          =   None
        if self.use_token_type_ids:
            A :       List[Any]          =   ids_tensor([self.batch_size, self.seq_length],		self.type_vocab_size		)

        A :       Optional[int]          =   AlbertConfig(
            vocab_size=self.vocab_size,		hidden_size=self.hidden_size,		num_hidden_layers=self.num_hidden_layers,		num_attention_heads=self.num_attention_heads,		intermediate_size=self.intermediate_size,		hidden_act=self.hidden_act,		hidden_dropout_prob=self.hidden_dropout_prob,		attention_probs_dropout_prob=self.attention_probs_dropout_prob,		max_position_embeddings=self.max_position_embeddings,		type_vocab_size=self.type_vocab_size,		is_decoder=lowerCamelCase__,		initializer_range=self.initializer_range,		)

        return config, input_ids, token_type_ids, attention_mask






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.prepare_config_and_inputs()
        A     ,							A     ,							A     ,							A :       str          =   config_and_inputs
        A :       Union[str, Any]          =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
        return config, inputs_dict






@require_flax
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      (
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   FlaxAlbertModelTester(self		)






    @slow
    def 			_lowerCAmelCase					(       self		):
        for model_class_name in self.all_model_classes:
            A :       Dict          =   model_class_name.from_pretrained("""albert-base-v2"""		)
            A :       Optional[int]          =   model(np.ones((1, 1)		)		)
            self.assertIsNotNone(lowerCamelCase__		)






@require_flax
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   FlaxAlbertModel.from_pretrained("""albert-base-v2"""		)
        A :       List[str]          =   np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
        A :       str          =   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
        A :       Optional[int]          =   model(lowerCamelCase__,		attention_mask=lowerCamelCase__		)[0]
        A :       str          =   (1, 11, 768)
        self.assertEqual(output.shape,		lowerCamelCase__		)
        A :       Optional[int]          =   np.array(
            [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]		)

        self.assertTrue(jnp.allclose(output[:, 1:4, 1:4],		lowerCamelCase__,		atol=1e-4		)		)


