




"""simple docstring"""




class     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					None
							lowerCamelCase			      =					None
							lowerCamelCase			      =					graph

							self._normalize_graph(_a		, _a )
							lowerCamelCase			      =					len(_a )
							lowerCamelCase			      =					None





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							if sources is int:
										lowerCamelCase			      =					[sources]
							if sinks is int:
										lowerCamelCase			      =					[sinks]

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

							lowerCamelCase			      =					sources[0]
							lowerCamelCase			      =					sinks[0]

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

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

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





				def    _lowerCAmelCase				(       self ):





							"""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    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					algorithm(self )






class     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, _a ):





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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							if not self.executed:
										self._algorithm()
										lowerCamelCase			      =					True





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							pass






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a ):





							"""simple docstring"""
							super().__init__(_a )
							# use this to save your result
							lowerCamelCase			      =					-1





				def    _lowerCAmelCase				(       self ):





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

							return self.maximum_flow






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a ):





							"""simple docstring"""
							super().__init__(_a )

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

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					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			      =					[
							    i
							    for i in range(self.verticies_count )
							    if i != self.source_index and i != self.sink_index
							]

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

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





				def    _lowerCAmelCase				(       self		, _a ):





							"""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(_a		, _a )

										self.relabel(_a )





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					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    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					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			      =					self.heights[to_index]

							if min_height is not None:
										lowerCamelCase			      =					min_height + 1


if __name__ == "__main__":
					lowerCAmelCase							:			List[str]     							=							[0]
					lowerCAmelCase							:			Dict     							=							[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							:			Any     							=							[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

					# prepare our network
					lowerCAmelCase							:			List[Any]     							=							FlowNetwork(graph, entrances, exits)
					# set algorithm
					flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
					# and calculate
					lowerCAmelCase							:			Union[str, Any]     							=							flow_network.find_maximum_flow()

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









"""simple docstring"""




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase							:			int     							=							logging.get_logger(__name__)

lowerCAmelCase							:			List[str]     							=							{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						"sew-d"





				def __init__(       self		, _a=32		, _a=768		, _a=12		, _a=12		, _a=3_072		, _a=2		, _a=512		, _a=256		, _a=True		, _a=True		, _a=("p2c", "c2p")		, _a="layer_norm"		, _a="gelu_python"		, _a=0.1		, _a=0.1		, _a=0.1		, _a=0.0		, _a=0.1		, _a=0.02		, _a=1e-7		, _a=1e-5		, _a="group"		, _a="gelu"		, _a=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		, _a=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		, _a=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		, _a=False		, _a=128		, _a=16		, _a=True		, _a=0.05		, _a=10		, _a=2		, _a=0.0		, _a=10		, _a=0		, _a="mean"		, _a=False		, _a=False		, _a=256		, _a=0		, _a=1		, _a=2		, **_a		, ):





							"""simple docstring"""
							super().__init__(**_a		, pad_token_id=_a		, bos_token_id=_a		, eos_token_id=_a )
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					feat_extract_norm
							lowerCamelCase			      =					feat_extract_activation
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					conv_bias
							lowerCamelCase			      =					num_conv_pos_embeddings
							lowerCamelCase			      =					num_conv_pos_embedding_groups
							lowerCamelCase			      =					len(self.conv_dim )
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					squeeze_factor
							lowerCamelCase			      =					max_position_embeddings
							lowerCamelCase			      =					position_buckets
							lowerCamelCase			      =					share_att_key
							lowerCamelCase			      =					relative_attention
							lowerCamelCase			      =					norm_rel_ebd
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					hidden_dropout
							lowerCamelCase			      =					attention_dropout
							lowerCamelCase			      =					activation_dropout
							lowerCamelCase			      =					feat_proj_dropout
							lowerCamelCase			      =					final_dropout
							lowerCamelCase			      =					layer_norm_eps
							lowerCamelCase			      =					feature_layer_norm_eps
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					vocab_size

							if (
							    (len(self.conv_stride ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim ) != self.num_feat_extract_layers)
							):
										raise ValueError(
										    """Configuration for convolutional layers is incorrect."""
										    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
										    f'but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'
										    f'= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCamelCase			      =					apply_spec_augment
							lowerCamelCase			      =					mask_time_prob
							lowerCamelCase			      =					mask_time_length
							lowerCamelCase			      =					mask_time_min_masks
							lowerCamelCase			      =					mask_feature_prob
							lowerCamelCase			      =					mask_feature_length
							lowerCamelCase			      =					mask_feature_min_masks

							# ctc loss
							lowerCamelCase			      =					ctc_loss_reduction
							lowerCamelCase			      =					ctc_zero_infinity

							# sequence classification
							lowerCamelCase			      =					use_weighted_layer_sum
							lowerCamelCase			      =					classifier_proj_size





				@property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return functools.reduce(operator.mul		, self.conv_stride		, 1 )



