
class       __A:

					def __init__(	self					:     Optional[int]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_							  =					None
												lowerCamelCase_							  =					None
												lowerCamelCase_							  =					graph

												self._normalize_graph(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					len(__UpperCamelCase   )
												lowerCamelCase_							  =					None

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     List[str]   ):
												if sources is int:
																			lowerCamelCase_							  =					[sources]
												if sinks is int:
																			lowerCamelCase_							  =					[sinks]

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

												lowerCamelCase_							  =					sources[0]
												lowerCamelCase_							  =					sinks[0]

												# make fake vertex if there are more
												# than one source or sink
												if len(__UpperCamelCase   ) > 1 or len(__UpperCamelCase   ) > 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    lowercase__      (	self					:     Optional[Any]   ):
												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    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					algorithm(self   )





class       __A:

					def __init__(	self					:     Optional[Any]   ,					__UpperCamelCase					:     int   ):
												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    lowercase__      (	self					:     int   ):
												if not self.executed:
																			self._algorithm()
																			lowerCamelCase_							  =					True



					def    lowercase__      (	self					:     int   ):
												pass





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Tuple   ):
												super().__init__(__UpperCamelCase   )
												# use this to save your result
												lowerCamelCase_							  =					-1



					def    lowercase__      (	self					:     List[str]   ):
												if not self.executed:
																			raise Exception("""You should execute algorithm before using its result!"""   )

												return self.maximum_flow





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     List[str]   ):
												super().__init__(__UpperCamelCase   )

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

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

					def    lowercase__      (	self					:     Tuple   ):
												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(__UpperCamelCase   ):
																			lowerCamelCase_							  =					vertices_list[i]
																			lowerCamelCase_							  =					self.heights[vertex_index]
																			self.process_vertex(__UpperCamelCase   )
																			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(__UpperCamelCase   )   )
																										lowerCamelCase_							  =					0
																			else:
																										i += 1

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

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     List[str]   ):
												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(__UpperCamelCase   ,					__UpperCamelCase   )

																			self.relabel(__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     List[Any]   ):
												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    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     int   ):
												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__":
		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}""")

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor





class       __A:

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[str, Any]=3   ,					__UpperCamelCase					:     int=3_2   ,					__UpperCamelCase					:     Any=3   ,					__UpperCamelCase					:     List[str]=1_0   ,					__UpperCamelCase					:     int=[1_0, 2_0, 3_0, 4_0]   ,					__UpperCamelCase					:     List[str]=[1, 1, 2, 1]   ,					__UpperCamelCase					:     str=True   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     Dict="relu"   ,					__UpperCamelCase					:     int=3   ,					__UpperCamelCase					:     Dict=None   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					image_size
												lowerCamelCase_							  =					num_channels
												lowerCamelCase_							  =					embeddings_size
												lowerCamelCase_							  =					hidden_sizes
												lowerCamelCase_							  =					depths
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					num_labels
												lowerCamelCase_							  =					scope
												lowerCamelCase_							  =					len(__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

												lowerCamelCase_							  =					None
												if self.use_labels:
																			lowerCamelCase_							  =					ids_tensor([self.batch_size]   ,					self.num_labels   )

												lowerCamelCase_							  =					self.get_config()
												return config, pixel_values, labels

					def    lowercase__      (	self					:     str   ):
												return RegNetConfig(
												    num_channels=self.num_channels   ,					embeddings_size=self.embeddings_size   ,					hidden_sizes=self.hidden_sizes   ,					depths=self.depths   ,					hidden_act=self.hidden_act   ,					num_labels=self.num_labels   ,					)

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					TFRegNetModel(config=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					training=__UpperCamelCase   )
												# expected last hidden states: B, C, H // 32, W // 32
												self.parent.assertEqual(
												    result.last_hidden_state.shape   ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   ,					)

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					self.num_labels
												lowerCamelCase_							  =					TFRegNetForImageClassification(__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					labels=__UpperCamelCase   ,					training=__UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )



					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					config_and_inputs
												lowerCamelCase_							  =					{"""pixel_values""": pixel_values}
												return config, inputs_dict





@require_tf
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE			    =    (
					    {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					TFRegNetModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   ,					has_text_modality=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												return

					@unittest.skip(reason="""RegNet does not use inputs_embeds"""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0   ,					reason="""TF does not support backprop for grouped convolutions on CPU."""   ,					)
					@slow
					def    lowercase__      (	self					:     Tuple   ):
												super().test_keras_fit()

					@unittest.skip(reason="""RegNet does not support input and output embeddings"""   )
					def    lowercase__      (	self					:     Optional[Any]   ):
												pass

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			lowerCamelCase_							  =					inspect.signature(model.call   )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			lowerCamelCase_							  =					[*signature.parameters.keys()]

																			lowerCamelCase_							  =					["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ):
												def check_hidden_states_output(__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Any   ):
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			lowerCamelCase_							  =					model(**self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )   ,					training=__UpperCamelCase   )

																			lowerCamelCase_							  =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			lowerCamelCase_							  =					self.model_tester.num_stages
																			self.assertEqual(len(__UpperCamelCase   )   ,					expected_num_stages + 1   )

																			# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]   )   ,					[self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,					)

												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()
												lowerCamelCase_							  =					["""basic""", """bottleneck"""]
												for model_class in self.all_model_classes:
																			for layer_type in layers_type:
																										lowerCamelCase_							  =					layer_type
																										lowerCamelCase_							  =					True
																										check_hidden_states_output(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																										# check that output_hidden_states also work using config
																										del inputs_dict["output_hidden_states"]
																										lowerCamelCase_							  =					True

																										check_hidden_states_output(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

												def check_equivalence(__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[Any]={}   ):
																			lowerCamelCase_							  =					model(__UpperCamelCase   ,					return_dict=__UpperCamelCase   ,					**__UpperCamelCase   )
																			lowerCamelCase_							  =					model(__UpperCamelCase   ,					return_dict=__UpperCamelCase   ,					**__UpperCamelCase   ).to_tuple()

																			def recursive_check(__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ):
																										if isinstance(__UpperCamelCase   ,					(List, Tuple)   ):
																																	for tuple_iterable_value, dict_iterable_value in zip(__UpperCamelCase   ,					__UpperCamelCase   ):
																																								recursive_check(__UpperCamelCase   ,					__UpperCamelCase   )
																										elif tuple_object is None:
																																	return
																										else:
																																	self.assertTrue(
																																	    all(tf.equal(__UpperCamelCase   ,					__UpperCamelCase   )   )   ,					msg=(
																																	        """Tuple and dict output are not equal. Difference:"""
																																	        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object   )   )}'''
																																	    )   ,					)

																										recursive_check(__UpperCamelCase   ,					__UpperCamelCase   )

												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					{"""output_hidden_states""": True}   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					{"""output_hidden_states""": True}   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase   )



					@slow
					def    lowercase__      (	self					:     List[Any]   ):
												for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					TFRegNetModel.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )



def      __lowerCAmelCase						(							)  ->    Optional[int]:
							lowerCamelCase_							  =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
							return image





@require_tf
@require_vision
class       __A(				unittest.TestCase       ):

					@cached_property
					def    lowercase__      (	self					:     Union[str, Any]   ):
												return (
												    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
												    if is_vision_available()
												    else None
												)



					@slow
					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

												lowerCamelCase_							  =					self.default_image_processor
												lowerCamelCase_							  =					prepare_img()
												lowerCamelCase_							  =					image_processor(images=__UpperCamelCase   ,					return_tensors="""tf"""   )

												# forward pass
												lowerCamelCase_							  =					model(**__UpperCamelCase   ,					training=__UpperCamelCase   )

												# verify the logits
												lowerCamelCase_							  =					tf.TensorShape((1, 1_0_0_0)   )
												self.assertEqual(outputs.logits.shape   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					tf.constant([-0.4180, -1.5051, -3.4836]   )

												tf.debugging.assert_near(outputs.logits[0, :3]   ,					__UpperCamelCase   ,					atol=1E-4   )





