


class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Optional[Any]:
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     graph

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







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Dict:
														if sources is int:
																					lowerCAmelCase__	       =     [sources]
														if sinks is int:
																					lowerCAmelCase__	       =     [sinks]

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

														lowerCAmelCase__	       =     sources[0]
														lowerCAmelCase__	       =     sinks[0]

														# make fake vertex if there are more
														# than one source or sink
														if len(lowerCamelCase_       ) > 1 or len(lowerCamelCase_       ) > 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   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Optional[int]:
														lowerCAmelCase__	       =     algorithm(self       )




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_       )	->							List[Any]:
														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   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														if not self.executed:
																					self._algorithm()
																					lowerCAmelCase__	       =     True







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														pass




class 		a__    (							a__  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_       )	->							Optional[int]:
														super().__init__(lowerCamelCase_       )
														# use this to save your result
														lowerCAmelCase__	       =     -1







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														if not self.executed:
																					raise Exception('''You should execute algorithm before using its result!'''       )

														return self.maximum_flow




class 		a__    (							a__  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_       )	->							Union[str, Any]:
														super().__init__(lowerCamelCase_       )

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

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







							def   __SCREAMING_SNAKE_CASE							(		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(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     vertices_list[i]
																					lowerCAmelCase__	       =     self.heights[vertex_index]
																					self.process_vertex(lowerCamelCase_       )
																					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(lowerCamelCase_       )       )
																												lowerCAmelCase__	       =     0
																					else:
																												i += 1

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







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							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(lowerCamelCase_ ,   lowerCamelCase_       )

																					self.relabel(lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Union[str, 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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Optional[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__":
		__UpperCAmelCase       =							[0]
		__UpperCAmelCase       =							[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],
		# ]
		__UpperCAmelCase       =							[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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



'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__UpperCAmelCase       =							{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							lowercase__      :       List[Any]     						=				TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														lowercase__      :       Optional[Any]     						=				{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														lowercase__      :       Tuple     						=				{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}







							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     pipeline(
														    task='''text-classification''' ,   model='''hf-internal-testing/tiny-random-distilbert''' ,   framework='''pt'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )

														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]       )

														lowerCAmelCase__	       =     text_classifier(['''This is great !''', '''This is bad'''] ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														    ] ,   )

														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   top_k=1       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )

														# Legacy behavior
														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )

														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [[{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]]       )

														lowerCAmelCase__	       =     text_classifier(['''This is great !''', '''Something else'''] ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														    ] ,   )

														lowerCAmelCase__	       =     text_classifier(['''This is great !''', '''Something else'''] ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [
														        {'''label''': '''LABEL_0''', '''score''': 0.504},
														        {'''label''': '''LABEL_0''', '''score''': 0.504},
														    ] ,   )







							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														import torch

														lowerCAmelCase__	       =     pipeline(
														    task='''text-classification''' ,   model='''hf-internal-testing/tiny-random-distilbert''' ,   framework='''pt''' ,   device=torch.device('''cpu'''       ) ,   )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )







							@require_tf
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__	       =     pipeline(
														    task='''text-classification''' ,   model='''hf-internal-testing/tiny-random-distilbert''' ,   framework='''tf'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )







							@slow
							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     pipeline('''text-classification'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''This is bad !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''NEGATIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''Birds are a type of animal'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 0.988}]       )







							@slow
							@require_tf
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     pipeline('''text-classification''' ,   framework='''tf'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''This is bad !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''NEGATIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''Birds are a type of animal'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 0.988}]       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Tuple:
														lowerCAmelCase__	       =     TextClassificationPipeline(model=lowerCamelCase_ ,   tokenizer=lowerCamelCase_       )
														return text_classifier, ["HuggingFace is in", "This is another test"]







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     text_classifier.model
														# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
														lowerCAmelCase__	       =     '''HuggingFace is in'''
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_       )

														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}]       )
														self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()       )

														lowerCAmelCase__	       =     ['''HuggingFace is in ''', '''Paris is in France''']
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}, {'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] ,   )
														self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()       )
														self.assertTrue(outputs[1]['''label'''] in model.config.idalabel.values()       )

														# Forcing to get all results with `top_k=None`
														# This is NOT the legacy format
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_ ,   top_k=lowerCamelCase_       )
														lowerCAmelCase__	       =     len(model.config.idalabel.values()       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [[{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] * N, [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] * N] ,   )

														lowerCAmelCase__	       =     {'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   {'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )} ,   )
														self.assertTrue(outputs['''label'''] in model.config.idalabel.values()       )

														# This might be used a text pair, but tokenizer + pipe interaction
														# makes it hard to understand that it's not using the pair properly
														# https://github.com/huggingface/transformers/issues/17305
														# We disabled this usage instead as it was outputting wrong outputs.
														lowerCAmelCase__	       =     [['''HuggingFace is in ''', '''Paris is in France''']]
														with self.assertRaises(lowerCamelCase_       ):
																					text_classifier(lowerCamelCase_       )

														# This used to be valid for doing text pairs
														# We're keeping it working because of backward compatibility
														lowerCAmelCase__	       =     text_classifier([[['''HuggingFace is in ''', '''Paris is in France''']]]       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] ,   )
														self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()       )