


"""simple docstring"""




import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase						=		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
lowerCamelCase						=		'    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'





class  _a							(    unittest.TestCase):




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	List[str]:
			lowerCAmelCase__       :      List[Any]   					= tempfile.mkdtemp()
			os.makedirs(os.path.join(self.transformer_dir		, '''models/bert/'''		)		)
			lowerCAmelCase__       :      List[Any]   					= self.transformer_dir
			shutil.copy(
			    os.path.join(__lowerCamelCase		, '''src/transformers/models/bert/modeling_bert.py'''		)		, os.path.join(self.transformer_dir		, '''models/bert/modeling_bert.py'''		)		, )




	def 							UpperCAmelCase__( self						:		Tuple		)->	Optional[Any]:
			lowerCAmelCase__       :      List[str]   					= '''src/transformers'''
			shutil.rmtree(self.transformer_dir		)




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		)->	Tuple:
			lowerCAmelCase__       :      Tuple   					= comment + F'\nclass {class_name}(nn.Module):\n' + class_code
			if overwrite_result is not None:
					lowerCAmelCase__       :      str   					= comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
			lowerCAmelCase__       :      Dict   					= black.Mode(target_versions={black.TargetVersion.PYaa}		, line_length=119		)
			lowerCAmelCase__       :      Tuple   					= black.format_str(__lowerCamelCase		, mode=__lowerCamelCase		)
			lowerCAmelCase__       :      Any   					= os.path.join(self.transformer_dir		, '''new_code.py'''		)
			with open(__lowerCamelCase		, '''w'''		, newline='''\n'''		) as f:
					f.write(__lowerCamelCase		)
			if overwrite_result is None:
					self.assertTrue(len(check_copies.is_copy_consistent(__lowerCamelCase		)		) == 0		)
			else:
					check_copies.is_copy_consistent(f.name		, overwrite=__lowerCamelCase		)
					with open(__lowerCamelCase		, '''r'''		) as f:
							self.assertTrue(f.read()		, __lowerCamelCase		)




	def 							UpperCAmelCase__( self						:		str		)->	str:
			lowerCAmelCase__       :      Any   					= check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''		)
			self.assertEqual(__lowerCamelCase		, __lowerCamelCase		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	Optional[Any]:
			self.check_copy_consistency(
			    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''		, '''BertLMPredictionHead'''		, REFERENCE_CODE + '''\n'''		, )

			# With no empty line at the end
			self.check_copy_consistency(
			    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''		, '''BertLMPredictionHead'''		, __lowerCamelCase		, )

			# Copy consistency with rename
			self.check_copy_consistency(
			    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''		, '''TestModelLMPredictionHead'''		, re.sub('''Bert'''		, '''TestModel'''		, __lowerCamelCase		)		, )

			# Copy consistency with a really long name
			lowerCAmelCase__       :      Any   					= '''TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
			self.check_copy_consistency(
			    F'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'		, F'{long_class_name}LMPredictionHead'		, re.sub('''Bert'''		, __lowerCamelCase		, __lowerCamelCase		)		, )

			# Copy consistency with overwrite
			self.check_copy_consistency(
			    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''		, '''TestModelLMPredictionHead'''		, __lowerCamelCase		, overwrite_result=re.sub('''Bert'''		, '''TestModel'''		, __lowerCamelCase		)		, )




	def 							UpperCAmelCase__( self						:		Any		)->	int:
			lowerCAmelCase__       :      Union[str, Any]   					= check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

			lowerCAmelCase__       :      Dict   					= (
			    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
			    ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
			    ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
			    ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'''
			    ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'''
			    ''' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
			    ''' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'''
			    ''' method has been applied to compress GPT2 into'''
			    ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
			    ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
			    ''' Multilingual BERT into'''
			    ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
			    ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'''
			    ''' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'''
			    ''' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'''
			    ''' Luong, Quoc V. Le, Christopher D. Manning.'''
			)
			lowerCAmelCase__       :      Optional[int]   					= (
			    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
			    ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
			    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
			    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
			)
			lowerCAmelCase__       :      int   					= (
			    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
			    ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
			    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
			    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'''
			    ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'''
			    ''' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
			    ''' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'''
			    ''' method has been applied to compress GPT2 into'''
			    ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
			    ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
			    ''' Multilingual BERT into'''
			    ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
			    ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'''
			    ''' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'''
			    ''' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'''
			    ''' Christopher D. Manning 发布。\n'''
			)

			lowerCAmelCase__       :      Dict   					= check_copies.convert_to_localized_md(
			    __lowerCamelCase		, __lowerCamelCase		, localized_readme['''format_model_list''']		)

			self.assertFalse(__lowerCamelCase		)
			self.assertEqual(__lowerCamelCase		, __lowerCamelCase		)

			lowerCAmelCase__       :      List[str]   					= check_copies.convert_to_localized_md(
			    __lowerCamelCase		, __lowerCamelCase		, localized_readme['''format_model_list''']		)

			# Check whether the number of models is equal to README.md after conversion.
			self.assertTrue(__lowerCamelCase		)

			lowerCAmelCase__       :      Optional[Any]   					= (
			    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
			    ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
			    ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
			    ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'''
			)
			lowerCAmelCase__       :      Any   					= (
			    '''1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'''
			    ''' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
			    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
			    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
			)
			lowerCAmelCase__       :      List[str]   					= (
			    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
			    ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
			    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
			    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
			)

			lowerCAmelCase__       :      Any   					= check_copies.convert_to_localized_md(
			    __lowerCamelCase		, __lowerCamelCase		, localized_readme['''format_model_list''']		)

			# Check if the model link is synchronized.
			self.assertEqual(__lowerCamelCase		, __lowerCamelCase		)








class  _a							:




	def __init__( self						:		str		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	Tuple:
			lowerCAmelCase__       :      List[Any]   					= None
			lowerCAmelCase__       :      int   					= None
			lowerCAmelCase__       :      Union[str, Any]   					= graph

			self._normalize_graph(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Tuple   					= len(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[int]   					= None




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	Union[str, Any]:
			if sources is int:
					lowerCAmelCase__       :      List[Any]   					= [sources]
			if sinks is int:
					lowerCAmelCase__       :      Optional[Any]   					= [sinks]

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

			lowerCAmelCase__       :      Union[str, Any]   					= sources[0]
			lowerCAmelCase__       :      Dict   					= sinks[0]

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

					lowerCAmelCase__       :      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__       :      Optional[Any]   					= max_input_flow
					lowerCAmelCase__       :      List[str]   					= 0

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




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Union[str, 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 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	int:
			lowerCAmelCase__       :      int   					= algorithm(self		)





class  _a							:




	def __init__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Tuple		)->	Union[str, Any]:
			lowerCAmelCase__       :      Tuple   					= flow_network
			lowerCAmelCase__       :      Dict   					= flow_network.verticesCount
			lowerCAmelCase__       :      Optional[Any]   					= flow_network.sourceIndex
			lowerCAmelCase__       :      Optional[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__       :      str   					= flow_network.graph
			lowerCAmelCase__       :      Optional[int]   					= False




	def 							UpperCAmelCase__( self						:		List[str]		)->	Dict:
			if not self.executed:
					self._algorithm()
					lowerCAmelCase__       :      Any   					= True




	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	int:
			pass





class  _a							(    _lowercase):




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	Union[str, Any]:
			super().__init__(_SCREAMING_SNAKE_CASE		)
			# use this to save your result
			lowerCAmelCase__       :      Dict   					= -1




	def 							UpperCAmelCase__( self						:		Any		)->	Optional[Any]:
			if not self.executed:
					raise Exception('''You should execute algorithm before using its result!'''		)

			return self.maximum_flow





class  _a							(    _lowercase):




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		List[str]		)->	List[str]:
			super().__init__(_SCREAMING_SNAKE_CASE		)

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

			lowerCAmelCase__       :      Optional[Any]   					= [0] * self.verticies_count
			lowerCAmelCase__       :      str   					= [0] * self.verticies_count




	def 							UpperCAmelCase__( self						:		Any		)->	List[Any]:
			lowerCAmelCase__       :      Optional[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__       :      str   					= 0
			while i < len(_SCREAMING_SNAKE_CASE		):
					lowerCAmelCase__       :      Union[str, Any]   					= vertices_list[i]
					lowerCAmelCase__       :      Any   					= self.heights[vertex_index]
					self.process_vertex(_SCREAMING_SNAKE_CASE		)
					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(_SCREAMING_SNAKE_CASE		)		)
							lowerCAmelCase__       :      Optional[Any]   					= 0
					else:
							i += 1

			lowerCAmelCase__       :      Optional[Any]   					= sum(self.preflow[self.source_index]		)




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	Optional[int]:
			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(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

					self.relabel(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	Union[str, Any]:
			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 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Any		)->	Optional[int]:
			lowerCAmelCase__       :      Optional[int]   					= 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__       :      List[Any]   					= self.heights[to_index]

			if min_height is not None:
					lowerCAmelCase__       :      Optional[Any]   					= 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}''')




