


'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :str				,      lowerCamelCase_  :str ):







		'''simple docstring'''
		# Return True if there is node that has not iterated.
		snake_case_		:							Optional[int]			       =						[False] * len(lowerCamelCase_ )
		snake_case_		:							Dict			       =						[]
		queue.append(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						True

		while queue:
				snake_case_		:							Union[str, Any]			       =						queue.pop(0 )
				for ind in range(len(graph[u] ) ):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(lowerCamelCase_ )
								snake_case_		:							str			       =						True
								snake_case_		:							Optional[Any]			       =						u

		return visited[t]





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		# This array is filled by BFS and to store path
		snake_case_		:							Optional[int]			       =						[-1] * (len(lowerCamelCase_ ))
		snake_case_		:							Tuple			       =						0
		while bfs(lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ ):
				snake_case_		:							int			       =						float("""Inf""" )
				snake_case_		:							Any			       =						sink

				while s != source:
						# Find the minimum value in select path
						snake_case_		:							Optional[Any]			       =						min(lowerCamelCase_				,      graph[parent[s]][s] )
						snake_case_		:							Union[str, Any]			       =						parent[s]

				max_flow += path_flow
				snake_case_		:							Dict			       =						sink

				while v != source:
						snake_case_		:							Optional[int]			       =						parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						snake_case_		:							Any			       =						parent[v]
		return max_flow


__A	:	Union[str, Any]     =     [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__A,   __A	:	Union[str, Any]     =     0, 5






print(ford_fulkerson(graph, source, sink))



'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch
@require_torch
@require_optimum
@slow
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoTokenizer.from_pretrained(_UpperCamelCase			)
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						tokenizer("""This is me"""  ,return_tensors="""pt"""			)

									snake_case_		:							List[str]			       =						model.to_bettertransformer()

									self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									snake_case_		:							List[str]			       =						model.generate(**_UpperCamelCase			)

									snake_case_		:							Dict			       =						model.reverse_bettertransformer()

									self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

											self.assertFalse(
											    any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

											snake_case_		:							Optional[Any]			       =						model_reloaded.generate(**_UpperCamelCase			)
											self.assertTrue(torch.allclose(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						model.to_bettertransformer()

									with tempfile.TemporaryDirectory() as tmpdirname:
											with self.assertRaises(_UpperCamelCase			):
													model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							Any			       =						model.reverse_bettertransformer()
											model.save_pretrained(_UpperCamelCase			)