






'''simple docstring'''

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict







@dataclass
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Dict   		=			ComputeEnvironment.AMAZON_SAGEMAKER
       snake_case__    :     Tuple   		=			True
       snake_case__    :     List[str]   		=			'ml.p3.2xlarge'
       snake_case__    :     Optional[Any]   		=			'accelerate_sagemaker_execution_role'
       snake_case__    :     Any   		=			'hf-sm'
       snake_case__    :     Optional[int]   		=			'us-east-1'
       snake_case__    :     int   		=			1
       snake_case__    :     Union[str, Any]   		=			'accelerate-sagemaker-1'
       snake_case__    :     int   		=			'1.6'
       snake_case__    :     int   		=			'4.4'
       snake_case__    :     str   		=			'train.py'
       snake_case__    :     Tuple   		=			[
           '--model_name_or_path',
           'bert',
           '--do_train',
           'False',
           '--epochs',
           '3',
           '--learning_rate',
           '5e-5',
           '--max_steps',
           '50.5',
       ]
       snake_case__    :     Optional[Any]   		=			[
           '--model_name_or_path',
           'bert',
           '--do_train',
           '--do_test',
           'False',
           '--do_predict',
           '--epochs',
           '3',
           '--learning_rate',
           '5e-5',
           '--max_steps',
           '50.5',
       ]







class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args					)
            assert isinstance(converted_args["""model_name_or_path"""]	,    A__					)
            assert isinstance(converted_args["""do_train"""]	,    A__					)
            assert isinstance(converted_args["""epochs"""]	,    A__					)
            assert isinstance(converted_args["""learning_rate"""]	,    A__					)
            assert isinstance(converted_args["""max_steps"""]	,    A__					)

            with pytest.raises(A__					):
                 _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args					)











'''simple docstring'''

def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     Union[str, Any]:


     """simple docstring"""
     # Return True if there is node that has not iterated.
     __lowerCamelCase						:       Optional[Any]				   =				[False] * len(_lowercase     )
     __lowerCamelCase						:       Dict				   =				[]
     queue.append(_lowercase     )
     __lowerCamelCase						:       Optional[int]				   =				True

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

     return visited[t]
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # This array is filled by BFS and to store path
     __lowerCamelCase						:       Any				   =				[-1] * (len(_lowercase     ))
     __lowerCamelCase						:       Optional[int]				   =				0
     while bfs(_lowercase,						_lowercase,						_lowercase,						_lowercase     ):
          __lowerCamelCase						:       List[Any]				   =				float("""Inf"""     )
          __lowerCamelCase						:       Union[str, Any]				   =				sink

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

          max_flow += path_flow
          __lowerCamelCase						:       str				   =				sink

          while v != source:
               __lowerCamelCase						:       List[Any]				   =				parent[v]
               graph[u][v] -= path_flow
               graph[v][u] += path_flow
               __lowerCamelCase						:       Dict				   =				parent[v]
     return max_flow


UpperCAmelCase__						:str          =			[
    [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],
]

UpperCAmelCase__       ,				UpperCAmelCase__						:Optional[Any]          =			0, 5
print(ford_fulkerson(graph, source, sink))



