






# using dfs for finding eulerian path traversal

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__=None    )			->							Dict:
  UpperCamelCase_:   str        =							(path or []) + [u]
  for v in graph[u]:
    if visited_edge[u][v] is False:
      UpperCamelCase_ ,UpperCamelCase_:   Any        =							True, True
      UpperCamelCase_:   List[str]        =							dfs(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )
  return path

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Dict:
  UpperCamelCase_:   int        =							0
  UpperCamelCase_:   Optional[int]        =							-1
  for i in range(UpperCAmelCase__    ):
    if i not in graph.keys():
      continue
    if len(graph[i]    ) % 2 == 1:
      odd_degree_nodes += 1
      UpperCamelCase_:   Union[str, Any]        =							i
  if odd_degree_nodes == 0:
    return 1, odd_node
  if odd_degree_nodes == 2:
    return 2, odd_node
  return 3, odd_node

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Optional[Any]:
  UpperCamelCase_:   str        =							[[False for _ in range(max_node + 1    )] for _ in range(max_node + 1    )]
  UpperCamelCase_ ,UpperCamelCase_:   Optional[int]        =							check_circuit_or_path(UpperCAmelCase__      ,       UpperCAmelCase__    )
  if check == 3:
    print('graph is not Eulerian'    )
    print('no path'    )
    return
  UpperCamelCase_:   str        =							1
  if check == 2:
    UpperCamelCase_:   Optional[int]        =							odd_node
    print('graph has a Euler path'    )
  if check == 1:
    print('graph has a Euler cycle'    )
  UpperCamelCase_:   str        =							dfs(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )
  print(UpperCAmelCase__    )

def     snake_case   ()			->							Optional[int]:
  UpperCamelCase_:   List[str]        =							{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
  UpperCamelCase_:   Tuple        =							{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
  UpperCamelCase_:   Dict        =							{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
  UpperCamelCase_:   str        =							{1: [2, 3], 2: [1, 3], 3: [1, 2]}
  UpperCamelCase_:   str        =							{
      1: [],
      2: []
      # all degree is zero
  }
  UpperCamelCase_:   Optional[Any]        =							1_0
  check_euler(UpperCAmelCase__      ,       UpperCAmelCase__    )
  check_euler(UpperCAmelCase__      ,       UpperCAmelCase__    )
  check_euler(UpperCAmelCase__      ,       UpperCAmelCase__    )
  check_euler(UpperCAmelCase__      ,       UpperCAmelCase__    )
  check_euler(UpperCAmelCase__      ,       UpperCAmelCase__    )


if __name__ == "__main__":
   main()







import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					0
	lowerCAmelCase						=					False
	lowerCAmelCase						=					3.0






class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								self.assertDictEqual(MockClass().to_kwargs()			,     {}   )
								self.assertDictEqual(MockClass(a=2   ).to_kwargs()			,     {'a': 2}   )
								self.assertDictEqual(MockClass(a=2			,     b=a   ).to_kwargs()			,     {'a': 2, 'b': True}   )
								self.assertDictEqual(MockClass(a=2			,     c=2.25   ).to_kwargs()			,     {'a': 2, 'c': 2.25}   )




	@require_cuda
	def 				_UpperCamelCase     (			self   )			->       str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								snake_case_           =				GradScalerKwargs(init_scale=10_24			,     growth_factor=2   )
								AcceleratorState._reset_state()
								snake_case_           =				Accelerator(mixed_precision='fp16'			,     kwargs_handlers=[scaler_handler]   )
								print(accelerator.use_fpaa   )
								snake_case_           =				accelerator.scaler

								# Check the kwargs have been applied
								self.assertEqual(scaler._init_scale			,     1_024.0   )
								self.assertEqual(scaler._growth_factor			,     2.0   )

								# Check the other values are at the default
								self.assertEqual(scaler._backoff_factor			,     0.5   )
								self.assertEqual(scaler._growth_interval			,     20_00   )
								self.assertEqual(scaler._enabled			,     a   )






	@require_multi_gpu
	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								snake_case_           =				['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
								execute_subprocess_async(a			,     env=os.environ.copy()   )


if __name__ == "__main__":
	lowercase    				=   DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
	lowercase    				=   Accelerator(kwargs_handlers=[ddp_scaler])
	lowercase    				=   torch.nn.Linear(100, 200)
	lowercase    				=   accelerator.prepare(model)

	# Check the values changed in kwargs
	lowercase    				=   ""
	lowercase    				=   model.bucket_bytes_cap // (1024 * 1024)
	if observed_bucket_cap_map != 15:
		error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
	if model.find_unused_parameters is not True:
		error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

	# Check the values of the defaults
	if model.dim != 0:
		error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
	if model.broadcast_buffers is not True:
		error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
	if model.gradient_as_bucket_view is not False:
		error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

	# Raise error at the end to make sure we don't stop at the first failure.
	if len(error_msg) > 0:
		raise ValueError(error_msg)





