"""simple docstring"""







from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
						import torch

SCREAMING_SNAKE_CASE						=    logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
						from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


						def   __lowerCAmelCase(		cls      ):






							"""simple docstring"""





							return cls


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,):






	"""simple docstring"""





	_lowercase			:     List[Any]      					=				device
	_lowercase			:     Any      					=				shape[0]

	_lowercase			:     List[Any]      					=				layout or torch.strided
	_lowercase			:     str      					=				device or torch.device('cpu'      )

	if generator is not None:
		_lowercase			:     List[Any]      					=				generator.device.type if not isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) else generator[0].device.type
		if gen_device_type != device.type and gen_device_type == "cpu":
			_lowercase			:     Union[str, Any]      					=				'cpu'
			if device != "mps":
				logger.info(
				    F'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
				    F''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
				    F''' slighly speed up this function by passing a generator that was created on the {device} device.'''      )
		elif gen_device_type != device.type and gen_device_type == "cuda":
			raise ValueError(F'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''      )

	if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
		_lowercase			:     Dict      					=				(1,) + shape[1:]
		_lowercase			:     int      					=				[
		    torch.randn(__UpperCAmelCase  ,generator=generator[i]  ,device=__UpperCAmelCase  ,dtype=__UpperCAmelCase  ,layout=__UpperCAmelCase      )
		    for i in range(__UpperCAmelCase      )
		]
		_lowercase			:     List[str]      					=				torch.cat(__UpperCAmelCase  ,dim=0      ).to(__UpperCAmelCase      )
	else:
		_lowercase			:     Tuple      					=				torch.randn(__UpperCAmelCase  ,generator=__UpperCAmelCase  ,device=__UpperCAmelCase  ,dtype=__UpperCAmelCase  ,layout=__UpperCAmelCase      ).to(__UpperCAmelCase      )

	return latents







def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	if is_torch_version('<'  ,'2.0.0'      ) or not hasattr(__UpperCAmelCase  ,'_dynamo'      ):
		return False
	return isinstance(__UpperCAmelCase  ,torch._dynamo.eval_frame.OptimizedModule      )









class        snake_case__				:
				"""simple docstring"""



				def __init__( self							:	List[str]   , __lowerCamelCase							:	int				)   ->	None:
							a       =     data
							a       =     42
							a       =     42




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			a       =     0
			a       =     x




def      __magic_name__							(					A						:   Node,    A						:   Node  ):






			'''simple docstring'''







			a       ,						a       =     find_set(A  ), find_set(A  )
			if x == y:
						return

			elif x.rank > y.rank:
						a       =     x
			else:
						a       =     y
						if x.rank == y.rank:
									y.rank += 1




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			if x != x.parent:
						a       =     find_set(x.parent  )
			return x.parent




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			a       =     ({0, 1, 2}, {3, 4, 5})
			for s in sets:
						if node.data in s:
									return s
			a       =     F"""{node.data} is not in {sets}"""
			raise ValueError(A  )




def      __magic_name__							(					):






			'''simple docstring'''







			a       =     [Node(A  ) for i in range(6  )]
			for v in vertex:
						make_set(A  )

			union_set(vertex[0],    vertex[1]  )
			union_set(vertex[1],    vertex[2]  )
			union_set(vertex[3],    vertex[4]  )
			union_set(vertex[3],    vertex[5]  )

			for nodea in vertex:
						for nodea in vertex:
									if find_python_set(A  ).isdisjoint(find_python_set(A  )  ):
												assert find_set(A  ) != find_set(A  )
									else:
												assert find_set(A  ) == find_set(A  )


if __name__ == "__main__":
		test_disjoint_set()






