






lowercase  							= [[0, 1, 1], [0, 0, 1], [1, 0, 0]]




class 			UpperCamelCase_				:



	'''simple docstring'''




	def __init__(	self	,      a )	->   Optional[Any]:
								snake_case_           =							name
								snake_case_           =							[]
								snake_case_           =							[]




	def 							_UpperCamelCase      (	self	,      a )	->   Optional[int]:
								self.inbound.append(a )




	def 							_UpperCamelCase      (	self	,      a )	->   Tuple:
								self.outbound.append(a )






	def __repr__(	self )	->   Dict:
								return F'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''






def 						__UpperCAmelCase      (					a_							,					a_=3							,					a_=0.85):
							snake_case_           =							{}
							for node in nodes:
														snake_case_           =							1

							snake_case_           =							{}
							for node in nodes:
														snake_case_           =							len(node.outbound)

							for i in range(a_):
														print(f'''======= Iteration {i + 1} =======''')
														for _, node in enumerate(a_):
																					snake_case_           =							(1 - d) + d * sum(
																					    ranks[ib] / outbounds[ib] for ib in node.inbound)
														print(a_)





def 						__UpperCAmelCase      (					):
							snake_case_           =							list(input('Enter Names of the Nodes: ').split())

							snake_case_           =							[Node(a_) for name in names]

							for ri, row in enumerate(a_):
														for ci, col in enumerate(a_):
																					if col == 1:
																												nodes[ci].add_inbound(names[ri])
																												nodes[ri].add_outbound(names[ci])

							print('======= Nodes =======')
							for node in nodes:
														print(a_)

							page_rank(a_)


if __name__ == "__main__":
	main()













import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
	import transformer_engine.pytorch as te





def 						__UpperCAmelCase      (					a_							,					a_=True							,					a_=True							,					a_=True):
							if not is_fpa_available():
														raise ImportError('Using `convert_model` requires transformer_engine to be installed.')
							for name, module in model.named_children():
														if isinstance(a_							,					nn.Linear) and to_transformer_engine and _convert_linear:
																					# Return early if the linear layer weights are not multiples of 16
																					if any(p % 16 != 0 for p in module.weight.shape):
																												return
																					snake_case_           =							module.bias is not None
																					snake_case_           =							te.Linear(
																					    module.in_features							,					module.out_features							,					bias=a_							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					if has_bias:
																												snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														elif isinstance(a_							,					nn.LayerNorm) and to_transformer_engine and _convert_ln:
																					snake_case_           =							te.LayerNorm(module.normalized_shape[0]							,					eps=module.eps							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														elif isinstance(a_							,					te.Linear) and not to_transformer_engine and _convert_linear:
																					snake_case_           =							module.bias is not None
																					snake_case_           =							nn.Linear(
																					    module.in_features							,					module.out_features							,					bias=a_							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					if has_bias:
																												snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														elif isinstance(a_							,					te.LayerNorm) and not to_transformer_engine and _convert_ln:
																					snake_case_           =							nn.LayerNorm(module.normalized_shape[0]							,					eps=module.eps							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														else:
																					convert_model(
																					    a_							,					to_transformer_engine=a_							,					_convert_linear=_convert_linear							,					_convert_ln=_convert_ln							,					)





def 						__UpperCAmelCase      (					a_):
							if not is_fpa_available():
														raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.')
							for m in model.modules():
														if isinstance(a_							,					(te.LayerNorm, te.Linear)):
																					return True
							return False





