

import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
	import transformer_engine.pytorch as te




def        __UpperCamelCase     (      _A					:							Any      ,  _A					:							Optional[int]=True      ,  _A					:							Union[str, Any]=True      ,  _A					:							str=True		)				->Optional[int]:



							"""simple docstring"""
							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
																					lowerCamelCase_								=module.bias is not None
																					lowerCamelCase_								=te.Linear(
																					    module.in_features      ,  module.out_features      ,  bias=_A      ,  params_dtype=module.weight.dtype		)
																					lowerCamelCase_								=module.weight.data.clone()
																					if has_bias:
																												lowerCamelCase_								=module.bias.data.clone()

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

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

																					setattr(_A      ,  _A      ,  _A		)
														elif isinstance(_A      ,  te.LayerNorm		) and not to_transformer_engine and _convert_ln:
																					lowerCamelCase_								=nn.LayerNorm(module.normalized_shape[0]      ,  eps=module.eps      ,  params_dtype=module.weight.dtype		)
																					lowerCamelCase_								=module.weight.data.clone()
																					lowerCamelCase_								=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					:							Optional[Any]		)				->int:



							"""simple docstring"""
							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



from __future__ import annotations




def        __UpperCamelCase     (      _A					:							int      ,  _A					:							list[int]      ,  _A					:							list[int]      ,  _A					:							int		)				->int:



							"""simple docstring"""

							# Base Case
							if counter == 0 or capacity == 0:
														return 0

							# If weight of the nth item is more than Knapsack of capacity,
							#   then this item cannot be included in the optimal solution,
							# else return the maximum of two cases:
							#   (1) nth item included
							#   (2) not included
							if weights[counter - 1] > capacity:
														return knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
							else:
														lowerCamelCase_								=capacity - weights[counter - 1]
														lowerCamelCase_								=values[counter - 1] + knapsack(
														    _A      ,  _A      ,  _A      ,  counter - 1		)
														lowerCamelCase_								=knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
														return max(_A      ,  _A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
