



import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
				import transformer_engine.pytorch as te

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_=True	,			lowercase_=True	,			lowercase_=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(lowercase_	,			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
																		UpperCAmelCase    =				module.bias is not None
																		UpperCAmelCase    =				te.Linear(
																		    module.in_features	,			module.out_features	,			bias=lowercase_	,			params_dtype=module.weight.dtype		)
																		UpperCAmelCase    =				module.weight.data.clone()
																		if has_bias:
																								UpperCAmelCase    =				module.bias.data.clone()

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

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

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

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






def 				_lowerCAmelCase   (     lowercase_		):
						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(lowercase_	,			(te.LayerNorm, te.Linear)		):
																		return True
						return False










"""simple docstring"""


from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

snake_case_         = TypeVar("""T""", bound=bool)







class    A_    (  Generic[T]       ):



						"""simple docstring"""






						def __init__(      self				:str     ,						lowercase_				:T      )       ->    None:
												UpperCAmelCase    =				value
												UpperCAmelCase    =				None
												UpperCAmelCase    =				None






						@property
						def 			UpperCAmelCase__  (      self				:Any      )       ->    T:
												return self._value






						@staticmethod
						def 			UpperCAmelCase__  (      lowercase_				:RandomizedHeapNode[T] | None     ,						lowercase_				:RandomizedHeapNode[T] | None      )       ->    RandomizedHeapNode[T] | None:
												if not roota:
																		return roota

												if not roota:
																		return roota

												if roota.value > roota.value:
																		UpperCAmelCase   ,					UpperCAmelCase    =				roota, roota

												if random.choice([True, False]      ):
																		UpperCAmelCase   ,					UpperCAmelCase    =				roota.right, roota.left

												UpperCAmelCase    =				RandomizedHeapNode.merge(roota.left     ,						lowercase_      )

												return roota







class    A_    (  Generic[T]       ):



						"""simple docstring"""






						def __init__(      self				:Optional[Any]     ,						lowercase_				:Iterable[T] | None = ()      )       ->    None:
												UpperCAmelCase    =				None

												if data:
																		for item in data:
																								self.insert(lowercase_      )






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:T      )       ->    None:
												UpperCAmelCase    =				RandomizedHeapNode.merge(self._root     ,						RandomizedHeapNode(lowercase_      )      )






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    T | None:
												UpperCAmelCase    =				self.top()

												if self._root is None:
																		return None

												UpperCAmelCase    =				RandomizedHeapNode.merge(self._root.left     ,						self._root.right      )

												return result






						def 			UpperCAmelCase__  (      self				:Tuple      )       ->    T:
												if not self._root:
																		raise IndexError('Can\'t get top element for the empty heap.'      )
												return self._root.value






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    None:
												UpperCAmelCase    =				None






						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    list[Any]:
												UpperCAmelCase    =				[]
												while self:
																		result.append(self.pop()      )

												return result






						def __bool__(      self				:Optional[Any]      )       ->    bool:
												return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





