



"""simple docstring"""







import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class 			_a     (   nn.Linear):



			def __init__( self       :							List[str]      ,  _lowercase       :							int      ,  _lowercase       :							int      ,  _lowercase       :							bool = True      ,  _lowercase       :							str = "constant"      ,  _lowercase       :							float = 0.0      ,  _lowercase       :							str = "topK"      ,  )       ->				Union[str, Any]:
									super(_lowercase      ,  self		).__init__(in_features=_lowercase      ,  out_features=_lowercase      ,  bias=_lowercase		)
									assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
									snake_case      : List[Any]							=   pruning_method

									if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
															snake_case      : str							=   mask_scale
															snake_case      : int							=   mask_init
															snake_case      : List[Any]							=   nn.Parameter(torch.empty(self.weight.size()		)		)
															self.init_mask()



			def 							__lowercase				( self       :							Optional[Any]		)       ->				Union[str, Any]:
									if self.mask_init == "constant":
															init.constant_(self.mask_scores      ,  val=self.mask_scale		)
									elif self.mask_init == "uniform":
															init.uniform_(self.mask_scores      ,  a=-self.mask_scale      ,  b=self.mask_scale		)
									elif self.mask_init == "kaiming":
															init.kaiming_uniform_(self.mask_scores      ,  a=math.sqrt(5		)		)



			def 							__lowercase				( self       :							Dict      ,  _lowercase       :							torch.tensor      ,  _lowercase       :							float		)       ->				Dict:
									# Get the mask
									if self.pruning_method == "topK":
															snake_case      : Any							=   TopKBinarizer.apply(self.mask_scores      ,  _lowercase		)
									elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
															snake_case      : Tuple							=   "sigmoied" in self.pruning_method
															snake_case      : List[Any]							=   ThresholdBinarizer.apply(self.mask_scores      ,  _lowercase      ,  _lowercase		)
									elif self.pruning_method == "magnitude":
															snake_case      : List[str]							=   MagnitudeBinarizer.apply(self.weight      ,  _lowercase		)
									elif self.pruning_method == "l0":
															snake_case						, snake_case						, snake_case      : str							=   -0.1, 1.1, 2 / 3
															if self.training:
																					snake_case      : Optional[Any]							=   torch.zeros_like(self.mask_scores		).uniform_().clamp(0.0001      ,  0.9999		)
																					snake_case      : str							=   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b		)
															else:
																					snake_case      : int							=   torch.sigmoid(self.mask_scores		)
															snake_case      : Tuple							=   s * (r - l) + l
															snake_case      : int							=   s_bar.clamp(min=0.0      ,  max=1.0		)
									# Mask weights with computed mask
									snake_case      : int							=   mask * self.weight
									# Compute output (linear layer) with masked weights
									return nn.functional.linear(_lowercase      ,  _lowercase      ,  self.bias		)








"""simple docstring"""







import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       int    ,		lowerCamelCase_:       bool = False   ):
						"""simple docstring"""






						if device_specific:
												seed += AcceleratorState().process_index
						random.seed(lowerCamelCase_   )
						np.random.seed(lowerCamelCase_   )
						torch.manual_seed(lowerCamelCase_   )
						if is_xpu_available():
												torch.xpu.manual_seed_all(lowerCamelCase_   )
						elif is_npu_available():
												torch.npu.manual_seed_all(lowerCamelCase_   )
						else:
												torch.cuda.manual_seed_all(lowerCamelCase_   )
						# ^^ safe to call this function even if cuda is not available
						if is_tpu_available():
												xm.set_rng_state(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Optional[RNGType] = None    ,		lowerCamelCase_:       Optional[torch.Generator] = None   ):
						"""simple docstring"""






						if rng_type == RNGType.TORCH:
												snake_case      : List[Any]							=   torch.get_rng_state()
						elif rng_type == RNGType.CUDA:
												snake_case      : Union[str, Any]							=   torch.cuda.get_rng_state()
						elif rng_type == RNGType.XLA:
												assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
												snake_case      : Optional[int]							=   torch.tensor(xm.get_rng_state()   )
						elif rng_type == RNGType.NPU:
												assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
												snake_case      : int							=   torch.npu.get_rng_state()
						elif rng_type == RNGType.XPU:
												assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
												snake_case      : int							=   torch.xpu.get_rng_state()
						elif rng_type == RNGType.GENERATOR:
												assert generator is not None, "Need a generator to synchronize its seed."
												snake_case      : List[Any]							=   generator.get_state()

						# Broadcast the rng state from device 0 to other devices
						snake_case      : Dict							=   AcceleratorState()
						if state.distributed_type == DistributedType.TPU:
												snake_case      : List[str]							=   rng_state.to(xm.xla_device()   )
												xm.collective_broadcast([rng_state]   )
												xm.mark_step()
												snake_case      : List[Any]							=   rng_state.cpu()
						elif (
						    state.distributed_type in CUDA_DISTRIBUTED_TYPES
						    or state.distributed_type == DistributedType.MULTI_NPU
						    or state.distributed_type == DistributedType.MULTI_XPU
						):
												snake_case      : str							=   rng_state.to(state.device   )
												torch.distributed.broadcast(lowerCamelCase_    ,		0   )
												snake_case      : Union[str, Any]							=   rng_state.cpu()
						elif state.distributed_type == DistributedType.MULTI_CPU:
												torch.distributed.broadcast(lowerCamelCase_    ,		0   )

						# Set the broadcast rng state
						if rng_type == RNGType.TORCH:
												torch.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.CUDA:
												torch.cuda.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.NPU:
												torch.npu.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.XPU:
												torch.xpu.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.XLA:
												xm.set_rng_state(rng_state.item()   )
						elif rng_type == RNGType.GENERATOR:
												generator.set_state(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       List[Union[str, RNGType]]    ,		lowerCamelCase_:       Optional[torch.Generator] = None   ):
						"""simple docstring"""






						for rng_type in rng_types:
												synchronize_rng_state(RNGType(lowerCamelCase_   )    ,		generator=lowerCamelCase_   )



