'''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 					UpperCamelCase_						( snake_case_	:	int						,		snake_case_	:	bool = False				) ->					Optional[Any]:






 '''simple docstring'''

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







def 					UpperCamelCase_						( snake_case_	:	Optional[RNGType] = None						,		snake_case_	:	Optional[torch.Generator] = None				) ->					List[Any]:






 '''simple docstring'''

 if rng_type == RNGType.TORCH:
  __lowerCAmelCase    							=						torch.get_rng_state()
 elif rng_type == RNGType.CUDA:
  __lowerCAmelCase    							=						torch.cuda.get_rng_state()
 elif rng_type == RNGType.XLA:
  assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  __lowerCAmelCase    							=						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."
  __lowerCAmelCase    							=						torch.npu.get_rng_state()
 elif rng_type == RNGType.XPU:
  assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  __lowerCAmelCase    							=						torch.xpu.get_rng_state()
 elif rng_type == RNGType.GENERATOR:
  assert generator is not None, "Need a generator to synchronize its seed."
  __lowerCAmelCase    							=						generator.get_state()

 # Broadcast the rng state from device 0 to other devices
 __lowerCAmelCase    							=						AcceleratorState()
 if state.distributed_type == DistributedType.TPU:
  __lowerCAmelCase    							=						rng_state.to(xm.xla_device()				)
  xm.collective_broadcast([rng_state]				)
  xm.mark_step()
  __lowerCAmelCase    							=						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
 ):
  __lowerCAmelCase    							=						rng_state.to(state.device				)
  torch.distributed.broadcast(snake_case_						,		0				)
  __lowerCAmelCase    							=						rng_state.cpu()
 elif state.distributed_type == DistributedType.MULTI_CPU:
  torch.distributed.broadcast(snake_case_						,		0				)

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







def 					UpperCamelCase_						( snake_case_	:	List[Union[str, RNGType]]						,		snake_case_	:	Optional[torch.Generator] = None				) ->					Any:






 '''simple docstring'''

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







'''simple docstring'''

import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class     _lowercase					(    nn.Linear      ):


 '''simple docstring'''






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

  if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
   __lowerCAmelCase    							=						mask_scale
   __lowerCAmelCase    							=						mask_init
   __lowerCAmelCase    							=						nn.Parameter(torch.empty(self.weight.size()							)							)
   self.init_mask()
 def a (				self       :     Union[str, Any]							)  ->   int:
  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 a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     torch.tensor				,	SCREAMING_SNAKE_CASE__       :     float							)  ->   Tuple:
  # Get the mask
  if self.pruning_method == "topK":
   __lowerCAmelCase    							=						TopKBinarizer.apply(self.mask_scores				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
   __lowerCAmelCase    							=						"""sigmoied""" in self.pruning_method
   __lowerCAmelCase    							=						ThresholdBinarizer.apply(self.mask_scores				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method == "magnitude":
   __lowerCAmelCase    							=						MagnitudeBinarizer.apply(self.weight				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method == "l0":
   __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						-0.1, 1.1, 2 / 3
   if self.training:
    __lowerCAmelCase    							=						torch.zeros_like(self.mask_scores							).uniform_().clamp(0.0_0_0_1				,	0.9_9_9_9							)
    __lowerCAmelCase    							=						torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b							)
   else:
    __lowerCAmelCase    							=						torch.sigmoid(self.mask_scores							)
   __lowerCAmelCase    							=						s * (r - l) + l
   __lowerCAmelCase    							=						s_bar.clamp(min=0.0				,	max=1.0							)
  # Mask weights with computed mask
  __lowerCAmelCase    							=						mask * self.weight
  # Compute output (linear layer) with masked weights
  return nn.functional.linear(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	self.bias							)






