
import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer





class      _lowerCAmelCase (     nn.Linear	):




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = True    ,    _UpperCamelCase = "constant"    ,    _UpperCamelCase = 0.0    ,    _UpperCamelCase = "topK"    ,    )    ->				List[str]:
							super(_UpperCamelCase    ,    self					).__init__(in_features=_UpperCamelCase    ,    out_features=_UpperCamelCase    ,    bias=_UpperCamelCase					)
							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					)    ->				Tuple:
							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    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				List[Any]:
							# Get the mask
							if self.pruning_method == "topK":
										lowerCAmelCase_      =     TopKBinarizer.apply(self.mask_scores    ,    _UpperCamelCase					)
							elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
										lowerCAmelCase_      =     "sigmoied" in self.pruning_method
										lowerCAmelCase_      =     ThresholdBinarizer.apply(self.mask_scores    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							elif self.pruning_method == "magnitude":
										lowerCAmelCase_      =     MagnitudeBinarizer.apply(self.weight    ,    _UpperCamelCase					)
							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(0.0001    ,    0.9999					)
													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(_UpperCamelCase    ,    _UpperCamelCase    ,    self.bias					)





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest





class      _lowerCAmelCase (     __a	):
				_lowercase      		=(VQDiffusionScheduler,)




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     {
							    "num_vec_classes": 4_097,
							    "num_train_timesteps": 100,
							}

							config.update(**_UpperCamelCase					)
							return config




				def 					__a						( self    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     4
							lowerCAmelCase_      =     8
							lowerCAmelCase_      =     8

							lowerCAmelCase_      =     torch.randint(0    ,    _UpperCamelCase    ,    (batch_size, height * width)					)

							return sample




				@property
				def 					__a						( self					)    ->				List[str]:
							assert False




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							def model(_UpperCamelCase    ,    _UpperCamelCase    ,    *_UpperCamelCase					):
										lowerCAmelCase_     ,  lowerCAmelCase_      =     sample.shape
										lowerCAmelCase_      =     torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)					)
										lowerCAmelCase_      =     F.log_softmax(logits.double()    ,    dim=1					).float()
										return return_value

							return model




				def 					__a						( self					)    ->				str:
							for timesteps in [2, 5, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




				def 					__a						( self					)    ->				Dict:
							for num_vec_classes in [5, 100, 1_000, 4_000]:
										self.check_over_configs(num_vec_classes=_UpperCamelCase					)




				def 					__a						( self					)    ->				Tuple:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=_UpperCamelCase					)




				def 					__a						( self					)    ->				List[str]:
							pass



