



'''simple docstring'''




import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






class 						_UpperCamelCase  (							nn.Linear   ):


			'''simple docstring'''

			def __init__(							self					:			List[str]							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			bool = True							,					lowerCAmelCase__					:			str = "constant"							,					lowerCAmelCase__					:			float = 0.0							,					lowerCAmelCase__					:			str = "topK"							,					):





					"""simple docstring"""






					super(lowerCAmelCase__							,					self						).__init__(in_features=lowerCAmelCase__							,					out_features=lowerCAmelCase__							,					bias=lowerCAmelCase__						)
					assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		pruning_method

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

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					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 UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			torch.tensor							,					lowerCAmelCase__					:			float						):





					"""simple docstring"""






					if self.pruning_method == "topK":
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		TopKBinarizer.apply(self.mask_scores							,					lowerCAmelCase__						)
					elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
							__SCREAMING_SNAKE_CASE	:		int   	=		"""sigmoied""" in self.pruning_method
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		ThresholdBinarizer.apply(self.mask_scores							,					lowerCAmelCase__							,					lowerCAmelCase__						)
					elif self.pruning_method == "magnitude":
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		MagnitudeBinarizer.apply(self.weight							,					lowerCAmelCase__						)
					elif self.pruning_method == "l0":
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		-0.1, 1.1, 2 / 3
							if self.training:
									__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.zeros_like(self.mask_scores						).uniform_().clamp(0.00_01							,					0.99_99						)
									__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b						)
							else:
									__SCREAMING_SNAKE_CASE	:		Tuple   	=		torch.sigmoid(self.mask_scores						)
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		s * (r - l) + l
							__SCREAMING_SNAKE_CASE	:		Dict   	=		s_bar.clamp(min=0.0							,					max=1.0						)
					# Mask weights with computed mask
					__SCREAMING_SNAKE_CASE	:		str   	=		mask * self.weight
					# Compute output (linear layer) with masked weights
					return nn.functional.linear(lowerCAmelCase__							,					lowerCAmelCase__							,					self.bias						)




'''simple docstring'''




import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		(VQDiffusionScheduler,)

			def UpperCamelCase__ (							self					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		{
					    """num_vec_classes""": 4_0_9_7,
					    """num_train_timesteps""": 1_0_0,
					}

					config.update(**lowerCAmelCase__						)
					return config

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		4
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		8
					__SCREAMING_SNAKE_CASE	:		int   	=		8

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.randint(0							,					lowerCAmelCase__							,					(batch_size, height * width)						)

					return sample

			@property
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					assert False

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					def model(lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					*lowerCAmelCase__					:			Optional[int]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sample.shape
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
							__SCREAMING_SNAKE_CASE	:		str   	=		F.log_softmax(logits.double()							,					dim=1						).float()
							return return_value

					return model

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
							self.check_over_configs(num_vec_classes=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					for t in [0, 5_0, 9_9]:
							self.check_over_forward(time_step=lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass