'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	Union[str, Any]				     = (LMSDiscreteScheduler,)
 _SCREAMING_SNAKE_CASE					:	Any				     = 10
 def a (				self       :     Any				,	**SCREAMING_SNAKE_CASE__       :     List[str]							)  ->   str:
  __lowerCAmelCase    							=						{
      """num_train_timesteps""": 11_00,
      """beta_start""": 0.0_0_0_1,
      """beta_end""": 0.0_2,
      """beta_schedule""": """linear""",
  }

  config.update(**SCREAMING_SNAKE_CASE__							)
  return config
 def a (				self       :     Dict							)  ->   Optional[int]:
  for timesteps in [10, 50, 1_00, 10_00]:
   self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Any							)  ->   int:
  for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]				,	[0.0_0_0_2, 0.0_0_2, 0.0_2]							):
   self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__				,	beta_end=SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Union[str, Any]							)  ->   Union[str, Any]:
  for schedule in ["linear", "scaled_linear"]:
   self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__							)
 def a (				self       :     List[Any]							)  ->   Union[str, Any]:
  for prediction_type in ["epsilon", "v_prediction"]:
   self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__							)
 def a (				self       :     str							)  ->   int:
  for t in [0, 5_00, 8_00]:
   self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Any							)  ->   Union[str, Any]:
  __lowerCAmelCase    							=						self.scheduler_classes[0]
  __lowerCAmelCase    							=						self.get_scheduler_config()
  __lowerCAmelCase    							=						scheduler_class(**SCREAMING_SNAKE_CASE__							)

  scheduler.set_timesteps(self.num_inference_steps							)

  __lowerCAmelCase    							=						self.dummy_model()
  __lowerCAmelCase    							=						self.dummy_sample_deter * scheduler.init_noise_sigma

  for i, t in enumerate(scheduler.timesteps							):
   __lowerCAmelCase    							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase    							=						scheduler.step(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						output.prev_sample

  __lowerCAmelCase    							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase    							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

  assert abs(result_sum.item() - 1_0_0_6.3_8_8							) < 1e-2
  assert abs(result_mean.item() - 1.3_1							) < 1e-3
 def a (				self       :     List[Any]							)  ->   int:
  __lowerCAmelCase    							=						self.scheduler_classes[0]
  __lowerCAmelCase    							=						self.get_scheduler_config(prediction_type="""v_prediction"""							)
  __lowerCAmelCase    							=						scheduler_class(**SCREAMING_SNAKE_CASE__							)

  scheduler.set_timesteps(self.num_inference_steps							)

  __lowerCAmelCase    							=						self.dummy_model()
  __lowerCAmelCase    							=						self.dummy_sample_deter * scheduler.init_noise_sigma

  for i, t in enumerate(scheduler.timesteps							):
   __lowerCAmelCase    							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase    							=						scheduler.step(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						output.prev_sample

  __lowerCAmelCase    							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase    							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

  assert abs(result_sum.item() - 0.0_0_1_7							) < 1e-2
  assert abs(result_mean.item() - 2.2_6_7_6e-0_6							) < 1e-3
 def a (				self       :     Optional[Any]							)  ->   Union[str, Any]:
  __lowerCAmelCase    							=						self.scheduler_classes[0]
  __lowerCAmelCase    							=						self.get_scheduler_config()
  __lowerCAmelCase    							=						scheduler_class(**SCREAMING_SNAKE_CASE__							)

  scheduler.set_timesteps(self.num_inference_steps				,	device=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						self.dummy_model()
  __lowerCAmelCase    							=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  __lowerCAmelCase    							=						sample.to(SCREAMING_SNAKE_CASE__							)

  for i, t in enumerate(scheduler.timesteps							):
   __lowerCAmelCase    							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase    							=						scheduler.step(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						output.prev_sample

  __lowerCAmelCase    							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase    							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

  assert abs(result_sum.item() - 1_0_0_6.3_8_8							) < 1e-2
  assert abs(result_mean.item() - 1.3_1							) < 1e-3





 def a (				self       :     List[str]							)  ->   Any:
  __lowerCAmelCase    							=						self.scheduler_classes[0]
  __lowerCAmelCase    							=						self.get_scheduler_config()
  __lowerCAmelCase    							=						scheduler_class(**SCREAMING_SNAKE_CASE__				,	use_karras_sigmas=SCREAMING_SNAKE_CASE__							)

  scheduler.set_timesteps(self.num_inference_steps				,	device=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						self.dummy_model()
  __lowerCAmelCase    							=						self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__							) * scheduler.init_noise_sigma
  __lowerCAmelCase    							=						sample.to(SCREAMING_SNAKE_CASE__							)

  for t in scheduler.timesteps:
   __lowerCAmelCase    							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase    							=						scheduler.step(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						output.prev_sample

  __lowerCAmelCase    							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase    							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

  assert abs(result_sum.item() - 3_8_1_2.9_9_2_7							) < 2e-2
  assert abs(result_mean.item() - 4.9_6_4_8							) < 1e-3







'''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							)






