


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       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__ = True       ,		lowerCAmelCase__ = "constant"       ,		lowerCAmelCase__ = 0.0       ,		lowerCAmelCase__ = "topK"       ,		):





										"""simple docstring"""




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

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






							def  UpperCamelCase__			(	self    ):





										"""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       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										if self.pruning_method == "topK":
													SCREAMING_SNAKE_CASE_						:			Dict       						= TopKBinarizer.apply(self.mask_scores       ,		A__    )
										elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
													SCREAMING_SNAKE_CASE_						:			int       						= 'sigmoied' in self.pruning_method
													SCREAMING_SNAKE_CASE_						:			Dict       						= ThresholdBinarizer.apply(self.mask_scores       ,		A__       ,		A__    )
										elif self.pruning_method == "magnitude":
													SCREAMING_SNAKE_CASE_						:			Any       						= MagnitudeBinarizer.apply(self.weight       ,		A__    )
										elif self.pruning_method == "l0":
													SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[str]       						= -0.1, 1.1, 2 / 3
													if self.training:
																SCREAMING_SNAKE_CASE_						:			str       						= torch.zeros_like(self.mask_scores    ).uniform_().clamp(0.0_001       ,		0.9_999    )
																SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b    )
													else:
																SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= torch.sigmoid(self.mask_scores    )
													SCREAMING_SNAKE_CASE_						:			Tuple       						= s * (r - l) + l
													SCREAMING_SNAKE_CASE_						:			Tuple       						= s_bar.clamp(min=0.0       ,		max=1.0    )
										# Mask weights with computed mask
										SCREAMING_SNAKE_CASE_						:			int       						= mask * self.weight
										# Compute output (linear layer) with masked weights
										return nn.functional.linear(A__       ,		A__       ,		self.bias    )







'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							A__				,							A__					)			->	Union[str, Any]:
          super().__init__()
          self.register_modules(unet=A__				,							scheduler=A__					)






       @torch.no_grad()
       def __call__(							self				,							A__ = 1				,							A__ = None				,							A__ = 10_00				,							A__ = "pil"				,							A__ = True				,							)			->	Union[ImagePipelineOutput, Tuple]:
          # Sample gaussian noise to begin loop
          if isinstance(self.unet.config.sample_size				,							A__					):
             _SCREAMING_SNAKE_CASE          = (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size,
                 self.unet.config.sample_size,
             )
          else:
             _SCREAMING_SNAKE_CASE          = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

          if self.device.type == "mps":
             # randn does not work reproducibly on mps
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__					)
             _SCREAMING_SNAKE_CASE          = image.to(self.device					)
          else:
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__				,							device=self.device					)

          # set step values
          self.scheduler.set_timesteps(A__					)

          for t in self.progress_bar(self.scheduler.timesteps					):
             # 1. predict noise model_output
             _SCREAMING_SNAKE_CASE          = self.unet(A__				,							A__					).sample

             # 2. compute previous image: x_t -> x_t-1
             _SCREAMING_SNAKE_CASE          = self.scheduler.step(A__				,							A__				,							A__				,							generator=A__					).prev_sample

          _SCREAMING_SNAKE_CASE          = (image / 2 + 0.5).clamp(0				,							1					)
          _SCREAMING_SNAKE_CASE          = image.cpu().permute(0				,							2				,							3				,							1					).numpy()
          if output_type == "pil":
             _SCREAMING_SNAKE_CASE          = self.numpy_to_pil(A__					)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A__					)






