



import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class 	__snake_case (				nn.Linear):







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_					,					a_ = True					,					a_ = "constant"					,					a_ = 0.0					,					a_ = "topK"					,					):
											super(a_					,					self						).__init__(in_features=a_					,					out_features=a_					,					bias=a_						)
											assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
											a__			 =						pruning_method

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


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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Tuple             =   """regnet"""
							UpperCamelCase__			: int             =   ["""x""", """y"""]


							def __init__(							self					,					a_=3					,					a_=32					,					a_=[128, 192, 512, 1_088]					,					a_=[2, 6, 12, 2]					,					a_=64					,					a_="y"					,					a_="relu"					,					**a_					,					):
											super().__init__(**a_						)
											if layer_type not in self.layer_types:
															raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types						)}'''						)
											a__			 =						num_channels
											a__			 =						embedding_size
											a__			 =						hidden_sizes
											a__			 =						depths
											a__			 =						groups_width
											a__			 =						layer_type
											a__			 =						hidden_act
											# always downsample in the first stage
											a__			 =						True

