





import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class      A_	(nn.Linear      ):
	def __init__(   self							, _A							, _A							, _A = True							, _A = "constant"							, _A = 0.0							, _A = "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"]
				UpperCAmelCase       		=    pruning_method

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






				'''simple docstring'''







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










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__A    :   Tuple          =       {
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   List[str]          =       [
				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "MobileViTV2ForImageClassification",
				    "MobileViTV2ForSemanticSegmentation",
				    "MobileViTV2Model",
				    "MobileViTV2PreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				__A    :   Tuple          =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



