




'''simple docstring'''


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       :  Union[str, Any]							,			lowerCAmelCase_       :  int							,			lowerCAmelCase_       :  int							,			lowerCAmelCase_       :  bool = True							,			lowerCAmelCase_       :  str = "constant"							,			lowerCAmelCase_       :  float = 0.0							,			lowerCAmelCase_       :  str = "topK"							,			)			->							Tuple:


														"""simple docstring"""


														super(lowerCAmelCase_							,			self					).__init__(in_features=lowerCAmelCase_							,			out_features=lowerCAmelCase_							,			bias=lowerCAmelCase_					)
														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    __lowerCAmelCase			(  self       :  Union[str, Any]					)			->							Dict:


														"""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    __lowerCAmelCase			(  self       :  Any							,			lowerCAmelCase_       :  torch.tensor							,			lowerCAmelCase_       :  float					)			->							int:


														"""simple docstring"""


														if self.pruning_method == "topK":
																					_a	     =				TopKBinarizer.apply(self.mask_scores							,			lowerCAmelCase_					)
														elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																					_a	     =				'''sigmoied''' in self.pruning_method
																					_a	     =				ThresholdBinarizer.apply(self.mask_scores							,			lowerCAmelCase_							,			lowerCAmelCase_					)
														elif self.pruning_method == "magnitude":
																					_a	     =				MagnitudeBinarizer.apply(self.weight							,			lowerCAmelCase_					)
														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_0_0_1							,			0.9_9_9_9					)
																												_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(lowerCAmelCase_							,			lowerCAmelCase_							,			self.bias					)







'''simple docstring'''


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case      :      Tuple   							=		{
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case      :      Tuple   							=		[
				    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ConvNextV2ForImageClassification',
				    'ConvNextV2Model',
				    'ConvNextV2PreTrainedModel',
				    'ConvNextV2Backbone',
				]


if TYPE_CHECKING:
				from .configuration_convnextva import (
				    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ConvNextVaConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convnextva import (
								    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvNextVaBackbone,
								    ConvNextVaForImageClassification,
								    ConvNextVaModel,
								    ConvNextVaPreTrainedModel,
								)

else:
				import sys

				_snake_case      :      Any   							=		_LazyModule(__name__, globals()['__file__'], _import_structure)

