



"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


snake_case_         = logging.get_logger(__name__)

snake_case_         = {
    """facebook/convnext-tiny-224""": """https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json""",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}







class    A_    (  SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_       ):



      """simple docstring"""
      __UpperCamelCase	       =							"""convnext"""






      def __init__(      self				:Any     ,						lowercase_				:str=3     ,						lowercase_				:Tuple=4     ,						lowercase_				:Optional[Any]=4     ,						lowercase_				:Any=None     ,						lowercase_				:str=None     ,						lowercase_				:int="gelu"     ,						lowercase_				:int=0.02     ,						lowercase_				:Any=1E-12     ,						lowercase_				:Optional[int]=1E-6     ,						lowercase_				:Tuple=0.0     ,						lowercase_				:Dict=2_24     ,						lowercase_				:Union[str, Any]=None     ,						lowercase_				:Union[str, Any]=None     ,						**lowercase_				:List[Any]     ,						)       ->    Any:
            super().__init__(**lowercase_      )

            UpperCAmelCase    =				num_channels
            UpperCAmelCase    =				patch_size
            UpperCAmelCase    =				num_stages
            UpperCAmelCase    =				[96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
            UpperCAmelCase    =				[3, 3, 9, 3] if depths is None else depths
            UpperCAmelCase    =				hidden_act
            UpperCAmelCase    =				initializer_range
            UpperCAmelCase    =				layer_norm_eps
            UpperCAmelCase    =				layer_scale_init_value
            UpperCAmelCase    =				drop_path_rate
            UpperCAmelCase    =				image_size
            UpperCAmelCase    =				['stem'] + [f"""stage{idx}""" for idx in range(1     ,						len(self.depths      ) + 1      )]
            UpperCAmelCase   ,					UpperCAmelCase    =				get_aligned_output_features_output_indices(
                out_features=lowercase_     ,						out_indices=lowercase_     ,						stage_names=self.stage_names      )







class    A_    (  SCREAMING_SNAKE_CASE_       ):



      """simple docstring"""
      __UpperCamelCase	       =							version.parse("""1.11"""       )






      @property
      def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ]      )






      @property
      def 			UpperCAmelCase__  (      self				:Any      )       ->    float:
            return 1E-5










"""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       ):



						"""simple docstring"""






						def __init__(      self				:List[str]     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:bool = True     ,						lowercase_				:str = "constant"     ,						lowercase_				:float = 0.0     ,						lowercase_				:str = "topK"     ,						)       ->    Union[str, Any]:
												super(lowercase_     ,						self      ).__init__(in_features=lowercase_     ,						out_features=lowercase_     ,						bias=lowercase_      )
												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 			UpperCAmelCase__  (      self				:Optional[Any]      )       ->    Dict:
												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				:Dict     ,						lowercase_				:torch.tensor     ,						lowercase_				:float      )       ->    List[str]:
												# Get the mask
												if self.pruning_method == "topK":
																		UpperCAmelCase    =				TopKBinarizer.apply(self.mask_scores     ,						lowercase_      )
												elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		UpperCAmelCase    =				'sigmoied' in self.pruning_method
																		UpperCAmelCase    =				ThresholdBinarizer.apply(self.mask_scores     ,						lowercase_     ,						lowercase_      )
												elif self.pruning_method == "magnitude":
																		UpperCAmelCase    =				MagnitudeBinarizer.apply(self.weight     ,						lowercase_      )
												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(0.0001     ,						0.9999      )
																								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(lowercase_     ,						lowercase_     ,						self.bias      )





