"""simple docstring"""




import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
class      UpperCamelCase__(      nn.Linear			):





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = True		,__UpperCAmelCase = "constant"		,__UpperCAmelCase = 0.0		,__UpperCAmelCase = "topK"		,)       ->					Optional[int]:
													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 					snake_case__						(							self  )       ->					Optional[Any]:






													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 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Union[str, Any]:






													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__						  =				-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(a__		,a__		,self.bias  )









import copy

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


a					:				Optional[Any]       =      logging.get_logger(__name__)

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hybrid-clip"""
       _UpperCamelCase     		=   True

       def __init__(      self						,    a__=5_12						,    **a__       ):






        '''simple docstring'''






        super().__init__(**a__       )

        if "text_config" not in kwargs:
         raise ValueError('''`text_config` can not be `None`.'''       )

        if "vision_config" not in kwargs:
         raise ValueError('''`vision_config` can not be `None`.'''       )

        snake_case :			int							=   kwargs.pop('''text_config'''       )
        snake_case :			Optional[int]							=   kwargs.pop('''vision_config'''       )

        snake_case :			Optional[Any]							=   text_config.pop('''model_type'''       )
        snake_case :			str							=   vision_config.pop('''model_type'''       )

        from transformers import AutoConfig

        snake_case :			List[Any]							=   AutoConfig.for_model(a__						,    **a__       )

        if vision_model_type == "clip":
         snake_case :			Dict							=   AutoConfig.for_model(a__						,    **a__       ).vision_config
        elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         snake_case :			str							=   CLIPVisionConfig(**a__       )
        else:
         snake_case :			Any							=   AutoConfig.for_model(a__						,    **a__       )

        snake_case :			Optional[Any]							=   projection_dim
        snake_case :			int							=   1.0

       @classmethod
       def 					UpperCAmelCase					(      cls						,    a__						,    a__						,    **a__       ):






        '''simple docstring'''






        return cls(text_config=text_config.to_dict()						,    vision_config=vision_config.to_dict()						,    **a__       )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   copy.deepcopy(self.__dict__       )
        snake_case :			Dict							=   self.text_config.to_dict()
        snake_case :			int							=   self.vision_config.to_dict()
        snake_case :			List[Any]							=   self.__class__.model_type
        return output






