import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class  __lowerCAmelCase	(							nn.Linear    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[int]							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		str = "constant"							,      lowerCamelCase							:		float = 0.0							,      lowerCamelCase							:		str = "topK"							,      )			->				Optional[int]:
        super(lowerCamelCase							,      self	).__init__(in_features=lowerCamelCase							,      out_features=lowerCamelCase							,      bias=lowerCamelCase	)
        assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
        _UpperCamelCase  :							Optional[Any]	    =	pruning_method

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

 def        __snake_case       (      self							:		Tuple	)			->				Optional[int]:
        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							:		Union[str, Any]							,      lowerCamelCase							:		torch.tensor							,      lowerCamelCase							:		float	)			->				List[Any]:
        # Get the mask
        if self.pruning_method == "topK":
               _UpperCamelCase  :							List[str]	    =	TopKBinarizer.apply(self.mask_scores							,      lowerCamelCase	)
        elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
               _UpperCamelCase  :							Optional[Any]	    =	'sigmoied' in self.pruning_method
               _UpperCamelCase  :							Optional[Any]	    =	ThresholdBinarizer.apply(self.mask_scores							,      lowerCamelCase							,      lowerCamelCase	)
        elif self.pruning_method == "magnitude":
               _UpperCamelCase  :							Union[str, Any]	    =	MagnitudeBinarizer.apply(self.weight							,      lowerCamelCase	)
        elif self.pruning_method == "l0":
               _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	-0.1, 1.1, 2 / 3
               if self.training:
                      _UpperCamelCase  :							List[str]	    =	torch.zeros_like(self.mask_scores	).uniform_().clamp(0.0_0_0_1							,      0.9_9_9_9	)
                      _UpperCamelCase  :							Optional[Any]	    =	torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b	)
               else:
                      _UpperCamelCase  :							List[Any]	    =	torch.sigmoid(self.mask_scores	)
               _UpperCamelCase  :							List[Any]	    =	s * (r - l) + l
               _UpperCamelCase  :							Tuple	    =	s_bar.clamp(min=0.0							,      max=1.0	)
        # Mask weights with computed mask
        _UpperCamelCase  :							Optional[int]	    =	mask * self.weight
        # Compute output (linear layer) with masked weights
        return nn.functional.linear(lowerCamelCase							,      lowerCamelCase							,      self.bias	)




import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						Dict		,    lowercase_:						List[Any]					):
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							List[Any]	    =	pt_weights[F"{prefix}.in_proj_weight"].chunk(3		,    dim=0					)
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Dict	    =	pt_weights[F"{prefix}.in_proj_bias"].chunk(3		,    dim=0					)

       _UpperCamelCase  :							List[str]	    =	pt_weights[F"{prefix}.out_proj.weight"]
       _UpperCamelCase  :							Any	    =	pt_weights[F"{prefix}.out_proj.bias"]

       _UpperCamelCase  :							List[Any]	    =	q_proj
       _UpperCamelCase  :							Union[str, Any]	    =	q_proj_bias

       _UpperCamelCase  :							str	    =	k_proj
       _UpperCamelCase  :							List[str]	    =	k_proj_bias

       _UpperCamelCase  :							Dict	    =	v_proj
       _UpperCamelCase  :							str	    =	v_proj_bias

       _UpperCamelCase  :							List[str]	    =	out_proj_weights
       _UpperCamelCase  :							Any	    =	out_proj_bias


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						List[Any]		,    lowercase_:						Any					):
       copy_linear(hf_mlp.fca		,    lowercase_		,    F"{prefix}.c_fc"					)
       copy_linear(hf_mlp.fca		,    lowercase_		,    F"{prefix}.c_proj"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						List[str]		,    lowercase_:						Any					):
       _UpperCamelCase  :							Dict	    =	pt_weights[F"{prefix}.weight"].data
       _UpperCamelCase  :							List[Any]	    =	pt_weights[F"{prefix}.bias"].data


def SCREAMING_SNAKE_CASE					(  lowercase_:						Any		,    lowercase_:						Optional[int]		,    lowercase_:						Union[str, Any]					):
       # copy layer norms
       copy_linear(hf_layer.layer_norma		,    lowercase_		,    F"{prefix}.ln_1"					)
       copy_linear(hf_layer.layer_norma		,    lowercase_		,    F"{prefix}.ln_2"					)

       # copy MLP
       copy_mlp(hf_layer.mlp		,    lowercase_		,    F"{prefix}.mlp"					)

       # copy attn
       copy_attn_layer(hf_layer.self_attn		,    lowercase_		,    F"{prefix}.attn"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						str		,    lowercase_:						str		,    lowercase_:						int					):
       for layer_id, hf_layer in enumerate(lowercase_					):
              copy_layer(lowercase_		,    lowercase_		,    F"{prefix}.{layer_id}"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Union[str, Any]		,    lowercase_:						List[Any]					):
       # copy projection
       _UpperCamelCase  :							List[str]	    =	pt_weights['text_projection'].data.T

       # copy text encoder
       for name, param in hf_model.text_model.named_parameters():
              _UpperCamelCase  :							Tuple	    =	pt_weights[F"bert.{name}"].data


def SCREAMING_SNAKE_CASE					(  lowercase_:						str		,    lowercase_:						Any					):
       # copy projection
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.proj'].data.T

       # copy layer norms
       copy_linear(hf_model.vision_model.pre_layrnorm		,    lowercase_		,    'visual.ln_pre'					)
       copy_linear(hf_model.vision_model.post_layernorm		,    lowercase_		,    'visual.ln_post'					)

       # copy embeddings
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.conv1.weight'].data
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.class_embedding'].data
       _UpperCamelCase  :							int	    =	pt_weights['visual.positional_embedding'].data

       # copy encoder
       copy_layers(hf_model.vision_model.encoder.layers		,    lowercase_		,    'visual.transformer.resblocks'					)


@torch.no_grad()
def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						Optional[int]		,    lowercase_:						List[Any]=None					):
       assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
       _UpperCamelCase  :							Optional[Any]	    =	ChineseCLIPConfig.from_pretrained(lowercase_					)

       _UpperCamelCase  :							List[Any]	    =	ChineseCLIPModel(lowercase_					).eval()

       _UpperCamelCase  :							List[Any]	    =	torch.load(lowercase_		,    map_location='cpu'					)['state_dict']
       _UpperCamelCase  :							Union[str, Any]	    =	{(name[7:] if name.startswith('module.'					) else name): value for name, value in pt_weights.items()}

       copy_text_model_and_projection(lowercase_		,    lowercase_					)
       copy_vision_model_and_projection(lowercase_		,    lowercase_					)
       _UpperCamelCase  :							List[Any]	    =	pt_weights['logit_scale'].data

       hf_model.save_pretrained(lowercase_					)


if __name__ == "__main__":
 UpperCAmelCase      		=					argparse.ArgumentParser()
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     help='''Path to the output folder storing converted hf PyTorch model.''',
 )
 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, help='''Path to original github format ChineseCLIP checkpoint.'''
 )
 parser.add_argument(
     '''--config_path''', default=None, required=True, type=str, help='''Path to hf config.json of model to convert.'''
 )
 UpperCAmelCase      		=					parser.parse_args()

 convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
 print('''The conversion is finished!''')



