

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel
def 					a				(      A__     :       Tuple				,       A__     :       Dict					)   ->				Any:





      """simple docstring"""



      _lowercase	,	_lowercase	,	_lowercase     	=pt_attn_layer.in_proj_weight.chunk(3				,       dim=0					)
      _lowercase	,	_lowercase	,	_lowercase     	=pt_attn_layer.in_proj_bias.chunk(3				,       dim=0					)

      _lowercase     	=pt_attn_layer.out_proj.weight
      _lowercase     	=pt_attn_layer.out_proj.bias

      _lowercase     	=q_proj
      _lowercase     	=q_proj_bias

      _lowercase     	=k_proj
      _lowercase     	=k_proj_bias

      _lowercase     	=v_proj
      _lowercase     	=v_proj_bias

      _lowercase     	=out_proj_weights
      _lowercase     	=out_proj_bias
def 					a				(      A__     :       List[Any]				,       A__     :       Any					)   ->				int:





      """simple docstring"""



      copy_linear(hf_mlp.fca				,       pt_mlp.c_fc					)
      copy_linear(hf_mlp.fca				,       pt_mlp.c_proj					)
def 					a				(      A__     :       Optional[int]				,       A__     :       Optional[Any]					)   ->				str:





      """simple docstring"""



      _lowercase     	=pt_linear.weight
      _lowercase     	=pt_linear.bias
def 					a				(      A__     :       Dict				,       A__     :       int					)   ->				Optional[Any]:





      """simple docstring"""



      copy_linear(hf_layer.layer_norma				,       pt_layer.ln_a					)
      copy_linear(hf_layer.layer_norma				,       pt_layer.ln_a					)

      # copy MLP
      copy_mlp(hf_layer.mlp				,       pt_layer.mlp					)

      # copy attn
      copy_attn_layer(hf_layer.self_attn				,       pt_layer.attn					)
def 					a				(      A__     :       Any				,       A__     :       Tuple					)   ->				Optional[int]:





      """simple docstring"""



      for hf_layer, pt_layer in zip(A__				,       A__					):
            copy_layer(A__				,       A__					)
def 					a				(      A__     :       str				,       A__     :       Optional[int]					)   ->				Any:





      """simple docstring"""



      _lowercase     	=pt_model.token_embedding.weight
      _lowercase     	=pt_model.positional_embedding

      # copy layer norm
      copy_linear(hf_encoder.final_layer_norm				,       pt_model.ln_final					)

      # copy hidden layers
      copy_layers(hf_encoder.encoder.layers				,       pt_model.transformer.resblocks					)
def 					a				(      A__     :       Union[str, Any]				,       A__     :       Dict					)   ->				int:





      """simple docstring"""



      _lowercase     	=pt_model.text_projection.data.T

      # copy text encoder
      copy_encoder(hf_model.text_model				,       A__					)
def 					a				(      A__     :       Optional[int]				,       A__     :       Optional[int]					)   ->				Any:





      """simple docstring"""



      _lowercase     	=pt_model.visual.proj.data.T

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

      # copy embeds
      _lowercase     	=pt_model.visual.conva.weight.data
      _lowercase     	=pt_model.visual.class_embedding
      _lowercase     	=pt_model.visual.positional_embedding.data

      # copy encoder
      copy_layers(hf_model.vision_model.encoder.layers				,       pt_model.visual.transformer.resblocks					)
@torch.no_grad()
def 					a				(      A__     :       str				,       A__     :       int				,       A__     :       List[Any]=None					)   ->				int:





      """simple docstring"""



      if config_path is not None:
            _lowercase     	=CLIPConfig.from_pretrained(A__					)
      else:
            _lowercase     	=CLIPConfig(projection_dim=512				,       text_config={}				,       vision_config={}					)

      _lowercase     	=CLIPModel(A__					).eval()

      _lowercase	,	_lowercase     	=load(A__				,       device='cpu'				,       jit=A__					)
      _lowercase     	=pt_model.eval()

      copy_text_model_and_projection(A__				,       A__					)
      copy_vison_model_and_projection(A__				,       A__					)
      _lowercase     	=pt_model.logit_scale

      _lowercase     	=torch.arange(0				,       77					).unsqueeze(0					)
      _lowercase     	=torch.randn(1				,       3				,       224				,       224					)

      _lowercase     	=hf_model(input_ids=A__				,       pixel_values=A__				,       return_dict=A__					)
      _lowercase     	=hf_outputs.logits_per_image
      _lowercase     	=hf_outputs.logits_per_text
      _lowercase	,	_lowercase     	=pt_model(A__				,       A__					)

      assert torch.allclose(A__				,       A__				,       atol=1e-3					)
      assert torch.allclose(A__				,       A__				,       atol=1e-3					)

      hf_model.save_pretrained(A__					)


if __name__ == "__main__":
  lowercase_     =       argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  lowercase_     =       parser.parse_args()

  convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use ViTImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


