
import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




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

  _lowerCAmelCase						=		pt_attn_layer.out_proj.weight
  _lowerCAmelCase						=		pt_attn_layer.out_proj.bias

  _lowerCAmelCase						=		q_proj
  _lowerCAmelCase						=		q_proj_bias

  _lowerCAmelCase						=		k_proj
  _lowerCAmelCase						=		k_proj_bias

  _lowerCAmelCase						=		v_proj
  _lowerCAmelCase						=		v_proj_bias

  _lowerCAmelCase						=		out_proj_weights
  _lowerCAmelCase						=		out_proj_bias



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




  copy_linear(hf_mlp.fca		,				pt_mlp.c_fc    )
  copy_linear(hf_mlp.fca		,				pt_mlp.c_proj    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		pt_linear.weight
  _lowerCAmelCase						=		pt_linear.bias



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  Union[str, 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  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  List[Any]    ):






  """simple docstring"""




  for hf_layer, pt_layer in zip(lowerCamelCase_		,				lowerCamelCase_    ):
    copy_layer(lowerCamelCase_		,				lowerCamelCase_    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




  _lowerCAmelCase						=		pt_model.token_embedding.weight
  _lowerCAmelCase						=		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  (					__SCREAMING_SNAKE_CASE					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model		,				lowerCamelCase_    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  Any    ):






  """simple docstring"""




  _lowerCAmelCase						=		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
  _lowerCAmelCase						=		pt_model.visual.conva.weight.data
  _lowerCAmelCase						=		pt_model.visual.class_embedding
  _lowerCAmelCase						=		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  (					__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  int=None    ):






  """simple docstring"""




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

  _lowerCAmelCase						=		CLIPModel(lowerCamelCase_    ).eval()

  _lowerCAmelCase	,       _lowerCAmelCase						=		load(lowerCamelCase_		,				device='cpu'		,				jit=lowerCamelCase_    )
  _lowerCAmelCase						=		pt_model.eval()

  copy_text_model_and_projection(lowerCamelCase_		,				lowerCamelCase_    )
  copy_vison_model_and_projection(lowerCamelCase_		,				lowerCamelCase_    )
  _lowerCAmelCase						=		pt_model.logit_scale

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

  _lowerCAmelCase						=		hf_model(input_ids=lowerCamelCase_		,				pixel_values=lowerCamelCase_		,				return_dict=lowerCamelCase_    )
  _lowerCAmelCase						=		hf_outputs.logits_per_image
  _lowerCAmelCase						=		hf_outputs.logits_per_text
  _lowerCAmelCase	,       _lowerCAmelCase						=		pt_model(lowerCamelCase_		,				lowerCamelCase_    )

  assert torch.allclose(lowerCamelCase_		,				lowerCamelCase_		,				atol=1E-3    )
  assert torch.allclose(lowerCamelCase_		,				lowerCamelCase_		,				atol=1E-3    )

  hf_model.save_pretrained(lowerCamelCase_    )


if __name__ == "__main__":
       _UpperCamelCase:					Optional[Any]     =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')
       _UpperCamelCase:					List[Any]     =parser.parse_args()

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




"""simple docstring"""






def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					str(lowerCamelCase_	).strip()
				if not oct_string:
								raise ValueError("Empty string was passed to the function"	)
				SCREAMING_SNAKE_CASE__  				=					oct_string[0] == "-"
				if is_negative:
								SCREAMING_SNAKE_CASE__  				=					oct_string[1:]
				if not oct_string.isdigit() or not all(0 <= int(lowerCamelCase_	) <= 7 for char in oct_string	):
								raise ValueError("Non-octal value was passed to the function"	)
				SCREAMING_SNAKE_CASE__  				=					0
				for char in oct_string:
								SCREAMING_SNAKE_CASE__  				=					8 * decimal_number + int(lowerCamelCase_	)
				if is_negative:
								SCREAMING_SNAKE_CASE__  				=					-decimal_number
				return decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()
