






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Optional[Any]     ,			__lowercase				:     str      )  -> Any:





     """simple docstring"""

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

     __A             =		pt_attn_layer.out_proj.weight
     __A             =		pt_attn_layer.out_proj.bias

     __A             =		q_proj
     __A             =		q_proj_bias

     __A             =		k_proj
     __A             =		k_proj_bias

     __A             =		v_proj
     __A             =		v_proj_bias

     __A             =		out_proj_weights
     __A             =		out_proj_bias



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Union[str, Any]     ,			__lowercase				:     Optional[int]      )  -> str:





     """simple docstring"""

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



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Optional[Any]     ,			__lowercase				:     Dict      )  -> Union[str, Any]:





     """simple docstring"""

     __A             =		pt_linear.weight
     __A             =		pt_linear.bias



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Any     ,			__lowercase				:     Optional[Any]      )  -> 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      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int     ,			__lowercase				:     Tuple      )  -> int:





     """simple docstring"""

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



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Optional[Any]     ,			__lowercase				:     str      )  -> List[Any]:





     """simple docstring"""

     __A             =		pt_model.token_embedding.weight
     __A             =		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      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Dict     ,			__lowercase				:     int      )  -> List[str]:





     """simple docstring"""

     __A             =		pt_model.text_projection.data.T

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



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Tuple     ,			__lowercase				:     str      )  -> str:





     """simple docstring"""

     __A             =		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
     __A             =		pt_model.visual.conva.weight.data
     __A             =		pt_model.visual.class_embedding
     __A             =		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      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Union[str, Any]     ,			__lowercase				:     List[str]     ,			__lowercase				:     Tuple=None      )  -> int:





     """simple docstring"""

     if config_path is not None:
          __A             =		CLIPConfig.from_pretrained(__lowercase      )
     else:
          __A             =		CLIPConfig(projection_dim=5_1_2     ,			text_config={}     ,			vision_config={}      )

     __A             =		CLIPModel(__lowercase      ).eval()

     __A      ,		__A             =		load(__lowercase     ,			device="""cpu"""     ,			jit=__lowercase      )
     __A             =		pt_model.eval()

     copy_text_model_and_projection(__lowercase     ,			__lowercase      )
     copy_vison_model_and_projection(__lowercase     ,			__lowercase      )
     __A             =		pt_model.logit_scale

     __A             =		torch.arange(0     ,			7_7      ).unsqueeze(0      )
     __A             =		torch.randn(1     ,			3     ,			2_2_4     ,			2_2_4      )

     __A             =		hf_model(input_ids=__lowercase     ,			pixel_values=__lowercase     ,			return_dict=__lowercase      )
     __A             =		hf_outputs.logits_per_image
     __A             =		hf_outputs.logits_per_text
     __A      ,		__A             =		pt_model(__lowercase     ,			__lowercase      )

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

     hf_model.save_pretrained(__lowercase      )


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

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












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     list[int]      )  -> list[int]:





    """simple docstring"""

    if len(__lowercase      ) > 1:
        __A             =		len(__lowercase      ) // 2  # Finds the middle of the array
        __A             =		arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        __A             =		arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        __A             =		len(__lowercase      )
        __A             =		len(__lowercase      )
        merge(__lowercase      )  # Starts sorting the left.
        merge(__lowercase      )  # Starts sorting the right
        __A             =		0  # Left Counter
        __A             =		0  # Right Counter
        __A             =		0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                __A             =		left_array[left_index]
                left_index += 1
            else:
                __A             =		right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            __A             =		left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            __A             =		right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
  import doctest

  doctest.testmod()




