import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			str     	=pt_attn_layer.in_proj_weight.chunk(3		,       dim=0    )
      UpperCAmelCase     :			Optional[int]     	=pt_attn_layer.in_proj_bias.chunk(3		,       dim=0    )

      UpperCAmelCase     :			Dict     	=pt_attn_layer.out_proj.weight
      UpperCAmelCase     :			Tuple     	=pt_attn_layer.out_proj.bias

      UpperCAmelCase     :			str     	=q_proj
      UpperCAmelCase     :			List[str]     	=q_proj_bias

      UpperCAmelCase     :			List[Any]     	=k_proj
      UpperCAmelCase     :			str     	=k_proj_bias

      UpperCAmelCase     :			str     	=v_proj
      UpperCAmelCase     :			List[str]     	=v_proj_bias

      UpperCAmelCase     :			Optional[Any]     	=out_proj_weights
      UpperCAmelCase     :			List[Any]     	=out_proj_bias





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	int:






      '''simple docstring'''





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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	str:






      '''simple docstring'''





      UpperCAmelCase     :			Optional[Any]     	=pt_linear.weight
      UpperCAmelCase     :			Union[str, Any]     	=pt_linear.bias





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	str:






      '''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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	Tuple:






      '''simple docstring'''





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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	Any:






      '''simple docstring'''





      UpperCAmelCase     :			Optional[int]     	=pt_model.token_embedding.weight
      UpperCAmelCase     :			List[Any]     	=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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	Dict:






      '''simple docstring'''





      UpperCAmelCase     :			Union[str, Any]     	=pt_model.text_projection.data.T

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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	List[str]:






      '''simple docstring'''





      UpperCAmelCase     :			Tuple     	=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
      UpperCAmelCase     :			Optional[int]     	=pt_model.visual.conva.weight.data
      UpperCAmelCase     :			Dict     	=pt_model.visual.class_embedding
      UpperCAmelCase     :			int     	=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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None    )->	Tuple:






      '''simple docstring'''





      if config_path is not None:
            UpperCAmelCase     :			int     	=CLIPConfig.from_pretrained(a_    )
      else:
            UpperCAmelCase     :			Tuple     	=CLIPConfig(projection_dim=5_12		,       text_config={}		,       vision_config={}    )

      UpperCAmelCase     :			int     	=CLIPModel(a_    ).eval()

      UpperCAmelCase     :			List[Any]     	=load(a_		,       device='''cpu'''		,       jit=a_    )
      UpperCAmelCase     :			str     	=pt_model.eval()

      copy_text_model_and_projection(a_		,       a_    )
      copy_vison_model_and_projection(a_		,       a_    )
      UpperCAmelCase     :			Optional[Any]     	=pt_model.logit_scale

      UpperCAmelCase     :			Optional[Any]     	=torch.arange(0		,       77    ).unsqueeze(0    )
      UpperCAmelCase     :			int     	=torch.randn(1		,       3		,       2_24		,       2_24    )

      UpperCAmelCase     :			Optional[int]     	=hf_model(input_ids=a_		,       pixel_values=a_		,       return_dict=a_    )
      UpperCAmelCase     :			List[str]     	=hf_outputs.logits_per_image
      UpperCAmelCase     :			int     	=hf_outputs.logits_per_text
      UpperCAmelCase     :			Optional[Any]     	=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__":
     __snake_case         =     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''')
     __snake_case         =     parser.parse_args()

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








from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_			:Union[str, Any]	    =		load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
A_    ,A_    ,A_    ,A_			:Tuple	    =		train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

A_			:Any	    =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_			:Optional[Any]	    =		[[1, 2, 1, 4], [2, 3, 4, 5]]

A_			:Union[str, Any]	    =		knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)

