'''simple docstring'''

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def 				UpperCamelCase__      (      a__      ,   a__   ):


						'''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 				UpperCamelCase__      (      a__      ,   a__   ):


						'''simple docstring'''






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



def 				UpperCamelCase__      (      a__      ,   a__   ):


						'''simple docstring'''






						_lowerCAmelCase					    =pt_linear.weight
						_lowerCAmelCase					    =pt_linear.bias



def 				UpperCamelCase__      (      a__      ,   a__   ):


						'''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 				UpperCamelCase__      (      a__      ,   a__   ):


						'''simple docstring'''






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



def 				UpperCamelCase__      (      a__      ,   a__   ):


						'''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 				UpperCamelCase__      (      a__      ,   a__   ):


						'''simple docstring'''






						_lowerCAmelCase					    =pt_model.text_projection.data.T

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



def 				UpperCamelCase__      (      a__      ,   a__   ):


						'''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 				UpperCamelCase__      (      a__      ,   a__      ,   a__=None   ):


						'''simple docstring'''






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

						_lowerCAmelCase					    =CLIPModel(a__   ).eval()

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

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

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

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






'''simple docstring'''

from .prime_check import Test

Test()





