



import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel






def        a( A			:       List[str]					,	A			:       Tuple			)						->			int:
					"""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        a( A			:       List[str]					,	A			:       Dict			)						->			int:
					"""simple docstring"""





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






def        a( A			:       List[str]					,	A			:       str			)						->			List[str]:
					"""simple docstring"""





					a   	=  pt_linear.weight
					a   	=  pt_linear.bias






def        a( A			:       List[Any]					,	A			:       int			)						->			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        a( A			:       Union[str, Any]					,	A			:       List[Any]			)						->			Union[str, Any]:
					"""simple docstring"""





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






def        a( A			:       Union[str, Any]					,	A			:       Optional[Any]			)						->			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        a( A			:       List[Any]					,	A			:       Optional[Any]			)						->			int:
					"""simple docstring"""





					a   	=  pt_model.text_projection.data.T

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






def        a( A			:       Optional[int]					,	A			:       Optional[Any]			)						->			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        a( A			:       Tuple					,	A			:       List[str]					,	A			:       Any=None			)						->			List[Any]:
					"""simple docstring"""





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

					a   	=  CLIPModel(A			).eval()

					a	,							a   	=  load(A					,	device="cpu"					,	jit=A			)
					a   	=  pt_model.eval()

					copy_text_model_and_projection(A					,	A			)
					copy_vison_model_and_projection(A					,	A			)
					a   	=  pt_model.logit_scale

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

					a   	=  hf_model(input_ids=A					,	pixel_values=A					,	return_dict=A			)
					a   	=  hf_outputs.logits_per_image
					a   	=  hf_outputs.logits_per_text
					a	,							a   	=  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:					int      =						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:					Optional[int]      =						parser.parse_args()

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





from .pipeline_kandinskya_a import KandinskyVaaPipeline
from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline
