





import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_    ,						UpperCAmelCase_    ,						UpperCAmelCase_		    =  pt_weights[f"""{prefix}.in_proj_weight"""].chunk(3					,   dim=0 )
	UpperCAmelCase_    ,						UpperCAmelCase_    ,						UpperCAmelCase_		    =  pt_weights[f"""{prefix}.in_proj_bias"""].chunk(3					,   dim=0 )

	UpperCAmelCase_		    =  pt_weights[f"""{prefix}.out_proj.weight"""]
	UpperCAmelCase_		    =  pt_weights[f"""{prefix}.out_proj.bias"""]

	UpperCAmelCase_		    =  q_proj
	UpperCAmelCase_		    =  q_proj_bias

	UpperCAmelCase_		    =  k_proj
	UpperCAmelCase_		    =  k_proj_bias

	UpperCAmelCase_		    =  v_proj
	UpperCAmelCase_		    =  v_proj_bias

	UpperCAmelCase_		    =  out_proj_weights
	UpperCAmelCase_		    =  out_proj_bias




def  A_(      A					,   A					,   A ):
	copy_linear(hf_mlp.fca					,   lowerCAmelCase__					,   f"""{prefix}.c_fc""" )
	copy_linear(hf_mlp.fca					,   lowerCAmelCase__					,   f"""{prefix}.c_proj""" )




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  pt_weights[f"""{prefix}.weight"""].data
	UpperCAmelCase_		    =  pt_weights[f"""{prefix}.bias"""].data




def  A_(      A					,   A					,   A ):
	copy_linear(hf_layer.layer_norma					,   lowerCAmelCase__					,   f"""{prefix}.ln_1""" )
	copy_linear(hf_layer.layer_norma					,   lowerCAmelCase__					,   f"""{prefix}.ln_2""" )

	# copy MLP
	copy_mlp(hf_layer.mlp					,   lowerCAmelCase__					,   f"""{prefix}.mlp""" )

	# copy attn
	copy_attn_layer(hf_layer.self_attn					,   lowerCAmelCase__					,   f"""{prefix}.attn""" )




def  A_(      A					,   A					,   A ):



	for layer_id, hf_layer in enumerate(lowerCAmelCase__ ):
		copy_layer(lowerCAmelCase__					,   lowerCAmelCase__					,   f"""{prefix}.{layer_id}""" )




def  A_(      A					,   A ):
	UpperCAmelCase_		    =  pt_weights["""text_projection"""].data.T

	# copy text encoder
	for name, param in hf_model.text_model.named_parameters():
		UpperCAmelCase_		    =  pt_weights[f"""bert.{name}"""].data




def  A_(      A					,   A ):
	UpperCAmelCase_		    =  pt_weights["""visual.proj"""].data.T

	# copy layer norms
	copy_linear(hf_model.vision_model.pre_layrnorm					,   lowerCAmelCase__					,   """visual.ln_pre""" )
	copy_linear(hf_model.vision_model.post_layernorm					,   lowerCAmelCase__					,   """visual.ln_post""" )

	# copy embeddings
	UpperCAmelCase_		    =  pt_weights["""visual.conv1.weight"""].data
	UpperCAmelCase_		    =  pt_weights["""visual.class_embedding"""].data
	UpperCAmelCase_		    =  pt_weights["""visual.positional_embedding"""].data

	# copy encoder
	copy_layers(hf_model.vision_model.encoder.layers					,   lowerCAmelCase__					,   """visual.transformer.resblocks""" )




@torch.no_grad()
def  A_(      A					,   A					,   A=None ):
	assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
	UpperCAmelCase_		    =  ChineseCLIPConfig.from_pretrained(lowerCAmelCase__ )

	UpperCAmelCase_		    =  ChineseCLIPModel(lowerCAmelCase__ ).eval()

	UpperCAmelCase_		    =  torch.load(lowerCAmelCase__					,   map_location="""cpu""" )["""state_dict"""]
	UpperCAmelCase_		    =  {(name[7:] if name.startswith("""module.""" ) else name): value for name, value in pt_weights.items()}

	copy_text_model_and_projection(lowerCAmelCase__					,   lowerCAmelCase__ )
	copy_vision_model_and_projection(lowerCAmelCase__					,   lowerCAmelCase__ )
	UpperCAmelCase_		    =  pt_weights["""logit_scale"""].data

	hf_model.save_pretrained(lowerCAmelCase__ )


if __name__ == "__main__":
				UpperCamelCase__ :			Tuple       	=			argparse.ArgumentParser()
				parser.add_argument(
				    """--pytorch_dump_folder_path""",
				    default=None,
				    type=str,
				    help="""Path to the output folder storing converted hf PyTorch model.""",
				)
				parser.add_argument(
				    """--checkpoint_path""", default=None, type=str, help="""Path to original github format ChineseCLIP checkpoint."""
				)
				parser.add_argument(
				    """--config_path""", default=None, required=True, type=str, help="""Path to hf config.json of model to convert."""
				)
				UpperCamelCase__ :			Optional[int]       	=			parser.parse_args()

				convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
				print("""The conversion is finished!""")






'''simple docstring'''






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline

