
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      StableDiffusionPipeline.from_pretrained(A__				,	torch_dtype=torch.floataa	)

    # load LoRA weight from .safetensors
    snake_case				  =      load_file(A__	)

    snake_case				  =      []

    # directly update weight in diffusers model
    for key in state_dict:
        # it is suggested to print out the key, it usually will be something like below
        # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

        # as we have set the alpha beforehand, so just skip
        if ".alpha" in key or key in visited:
            continue

        if "text" in key:
            snake_case				  =      key.split("."	)[0].split(LORA_PREFIX_TEXT_ENCODER + "_"	)[-1].split("_"	)
            snake_case				  =      pipeline.text_encoder
        else:
            snake_case				  =      key.split("."	)[0].split(LORA_PREFIX_UNET + "_"	)[-1].split("_"	)
            snake_case				  =      pipeline.unet

        # find the target layer
        snake_case				  =      layer_infos.pop(0	)
        while len(A__	) > -1:
            try:
                snake_case				  =      curr_layer.__getattr__(A__	)
                if len(A__	) > 0:
                    snake_case				  =      layer_infos.pop(0	)
                elif len(A__	) == 0:
                    break
            except Exception:
                if len(A__	) > 0:
                    temp_name += "_" + layer_infos.pop(0	)
                else:
                    snake_case				  =      layer_infos.pop(0	)

        snake_case				  =      []
        if "lora_down" in key:
            pair_keys.append(key.replace("lora_down"				,	"lora_up"	)	)
            pair_keys.append(A__	)
        else:
            pair_keys.append(A__	)
            pair_keys.append(key.replace("lora_up"				,	"lora_down"	)	)

        # update weight
        if len(state_dict[pair_keys[0]].shape	) == 4:
            snake_case				  =      state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
            snake_case				  =      state_dict[pair_keys[1]].squeeze(3	).squeeze(2	).to(torch.floataa	)
            curr_layer.weight.data += alpha * torch.mm(A__				,	A__	).unsqueeze(2	).unsqueeze(3	)
        else:
            snake_case				  =      state_dict[pair_keys[0]].to(torch.floataa	)
            snake_case				  =      state_dict[pair_keys[1]].to(torch.floataa	)
            curr_layer.weight.data += alpha * torch.mm(A__				,	A__	)

        # update visited list
        for item in pair_keys:
            visited.append(A__	)

    return pipeline


if __name__ == "__main__":
   _A						      =				argparse.ArgumentParser()

   parser.add_argument(
       "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
   )
   parser.add_argument(
       "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
   )
   parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
   parser.add_argument(
       "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
   )
   parser.add_argument(
       "--lora_prefix_text_encoder",
       default="lora_te",
       type=str,
       help="The prefix of text encoder weight in safetensors",
   )
   parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
   parser.add_argument(
       "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
   )
   parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

   _A						      =				parser.parse_args()

   _A						      =				args.base_model_path
   _A						      =				args.checkpoint_path
   _A						      =				args.dump_path
   _A						      =				args.lora_prefix_unet
   _A						      =				args.lora_prefix_text_encoder
   _A						      =				args.alpha

   _A						      =				convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

   _A						      =				pipe.to(args.device)
   pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Any     =	"poolformer"





     def __init__(self   :					Optional[int]       ,					_A   :					Optional[Any]=3       ,					_A   :					Optional[int]=1_6       ,					_A   :					Dict=1_6       ,					_A   :					Tuple=3       ,					_A   :					Tuple=4.0       ,					_A   :					int=[2, 2, 6, 2]       ,					_A   :					Dict=[6_4, 1_2_8, 3_2_0, 5_1_2]       ,					_A   :					int=[7, 3, 3, 3]       ,					_A   :					List[str]=[4, 2, 2, 2]       ,					_A   :					str=[2, 1, 1, 1]       ,					_A   :					List[Any]=4       ,					_A   :					Any=0.0       ,					_A   :					Optional[Any]="gelu"       ,					_A   :					Optional[Any]=True       ,					_A   :					List[str]=1E-5       ,					_A   :					List[str]=0.02       ,					**_A   :					str       ,					)		->		Tuple:
         snake_case				  =      num_channels
         snake_case				  =      patch_size
         snake_case				  =      stride
         snake_case				  =      padding
         snake_case				  =      pool_size
         snake_case				  =      hidden_sizes
         snake_case				  =      mlp_ratio
         snake_case				  =      depths
         snake_case				  =      patch_sizes
         snake_case				  =      strides
         snake_case				  =      num_encoder_blocks
         snake_case				  =      drop_path_rate
         snake_case				  =      hidden_act
         snake_case				  =      use_layer_scale
         snake_case				  =      layer_scale_init_value
         snake_case				  =      initializer_range
         super().__init__(**_A     )







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Tuple     =	version.parse("1.11"						)





     @property
     def 	UpperCAmelCase(self   :					int     )		->		Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]     )





     @property
     def 	UpperCAmelCase(self   :					int     )		->		float:
         return 2E-3

