import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 						A__  (					__A   , __A   , __A   , __A   , __A	):







    '''simple docstring'''
    # load base model
    _lowerCamelCase     :		List[Any] 						=						StableDiffusionPipeline.from_pretrained(__A   , torch_dtype=torch.floataa	)

    # load LoRA weight from .safetensors
    _lowerCamelCase     :		str 						=						load_file(__A	)

    _lowerCamelCase     :		int 						=						[]

    # 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:
            _lowerCamelCase     :		Union[str, Any] 						=						key.split("""."""	)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""	)[-1].split("""_"""	)
            _lowerCamelCase     :		Any 						=						pipeline.text_encoder
        else:
            _lowerCamelCase     :		Union[str, Any] 						=						key.split("""."""	)[0].split(LORA_PREFIX_UNET + """_"""	)[-1].split("""_"""	)
            _lowerCamelCase     :		Any 						=						pipeline.unet

        # find the target layer
        _lowerCamelCase     :		Any 						=						layer_infos.pop(0	)
        while len(__A	) > -1:
            try:
                _lowerCamelCase     :		List[Any] 						=						curr_layer.__getattr__(__A	)
                if len(__A	) > 0:
                    _lowerCamelCase     :		List[Any] 						=						layer_infos.pop(0	)
                elif len(__A	) == 0:
                    break
            except Exception:
                if len(__A	) > 0:
                    temp_name += "_" + layer_infos.pop(0	)
                else:
                    _lowerCamelCase     :		Union[str, Any] 						=						layer_infos.pop(0	)

        _lowerCamelCase     :		Optional[Any] 						=						[]
        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:
            _lowerCamelCase     :		int 						=						state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
            _lowerCamelCase     :		Optional[Any] 						=						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:
            _lowerCamelCase     :		Tuple 						=						state_dict[pair_keys[0]].to(torch.floataa	)
            _lowerCamelCase     :		Optional[Any] 						=						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__":
    lowerCAmelCase			:							Any     =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.)")

    lowerCAmelCase			:							List[Any]     =parser.parse_args()

    lowerCAmelCase			:							Tuple     =args.base_model_path
    lowerCAmelCase			:							Dict     =args.checkpoint_path
    lowerCAmelCase			:							Tuple     =args.dump_path
    lowerCAmelCase			:							Dict     =args.lora_prefix_unet
    lowerCAmelCase			:							List[Any]     =args.lora_prefix_text_encoder
    lowerCAmelCase			:							Optional[Any]     =args.alpha

    lowerCAmelCase			:							Optional[int]     =convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

    lowerCAmelCase			:							Optional[int]     =pipe.to(args.device)
    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)
    if n == 0:
        return 0
    _lowerCamelCase     :		Tuple 						=						float("""-inf"""	)
    for i in range(1   , n + 1	):
        _lowerCamelCase     :		Any 						=						max(
            __A   , prices[i - 1] + naive_cut_rod_recursive(n - i   , __A	)	)

    return max_revue







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)
    _lowerCamelCase     :		Optional[Any] 						=						[float("""-inf"""	) for _ in range(n + 1	)]
    return _top_down_cut_rod_recursive(__A   , __A   , __A	)







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        _lowerCamelCase     :		int 						=						float("""-inf"""	)
        for i in range(1   , n + 1	):
            _lowerCamelCase     :		Optional[Any] 						=						max(
                __A   , prices[i - 1] + _top_down_cut_rod_recursive(n - i   , __A   , __A	)   , )

        _lowerCamelCase     :		Optional[Any] 						=						max_revenue

    return max_rev[n]







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    _lowerCamelCase     :		List[Any] 						=						[float("""-inf"""	) for _ in range(n + 1	)]
    _lowerCamelCase     :		Any 						=						0

    for i in range(1   , n + 1	):
        _lowerCamelCase     :		Any 						=						max_rev[i]
        for j in range(1   , i + 1	):
            _lowerCamelCase     :		List[Any] 						=						max(__A   , prices[j - 1] + max_rev[i - j]	)

        _lowerCamelCase     :		int 						=						max_revenue_i

    return max_rev[n]







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    if n < 0:
        _lowerCamelCase     :		Any 						=						F"""n must be greater than or equal to 0. Got n = {n}"""
        raise ValueError(__A	)

    if n > len(__A	):
        _lowerCamelCase     :		List[Any] 						=						(
            """Each integral piece of rod must have a corresponding price. """
            F"""Got n = {n} but length of prices = {len(__A	)}"""
        )
        raise ValueError(__A	)







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						[6, 10, 12, 15, 20, 23]
    _lowerCamelCase     :		List[str] 						=						len(__A	)

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    _lowerCamelCase     :		Tuple 						=						36

    _lowerCamelCase     :		Any 						=						top_down_cut_rod(__A   , __A	)
    _lowerCamelCase     :		Dict 						=						bottom_up_cut_rod(__A   , __A	)
    _lowerCamelCase     :		List[str] 						=						naive_cut_rod_recursive(__A   , __A	)

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
    main()




