



"""simple docstring"""
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      List[str]:
		# load base model
		a_     :      Optional[int] 						=     StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__, torch_dtype=torch.floataa	)

		# load LoRA weight from .safetensors
		a_     :      Optional[Any] 						=     load_file(SCREAMING_SNAKE_CASE__	)

		a_     :      Union[str, Any] 						=     []

		# 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:
						a_     :      Optional[int] 						=     key.split("."	)[0].split(LORA_PREFIX_TEXT_ENCODER + "_"	)[-1].split("_"	)
						a_     :      int 						=     pipeline.text_encoder
				else:
						a_     :      List[Any] 						=     key.split("."	)[0].split(LORA_PREFIX_UNET + "_"	)[-1].split("_"	)
						a_     :      str 						=     pipeline.unet

				# find the target layer
				a_     :      Dict 						=     layer_infos.pop(0	)
				while len(SCREAMING_SNAKE_CASE__	) > -1:
						try:
								a_     :      str 						=     curr_layer.__getattr__(SCREAMING_SNAKE_CASE__	)
								if len(SCREAMING_SNAKE_CASE__	) > 0:
										a_     :      Tuple 						=     layer_infos.pop(0	)
								elif len(SCREAMING_SNAKE_CASE__	) == 0:
										break
						except Exception:
								if len(SCREAMING_SNAKE_CASE__	) > 0:
										temp_name += "_" + layer_infos.pop(0	)
								else:
										a_     :      List[str] 						=     layer_infos.pop(0	)

				a_     :      Optional[int] 						=     []
				if "lora_down" in key:
						pair_keys.append(key.replace("lora_down", "lora_up"	)	)
						pair_keys.append(SCREAMING_SNAKE_CASE__	)
				else:
						pair_keys.append(SCREAMING_SNAKE_CASE__	)
						pair_keys.append(key.replace("lora_up", "lora_down"	)	)

				# update weight
				if len(state_dict[pair_keys[0]].shape	) == 4:
						a_     :      Union[str, Any] 						=     state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						a_     :      Dict 						=     state_dict[pair_keys[1]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	).unsqueeze(2	).unsqueeze(3	)
				else:
						a_     :      str 						=     state_dict[pair_keys[0]].to(torch.floataa	)
						a_     :      Tuple 						=     state_dict[pair_keys[1]].to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

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

		return pipeline


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= 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.)""")

					SCREAMING_SNAKE_CASE_				= parser.parse_args()

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

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

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




"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE_				= """Create a default config file for Accelerate with only a few flags set."""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__="no", SCREAMING_SNAKE_CASE__ = default_json_config_file, SCREAMING_SNAKE_CASE__ = False	)			->      Tuple:
		a_     :      Union[str, Any] 						=     Path(SCREAMING_SNAKE_CASE__	)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__, exist_ok=SCREAMING_SNAKE_CASE__	)
		if path.exists():
				print(
				    F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""	)
				return False
		a_     :      Optional[int] 						=     mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""	)
		a_     :      str 						=     {
		    "compute_environment": "LOCAL_MACHINE",
		    "mixed_precision": mixed_precision,
		}
		if torch.cuda.is_available():
				a_     :      int 						=     torch.cuda.device_count()
				a_     :      Optional[Any] 						=     num_gpus
				a_     :      int 						=     False
				if num_gpus > 1:
						a_     :      Any 						=     "MULTI_GPU"
				else:
						a_     :      int 						=     "NO"
		elif is_xpu_available() and use_xpu:
				a_     :      int 						=     torch.xpu.device_count()
				a_     :      str 						=     num_xpus
				a_     :      Tuple 						=     False
				if num_xpus > 1:
						a_     :      int 						=     "MULTI_XPU"
				else:
						a_     :      List[str] 						=     "NO"
		elif is_npu_available():
				a_     :      List[Any] 						=     torch.npu.device_count()
				a_     :      int 						=     num_npus
				a_     :      List[Any] 						=     False
				if num_npus > 1:
						a_     :      str 						=     "MULTI_NPU"
				else:
						a_     :      Union[str, Any] 						=     "NO"
		else:
				a_     :      Optional[Any] 						=     0
				a_     :      Optional[Any] 						=     True
				a_     :      Tuple 						=     1
				a_     :      Optional[int] 						=     "NO"
		a_     :      str 						=     ClusterConfig(**SCREAMING_SNAKE_CASE__	)
		config.to_json_file(SCREAMING_SNAKE_CASE__	)
		return path



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		a_     :      Dict 						=     parser.add_parser("default", parents=SCREAMING_SNAKE_CASE__, help=SCREAMING_SNAKE_CASE__, formatter_class=SCREAMING_SNAKE_CASE__	)
		parser.add_argument(
		    "--config_file", default=SCREAMING_SNAKE_CASE__, help=(
		        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
		        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
		        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
		        "with 'huggingface'."
		    ), dest="save_location", )

		parser.add_argument(
		    "--mixed_precision", choices=["no", "fp16", "bf16"], type=SCREAMING_SNAKE_CASE__, help="Whether or not to use mixed precision training. "
		    "Choose between FP16 and BF16 (bfloat16) training. "
		    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", default="no", )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
		return parser



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      Any 						=     write_basic_config(args.mixed_precision, args.save_location	)
		if config_file:
				print(F"""accelerate configuration saved at {config_file}"""	)