

'''simple docstring'''


import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Union[str, Any]:
	UpperCAmelCase_		:      Any       	=					args.pruning_method
	UpperCAmelCase_		:      Optional[int]       	=					args.threshold

	UpperCAmelCase_		:      int       	=					args.model_name_or_path.rstrip('/'  )
	UpperCAmelCase_		:      Optional[Any]       	=					args.target_model_path

	print(f"""Load fine-pruned model from {model_name_or_path}"""  )
	UpperCAmelCase_		:      str       	=					torch.load(os.path.join(UpperCamelCase   ,'pytorch_model.bin'  )  )
	UpperCAmelCase_		:      int       	=					{}

	for name, tensor in model.items():
		if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
			UpperCAmelCase_		:      Union[str, Any]       	=					tensor
			print(f"""Copied layer {name}"""  )
		elif "classifier" in name or "qa_output" in name:
			UpperCAmelCase_		:      int       	=					tensor
			print(f"""Copied layer {name}"""  )
		elif "bias" in name:
			UpperCAmelCase_		:      List[str]       	=					tensor
			print(f"""Copied layer {name}"""  )
		else:
			if pruning_method == "magnitude":
				UpperCAmelCase_		:      List[str]       	=					MagnitudeBinarizer.apply(inputs=UpperCamelCase   ,threshold=UpperCamelCase  )
				UpperCAmelCase_		:      Optional[Any]       	=					tensor * mask
				print(f"""Pruned layer {name}"""  )
			elif pruning_method == "topK":
				if "mask_scores" in name:
					continue
				UpperCAmelCase_		:      Optional[Any]       	=					name[:-6]
				UpperCAmelCase_		:      Optional[Any]       	=					model[f"""{prefix_}mask_scores"""]
				UpperCAmelCase_		:      List[Any]       	=					TopKBinarizer.apply(UpperCamelCase   ,UpperCamelCase  )
				UpperCAmelCase_		:      int       	=					tensor * mask
				print(f"""Pruned layer {name}"""  )
			elif pruning_method == "sigmoied_threshold":
				if "mask_scores" in name:
					continue
				UpperCAmelCase_		:      int       	=					name[:-6]
				UpperCAmelCase_		:      List[Any]       	=					model[f"""{prefix_}mask_scores"""]
				UpperCAmelCase_		:      List[Any]       	=					ThresholdBinarizer.apply(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )
				UpperCAmelCase_		:      List[Any]       	=					tensor * mask
				print(f"""Pruned layer {name}"""  )
			elif pruning_method == "l0":
				if "mask_scores" in name:
					continue
				UpperCAmelCase_		:      int       	=					name[:-6]
				UpperCAmelCase_		:      Union[str, Any]       	=					model[f"""{prefix_}mask_scores"""]
				UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					-0.1, 1.1
				UpperCAmelCase_		:      Optional[int]       	=					torch.sigmoid(UpperCamelCase  )
				UpperCAmelCase_		:      List[str]       	=					s * (r - l) + l
				UpperCAmelCase_		:      Optional[int]       	=					s_bar.clamp(min=0.0   ,max=1.0  )
				UpperCAmelCase_		:      Union[str, Any]       	=					tensor * mask
				print(f"""Pruned layer {name}"""  )
			else:
				raise ValueError('Unknown pruning method'  )

	if target_model_path is None:
		UpperCAmelCase_		:      Optional[Any]       	=					os.path.join(
		    os.path.dirname(UpperCamelCase  )   ,f"""bertarized_{os.path.basename(UpperCamelCase  )}"""  )

	if not os.path.isdir(UpperCamelCase  ):
		shutil.copytree(UpperCamelCase   ,UpperCamelCase  )
		print(f"""\nCreated folder {target_model_path}"""  )

	torch.save(UpperCamelCase   ,os.path.join(UpperCamelCase   ,'pytorch_model.bin'  )  )
	print('\nPruned model saved! See you later!'  )


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

						parser.add_argument(
						    "--pruning_method",
						    choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
						    type=str,
						    required=True,
						    help=(
						        "Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
						        " sigmoied_threshold = Soft movement pruning)"
						    ),
						)
						parser.add_argument(
						    "--threshold",
						    type=float,
						    required=False,
						    help=(
						        "For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
						        "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
						        "Not needed for `l0`"
						    ),
						)
						parser.add_argument(
						    "--model_name_or_path",
						    type=str,
						    required=True,
						    help="Folder containing the model that was previously fine-pruned",
						)
						parser.add_argument(
						    "--target_model_path",
						    default=None,
						    type=str,
						    required=False,
						    help="Folder containing the model that was previously fine-pruned",
						)

						lowerCAmelCase__										= parser.parse_args()

						main(args)









'''simple docstring'''


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:
	# load base model
	UpperCAmelCase_		:      List[str]       	=					StableDiffusionPipeline.from_pretrained(UpperCamelCase   ,torch_dtype=torch.floataa  )

	# load LoRA weight from .safetensors
	UpperCAmelCase_		:      Union[str, Any]       	=					load_file(UpperCamelCase  )

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

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

		UpperCAmelCase_		:      str       	=					[]
		if "lora_down" in key:
			pair_keys.append(key.replace('lora_down'   ,'lora_up'  )  )
			pair_keys.append(UpperCamelCase  )
		else:
			pair_keys.append(UpperCamelCase  )
			pair_keys.append(key.replace('lora_up'   ,'lora_down'  )  )

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

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

	return pipeline


if __name__ == "__main__":
						lowerCAmelCase__										= 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__										= parser.parse_args()

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

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

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






