






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 						A__			(				__lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			List[Any]						):
						lowerCamelCase__         =  StableDiffusionPipeline.from_pretrained(a__   ,    torch_dtype=torch.floataa						)

						# load LoRA weight from .safetensors
						lowerCamelCase__         =  load_file(a__						)

						lowerCamelCase__         =  []

						# 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__         =  key.split("""."""						)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""						)[-1].split("""_"""						)
																		lowerCamelCase__         =  pipeline.text_encoder
												else:
																		lowerCamelCase__         =  key.split("""."""						)[0].split(LORA_PREFIX_UNET + """_"""						)[-1].split("""_"""						)
																		lowerCamelCase__         =  pipeline.unet

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

												lowerCamelCase__         =  []
												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__         =  state_dict[pair_keys[0]].squeeze(3						).squeeze(2						).to(torch.floataa						)
																		lowerCamelCase__         =  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__         =  state_dict[pair_keys[0]].to(torch.floataa						)
																		lowerCamelCase__         =  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__":
							UpperCamelCase    :      Dict  			=		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.)')

							UpperCamelCase    :      List[str]  			=		parser.parse_args()

							UpperCamelCase    :      Optional[Any]  			=		args.base_model_path
							UpperCamelCase    :      Any  			=		args.checkpoint_path
							UpperCamelCase    :      List[str]  			=		args.dump_path
							UpperCamelCase    :      Tuple  			=		args.lora_prefix_unet
							UpperCamelCase    :      List[Any]  			=		args.lora_prefix_text_encoder
							UpperCamelCase    :      Optional[int]  			=		args.alpha

							UpperCamelCase    :      List[Any]  			=		convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

							UpperCamelCase    :      str  			=		pipe.to(args.device)
							pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









'''simple docstring'''







import numpy

# List of input, output pairs
UpperCamelCase    :      List[Any]  			=		(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCamelCase    :      Optional[int]  			=		(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
UpperCamelCase    :      int  			=		[2, 4, 1, 5]
UpperCamelCase    :      int  			=		len(train_data)
UpperCamelCase    :      Dict  			=		0.009
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			str="train"						):
						return calculate_hypothesis_value(__lowerCAmelCase   ,    __lowerCAmelCase						) - output(
						    __lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				__lowerCAmelCase		:			Any						):
						lowerCamelCase__         =  0
						for i in range(len(__lowerCAmelCase						) - 1						):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val
def 						A__			(				__lowerCAmelCase		:			Optional[Any]   ,    __lowerCAmelCase		:			Tuple						):
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Dict						):
						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]						)
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]						)
						return None
def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			Optional[Any]=m						):
						lowerCamelCase__         =  0
						for i in range(__lowerCAmelCase						):
												if index == -1:
																		summation_value += _error(__lowerCAmelCase						)
												else:
																		summation_value += _error(__lowerCAmelCase						) * train_data[i][0][index]
						return summation_value
def 						A__			(				__lowerCAmelCase		:			List[Any]						):
						lowerCamelCase__         =  summation_of_cost_derivative(__lowerCAmelCase   ,    __lowerCAmelCase						) / m
						return cost_derivative_value
def 						A__			(				):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						lowerCamelCase__         =  0.00_0002
						lowerCamelCase__         =  0
						lowerCamelCase__         =  0
						while True:
												j += 1
												lowerCamelCase__         =  [0, 0, 0, 0]
												for i in range(0   ,    len(__lowerCAmelCase						)						):
																		lowerCamelCase__         =  get_cost_derivative(i - 1						)
																		lowerCamelCase__         =  (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    __lowerCAmelCase   ,    __lowerCAmelCase   ,    atol=__lowerCAmelCase   ,    rtol=__lowerCAmelCase   ,    ):
																		break
												lowerCamelCase__         =  temp_parameter_vector
						print(("""Number of iterations:""", j)						)
def 						A__			(				):
						for i in range(len(__lowerCAmelCase						)						):
												print(("""Actual output value:""", output(__lowerCAmelCase   ,    """test"""						))						)
												print(("""Hypothesis output:""", calculate_hypothesis_value(__lowerCAmelCase   ,    """test"""						))						)


if __name__ == "__main__":
							run_gradient_descent()
							print('\nTesting gradient descent for a linear hypothesis function.\n')
							test_gradient_descent()

