
"""simple docstring"""



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			StableDiffusionPipeline.from_pretrained(UpperCAmelCase__     ,  torch_dtype=torch.floataa						)

							# load LoRA weight from .safetensors
							_lowerCAmelCase  	=			load_file(UpperCAmelCase__						)

							_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(UpperCAmelCase__						) > -1:
																					try:
																												_lowerCAmelCase  	=			curr_layer.__getattr__(UpperCAmelCase__						)
																												if len(UpperCAmelCase__						) > 0:
																																			_lowerCAmelCase  	=			layer_infos.pop(0						)
																												elif len(UpperCAmelCase__						) == 0:
																																			break
																					except Exception:
																												if len(UpperCAmelCase__						) > 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(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:
																					_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(UpperCAmelCase__     ,  UpperCAmelCase__						).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(UpperCAmelCase__     ,  UpperCAmelCase__						)

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

							return pipeline


if __name__ == "__main__":
	_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.)''')

	_snake_case          =						parser.parse_args()

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

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

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








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			generate_pascal_triangle(SCREAMING_SNAKE_CASE						)
							for row_idx in range(SCREAMING_SNAKE_CASE						):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1						):
																					print(end=' '						)
														# Print row values
														for col_idx in range(row_idx + 1						):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]     ,  end=' '						)
																					else:
																												print(triangle[row_idx][col_idx]     ,  end=''						)
														print()






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							if not isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
														raise TypeError('The input value of \'num_rows\' should be \'int\''						)

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'						)

							_lowerCAmelCase  	=			[]
							for current_row_idx in range(SCREAMING_SNAKE_CASE						):
														_lowerCAmelCase  	=			populate_current_row(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
														triangle.append(SCREAMING_SNAKE_CASE						)
							return triangle






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					list[list[int]]     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							_lowerCAmelCase	,		_lowerCAmelCase  	=			1, 1
							for current_col_idx in range(1     ,  SCREAMING_SNAKE_CASE						):
														calculate_current_element(
														    SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							return current_row






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					list[list[int]]     ,  SCREAMING_SNAKE_CASE:					list[int]     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					int     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			triangle[current_row_idx - 1][current_col_idx - 1]
							_lowerCAmelCase  	=			triangle[current_row_idx - 1][current_col_idx]
							_lowerCAmelCase  	=			above_to_left_elt + above_to_right_elt






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							if not isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
														raise TypeError('The input value of \'num_rows\' should be \'int\''						)

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'						)

							_lowerCAmelCase  	=			[[1]]

							for row_index in range(1     ,  SCREAMING_SNAKE_CASE						):
														_lowerCAmelCase  	=			[0] + result[-1] + [0]
														_lowerCAmelCase  	=			row_index + 1
														# Calculate the number of distinct elements in a row
														_lowerCAmelCase  	=			sum(divmod(SCREAMING_SNAKE_CASE     ,  2						)						)
														_lowerCAmelCase  	=			[
														    temp_row[i - 1] + temp_row[i] for i in range(1     ,  distinct_elements + 1						)
														]
														_lowerCAmelCase  	=			row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														_lowerCAmelCase  	=			row_first_half + row_second_half
														result.append(SCREAMING_SNAKE_CASE						)

							return result






def 	__snake_case  (    ):






							"""simple docstring"""



							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(SCREAMING_SNAKE_CASE:					Callable     ,  SCREAMING_SNAKE_CASE:					int						) -> None:
														_lowerCAmelCase  	=			f"""{func.__name__}({value})"""
														_lowerCAmelCase  	=			timeit(f"""__main__.{call}"""     ,  setup='import __main__'						)
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(f"""{call:38} -- {timing:.4f} seconds"""						)

							for value in range(15						):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
														print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()






