



'''simple docstring'''






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline





def    UpperCAmelCase							(  UpperCAmelCase__							:							int       ,							UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Any):
				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__":
					A			      =      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.)')

					A			      =      parser.parse_args()

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

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

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


'''simple docstring'''






import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
A			      =      {
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
A			      =      'ETAOINSHRDLCUMWFGYPBVKJXQZ'
A			      =      'ABCDEFGHIJKLMNOPQRSTUVWXYZ'





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= {letter: 0 for letter in string.ascii_uppercase}
				for letter in message.upper():
								if letter in LETTERS:
												letter_count[letter] += 1

				return letter_count





def    UpperCAmelCase							(  UpperCAmelCase__							:							tuple):
				return x[0]





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= get_letter_count(UpperCAmelCase__)
				lowerCamelCase					:			dict[int, list[str]]									= {
				    freq: [] for letter, freq in letter_to_freq.items()
				}
				for letter in LETTERS:
								freq_to_letter[letter_to_freq[letter]].append(UpperCAmelCase__)

				lowerCamelCase					:			dict[int, str]									= {}

				for freq in freq_to_letter:
								freq_to_letter[freq].sort(key=ETAOIN.find       ,							reverse=UpperCAmelCase__)
								lowerCamelCase					:			int									= ''.join(freq_to_letter[freq])

				lowerCamelCase					:			Any									= list(freq_to_letter_str.items())
				freq_pairs.sort(key=UpperCAmelCase__       ,							reverse=UpperCAmelCase__)

				lowerCamelCase					:			list[str]									= [freq_pair[1] for freq_pair in freq_pairs]

				return "".join(UpperCAmelCase__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= get_frequency_order(UpperCAmelCase__)
				lowerCamelCase					:			Optional[Any]									= 0
				for common_letter in ETAOIN[:6]:
								if common_letter in freq_order[:6]:
												match_score += 1

				for uncommon_letter in ETAOIN[-6:]:
								if uncommon_letter in freq_order[-6:]:
												match_score += 1

				return match_score


if __name__ == "__main__":
					import doctest

					doctest.testmod()
