






'''simple docstring'''





import string



def 					_lowerCamelCase(			lowerCAmelCase__				:		str      ):





				'''simple docstring'''







				for key in range(len(string.ascii_uppercase      )      ):
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   ''
								for symbol in message:
												if symbol in string.ascii_uppercase:
																SCREAMING_SNAKE_CASE_   :	Any													=   string.ascii_uppercase.find(lowerCAmelCase__      )
																SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   num - key
																if num < 0:
																				SCREAMING_SNAKE_CASE_   :	Any													=   num + len(string.ascii_uppercase      )
																SCREAMING_SNAKE_CASE_   :	List[Any]													=   translated + string.ascii_uppercase[num]
												else:
																SCREAMING_SNAKE_CASE_   :	int													=   translated + symbol
								print(F'''Decryption using Key #{key}: {translated}'''      )



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   input('Encrypted message: '      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   message.upper()
				decrypt(lowerCAmelCase__      )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()







import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

A        =			[
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

A        =			[
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

A        =			[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
				# loop over downblocks/upblocks

				for j in range(2):
								# loop over resnets/attentions for downblocks
								A        =			f"""down_blocks.{i}.resnets.{j}."""
								A        =			f"""input_blocks.{3*i + j + 1}.0."""
								unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

								if i < 3:
												# no attention layers in down_blocks.3
												A        =			f"""down_blocks.{i}.attentions.{j}."""
												A        =			f"""input_blocks.{3*i + j + 1}.1."""
												unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

				for j in range(3):
								# loop over resnets/attentions for upblocks
								A        =			f"""up_blocks.{i}.resnets.{j}."""
								A        =			f"""output_blocks.{3*i + j}.0."""
								unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

								if i > 0:
												# no attention layers in up_blocks.0
												A        =			f"""up_blocks.{i}.attentions.{j}."""
												A        =			f"""output_blocks.{3*i + j}.1."""
												unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

				if i < 3:
								# no downsample in down_blocks.3
								A        =			f"""down_blocks.{i}.downsamplers.0.conv."""
								A        =			f"""input_blocks.{3*(i+1)}.0.op."""
								unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

								# no upsample in up_blocks.3
								A        =			f"""up_blocks.{i}.upsamplers.0."""
								A        =			f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
								unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

A        =			'mid_block.attentions.0.'
A        =			'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
				A        =			f"""mid_block.resnets.{j}."""
				A        =			f"""middle_block.{2*j}."""
				unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def 					_lowerCamelCase(			lowerCAmelCase__				:		Union[str, Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																SCREAMING_SNAKE_CASE_   :	int													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
												SCREAMING_SNAKE_CASE_   :	List[str]													=   v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												SCREAMING_SNAKE_CASE_   :	str													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Tuple													=   v
				SCREAMING_SNAKE_CASE_   :	str													=   {v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


# ================#
# VAE Conversion #
# ================#

A        =			[
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
				# down_blocks have two resnets
				for j in range(2):
								A        =			f"""encoder.down_blocks.{i}.resnets.{j}."""
								A        =			f"""encoder.down.{i}.block.{j}."""
								vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

				if i < 3:
								A        =			f"""down_blocks.{i}.downsamplers.0."""
								A        =			f"""down.{i}.downsample."""
								vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

								A        =			f"""up_blocks.{i}.upsamplers.0."""
								A        =			f"""up.{3-i}.upsample."""
								vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

				# up_blocks have three resnets
				# also, up blocks in hf are numbered in reverse from sd
				for j in range(3):
								A        =			f"""decoder.up_blocks.{i}.resnets.{j}."""
								A        =			f"""decoder.up.{3-i}.block.{j}."""
								vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
				A        =			f"""mid_block.resnets.{i}."""
				A        =			f"""mid.block_{i+1}."""
				vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


A        =			[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				return w.reshape(*w.shape    ,    1    ,    1      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   {k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												SCREAMING_SNAKE_CASE_   :	Any													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	int													=   v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																SCREAMING_SNAKE_CASE_   :	Dict													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
												SCREAMING_SNAKE_CASE_   :	Tuple													=   v
				SCREAMING_SNAKE_CASE_   :	Dict													=   {v: vae_state_dict[k] for k, v in mapping.items()}
				SCREAMING_SNAKE_CASE_   :	int													=   ['q', 'k', 'v', 'proj_out']
				for k, v in new_state_dict.items():
								for weight_name in weights_to_convert:
												if F'''mid.attn_1.{weight_name}.weight''' in k:
																print(F'''Reshaping {k} for SD format'''      )
																SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   reshape_weight_for_sd(lowerCAmelCase__      )
				return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


A        =			[
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
A        =			{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A        =			re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
A        =			{'q': 0, 'k': 1, 'v': 2}



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {}
				SCREAMING_SNAKE_CASE_   :	List[str]													=   {}
				SCREAMING_SNAKE_CASE_   :	Any													=   {}
				for k, v in text_enc_dict.items():
								if (
								    k.endswith('.self_attn.q_proj.weight'      )
								    or k.endswith('.self_attn.k_proj.weight'      )
								    or k.endswith('.self_attn.v_proj.weight'      )
								):
												SCREAMING_SNAKE_CASE_   :	int													=   k[: -len('.q_proj.weight'      )]
												SCREAMING_SNAKE_CASE_   :	List[str]													=   k[-len('q_proj.weight'      )]
												if k_pre not in capture_qkv_weight:
																SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [None, None, None]
												SCREAMING_SNAKE_CASE_   :	int													=   v
												continue

								if (
								    k.endswith('.self_attn.q_proj.bias'      )
								    or k.endswith('.self_attn.k_proj.bias'      )
								    or k.endswith('.self_attn.v_proj.bias'      )
								):
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   k[: -len('.q_proj.bias'      )]
												SCREAMING_SNAKE_CASE_   :	str													=   k[-len('q_proj.bias'      )]
												if k_pre not in capture_qkv_bias:
																SCREAMING_SNAKE_CASE_   :	Tuple													=   [None, None, None]
												SCREAMING_SNAKE_CASE_   :	Any													=   v
												continue

								SCREAMING_SNAKE_CASE_   :	Tuple													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Any													=   v

				for k_pre, tensors in capture_qkv_weight.items():
								if None in tensors:
												raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'      )
								SCREAMING_SNAKE_CASE_   :	Dict													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.cat(lowerCAmelCase__      )

				for k_pre, tensors in capture_qkv_bias.items():
								if None in tensors:
												raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'      )
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.cat(lowerCAmelCase__      )

				return new_state_dict



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				return text_enc_dict


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

				parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
				parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
				parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
				parser.add_argument(
				    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
				)

				A        =			parser.parse_args()

				assert args.model_path is not None, "Must provide a model path!"

				assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

				# Path for safetensors
				A        =			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
				A        =			osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
				A        =			osp.join(args.model_path, 'text_encoder', 'model.safetensors')

				# Load models from safetensors if it exists, if it doesn't pytorch
				if osp.exists(unet_path):
								A        =			load_file(unet_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
								A        =			torch.load(unet_path, map_location='cpu')

				if osp.exists(vae_path):
								A        =			load_file(vae_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
								A        =			torch.load(vae_path, map_location='cpu')

				if osp.exists(text_enc_path):
								A        =			load_file(text_enc_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
								A        =			torch.load(text_enc_path, map_location='cpu')

				# Convert the UNet model
				A        =			convert_unet_state_dict(unet_state_dict)
				A        =			{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

				# Convert the VAE model
				A        =			convert_vae_state_dict(vae_state_dict)
				A        =			{'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

				# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
				A        =			'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

				if is_vaa_model:
								# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
								A        =			{'transformer.' + k: v for k, v in text_enc_dict.items()}
								A        =			convert_text_enc_state_dict_vaa(text_enc_dict)
								A        =			{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
				else:
								A        =			convert_text_enc_state_dict(text_enc_dict)
								A        =			{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

				# Put together new checkpoint
				A        =			{**unet_state_dict, **vae_state_dict, **text_enc_dict}
				if args.half:
								A        =			{k: v.half() for k, v in state_dict.items()}

				if args.use_safetensors:
								save_file(state_dict, args.checkpoint_path)
				else:
								A        =			{'state_dict': state_dict}
								torch.save(state_dict, args.checkpoint_path)