



'''simple docstring'''
import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

snake_case_			:     Any  	=  [
    # (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'),
]

snake_case_			:     Optional[Any]  	=  [
    # (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'),
]

snake_case_			:     Union[str, Any]  	=  []
# 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
										snake_case_			:     str  	=  F"""down_blocks.{i}.resnets.{j}."""
										snake_case_			:     List[Any]  	=  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
															snake_case_			:     Dict  	=  F"""down_blocks.{i}.attentions.{j}."""
															snake_case_			:     Tuple  	=  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
										snake_case_			:     int  	=  F"""up_blocks.{i}.resnets.{j}."""
										snake_case_			:     str  	=  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
															snake_case_			:     List[str]  	=  F"""up_blocks.{i}.attentions.{j}."""
															snake_case_			:     Optional[Any]  	=  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
										snake_case_			:     Optional[int]  	=  F"""down_blocks.{i}.downsamplers.0.conv."""
										snake_case_			:     Any  	=  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
										snake_case_			:     Any  	=  F"""up_blocks.{i}.upsamplers.0."""
										snake_case_			:     Dict  	=  F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
										unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

snake_case_			:     int  	=  'mid_block.attentions.0.'
snake_case_			:     Optional[int]  	=  'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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






def A__   (					UpperCAmelCase_							):
						# buyer beware: this is a *brittle* function,
						# and correct output requires that all of these pieces interact in
						# the exact order in which I have arranged them.
						_UpperCamelCase :		str			    =       {k: k for k in unet_state_dict.keys()}
						for sd_name, hf_name in unet_conversion_map:
												_UpperCamelCase :		Dict			    =       sd_name
						for k, v in mapping.items():
												if "resnets" in k:
																		for sd_part, hf_part in unet_conversion_map_resnet:
																								_UpperCamelCase :		Tuple			    =       v.replace(a__  ,  a__							)
																		_UpperCamelCase :		Dict			    =       v
						for k, v in mapping.items():
												for sd_part, hf_part in unet_conversion_map_layer:
																		_UpperCamelCase :		Optional[Any]			    =       v.replace(a__  ,  a__							)
												_UpperCamelCase :		Union[str, Any]			    =       v
						_UpperCamelCase :		List[str]			    =       {v: unet_state_dict[k] for k, v in mapping.items()}
						return new_state_dict


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

snake_case_			:     List[str]  	=  [
    # (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):
										snake_case_			:     str  	=  F"""encoder.down_blocks.{i}.resnets.{j}."""
										snake_case_			:     Tuple  	=  F"""encoder.down.{i}.block.{j}."""
										vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

					if i < 3:
										snake_case_			:     Optional[int]  	=  F"""down_blocks.{i}.downsamplers.0."""
										snake_case_			:     Union[str, Any]  	=  F"""down.{i}.downsample."""
										vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

										snake_case_			:     List[Any]  	=  F"""up_blocks.{i}.upsamplers.0."""
										snake_case_			:     Union[str, Any]  	=  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):
										snake_case_			:     Any  	=  F"""decoder.up_blocks.{i}.resnets.{j}."""
										snake_case_			:     List[Any]  	=  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):
					snake_case_			:     Dict  	=  F"""mid_block.resnets.{i}."""
					snake_case_			:     Union[str, Any]  	=  F"""mid.block_{i+1}."""
					vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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






def A__   (					UpperCAmelCase_							):
						# convert HF linear weights to SD conv2d weights
						return w.reshape(*w.shape  ,  1  ,  1							)






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		List[Any]			    =       {k: k for k in vae_state_dict.keys()}
						for k, v in mapping.items():
												for sd_part, hf_part in vae_conversion_map:
																		_UpperCamelCase :		Optional[int]			    =       v.replace(a__  ,  a__							)
												_UpperCamelCase :		Tuple			    =       v
						for k, v in mapping.items():
												if "attentions" in k:
																		for sd_part, hf_part in vae_conversion_map_attn:
																								_UpperCamelCase :		Tuple			    =       v.replace(a__  ,  a__							)
																		_UpperCamelCase :		Dict			    =       v
						_UpperCamelCase :		List[Any]			    =       {v: vae_state_dict[k] for k, v in mapping.items()}
						_UpperCamelCase :		List[str]			    =       ['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'							)
																								_UpperCamelCase :		Optional[int]			    =       reshape_weight_for_sd(a__							)
						return new_state_dict


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


snake_case_			:     Dict  	=  [
    # (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'),
]
snake_case_			:     Dict  	=  {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
snake_case_			:     Tuple  	=  re.compile('|'.join(protected.keys()))

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






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		Dict			    =       {}
						_UpperCamelCase :		List[str]			    =       {}
						_UpperCamelCase :		int			    =       {}
						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'							)
												):
																		_UpperCamelCase :		Dict			    =       k[: -len('.q_proj.weight'							)]
																		_UpperCamelCase :		List[str]			    =       k[-len('q_proj.weight'							)]
																		if k_pre not in capture_qkv_weight:
																								_UpperCamelCase :		List[str]			    =       [None, None, None]
																		_UpperCamelCase :		Dict			    =       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'							)
												):
																		_UpperCamelCase :		Dict			    =       k[: -len('.q_proj.bias'							)]
																		_UpperCamelCase :		Any			    =       k[-len('q_proj.bias'							)]
																		if k_pre not in capture_qkv_bias:
																								_UpperCamelCase :		Optional[Any]			    =       [None, None, None]
																		_UpperCamelCase :		Union[str, Any]			    =       v
																		continue

												_UpperCamelCase :		Optional[int]			    =       textenc_pattern.sub(lambda UpperCAmelCase_							: protected[re.escape(m.group(0							)							)]  ,  a__							)
												_UpperCamelCase :		List[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'							)
												_UpperCamelCase :		Tuple			    =       textenc_pattern.sub(lambda UpperCAmelCase_							: protected[re.escape(m.group(0							)							)]  ,  a__							)
												_UpperCamelCase :		Optional[Any]			    =       torch.cat(a__							)

						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'							)
												_UpperCamelCase :		Dict			    =       textenc_pattern.sub(lambda UpperCAmelCase_							: protected[re.escape(m.group(0							)							)]  ,  a__							)
												_UpperCamelCase :		Any			    =       torch.cat(a__							)

						return new_state_dict






def A__   (					UpperCAmelCase_							):
						return text_enc_dict


if __name__ == "__main__":
					snake_case_			:     Dict  	=  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.'
					)

					snake_case_			:     List[Any]  	=  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
					snake_case_			:     int  	=  osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
					snake_case_			:     Union[str, Any]  	=  osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
					snake_case_			:     int  	=  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):
										snake_case_			:     List[Any]  	=  load_file(unet_path, device='cpu')
					else:
										snake_case_			:     Any  	=  osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
										snake_case_			:     Tuple  	=  torch.load(unet_path, map_location='cpu')

					if osp.exists(vae_path):
										snake_case_			:     Optional[int]  	=  load_file(vae_path, device='cpu')
					else:
										snake_case_			:     Tuple  	=  osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
										snake_case_			:     List[Any]  	=  torch.load(vae_path, map_location='cpu')

					if osp.exists(text_enc_path):
										snake_case_			:     int  	=  load_file(text_enc_path, device='cpu')
					else:
										snake_case_			:     List[Any]  	=  osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
										snake_case_			:     List[str]  	=  torch.load(text_enc_path, map_location='cpu')

					# Convert the UNet model
					snake_case_			:     Any  	=  convert_unet_state_dict(unet_state_dict)
					snake_case_			:     Optional[int]  	=  {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

					# Convert the VAE model
					snake_case_			:     Dict  	=  convert_vae_state_dict(vae_state_dict)
					snake_case_			:     List[Any]  	=  {'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
					snake_case_			:     int  	=  '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
										snake_case_			:     Union[str, Any]  	=  {'transformer.' + k: v for k, v in text_enc_dict.items()}
										snake_case_			:     Any  	=  convert_text_enc_state_dict_vaa(text_enc_dict)
										snake_case_			:     Tuple  	=  {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
					else:
										snake_case_			:     Dict  	=  convert_text_enc_state_dict(text_enc_dict)
										snake_case_			:     Union[str, Any]  	=  {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

					# Put together new checkpoint
					snake_case_			:     Tuple  	=  {**unet_state_dict, **vae_state_dict, **text_enc_dict}
					if args.half:
										snake_case_			:     List[str]  	=  {k: v.half() for k, v in state_dict.items()}

					if args.use_safetensors:
										save_file(state_dict, args.checkpoint_path)
					else:
										snake_case_			:     List[str]  	=  {'state_dict': state_dict}
										torch.save(state_dict, args.checkpoint_path)










'''simple docstring'''
from __future__ import annotations

import numpy as np






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase    ,      _UpperCamelCase :		Optional[int]			    =       np.shape(UpperCAmelCase_							)
						if rows != columns:
												_UpperCamelCase :		Union[str, Any]			    =       (
												    '\'table\' has to be of square shaped array but got a '
												    f'{rows}x{columns} array:\n{table}'
												)
												raise ValueError(UpperCAmelCase_							)

						_UpperCamelCase :		Optional[Any]			    =       np.zeros((rows, columns)							)
						_UpperCamelCase :		Tuple			    =       np.zeros((rows, columns)							)
						for i in range(UpperCAmelCase_							):
												for j in range(UpperCAmelCase_							):
																		_UpperCamelCase :		Optional[Any]			    =       sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase_							)							)
																		if upper[j][j] == 0:
																								raise ArithmeticError('No LU decomposition exists'							)
																		_UpperCamelCase :		Optional[Any]			    =       (table[i][j] - total) / upper[j][j]
												_UpperCamelCase :		int			    =       1
												for j in range(UpperCAmelCase_  ,  UpperCAmelCase_							):
																		_UpperCamelCase :		Optional[int]			    =       sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase_							)							)
																		_UpperCamelCase :		Tuple			    =       table[i][j] - total
						return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()


