




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowerCAmelCase_						=			[
    # (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'),
]

lowerCAmelCase_						=			[
    # (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'),
]

lowerCAmelCase_						=			[]
# 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
  lowerCAmelCase_						=			f'''down_blocks.{i}.resnets.{j}.'''
  lowerCAmelCase_						=			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
   lowerCAmelCase_						=			f'''down_blocks.{i}.attentions.{j}.'''
   lowerCAmelCase_						=			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
  lowerCAmelCase_						=			f'''up_blocks.{i}.resnets.{j}.'''
  lowerCAmelCase_						=			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
   lowerCAmelCase_						=			f'''up_blocks.{i}.attentions.{j}.'''
   lowerCAmelCase_						=			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
  lowerCAmelCase_						=			f'''down_blocks.{i}.downsamplers.0.conv.'''
  lowerCAmelCase_						=			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
  lowerCAmelCase_						=			f'''up_blocks.{i}.upsamplers.0.'''
  lowerCAmelCase_						=			f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

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

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






def 		snake_case(    __magic_name__   )			->							int:





     '''simple docstring'''


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


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

lowerCAmelCase_						=			[
    # (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):
  lowerCAmelCase_						=			f'''encoder.down_blocks.{i}.resnets.{j}.'''
  lowerCAmelCase_						=			f'''encoder.down.{i}.block.{j}.'''
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

  lowerCAmelCase_						=			f'''up_blocks.{i}.upsamplers.0.'''
  lowerCAmelCase_						=			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):
  lowerCAmelCase_						=			f'''decoder.up_blocks.{i}.resnets.{j}.'''
  lowerCAmelCase_						=			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):
 lowerCAmelCase_						=			f'''mid_block.resnets.{i}.'''
 lowerCAmelCase_						=			f'''mid.block_{i+1}.'''
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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






def 		snake_case(    __magic_name__   )			->							List[str]:





     '''simple docstring'''


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






def 		snake_case(    __magic_name__   )			->							Union[str, Any]:





     '''simple docstring'''


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


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


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

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






def 		snake_case(    __magic_name__   )			->							str:





     '''simple docstring'''


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

          lowercase :       Any       =			textenc_pattern.sub(lambda __magic_name__   : protected[re.escape(m.group(0   )   )]			,		_A   )
          lowercase :       Optional[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'''   )
          lowercase :       Any       =			textenc_pattern.sub(lambda __magic_name__   : protected[re.escape(m.group(0   )   )]			,		_A   )
          lowercase :       Optional[int]       =			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'''   )
          lowercase :       int       =			textenc_pattern.sub(lambda __magic_name__   : protected[re.escape(m.group(0   )   )]			,		_A   )
          lowercase :       Any       =			torch.cat(_A   )

     return new_state_dict






def 		snake_case(    __magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     return text_enc_dict


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

 lowerCAmelCase_						=			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
 lowerCAmelCase_						=			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
 lowerCAmelCase_						=			osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
 lowerCAmelCase_						=			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):
  lowerCAmelCase_						=			load_file(unet_path, device='cpu')
 else:
  lowerCAmelCase_						=			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
  lowerCAmelCase_						=			torch.load(unet_path, map_location='cpu')

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

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

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

 # Convert the VAE model
 lowerCAmelCase_						=			convert_vae_state_dict(vae_state_dict)
 lowerCAmelCase_						=			{'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
 lowerCAmelCase_						=			'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
  lowerCAmelCase_						=			{'transformer.' + k: v for k, v in text_enc_dict.items()}
  lowerCAmelCase_						=			convert_text_enc_state_dict_vaa(text_enc_dict)
  lowerCAmelCase_						=			{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
 else:
  lowerCAmelCase_						=			convert_text_enc_state_dict(text_enc_dict)
  lowerCAmelCase_						=			{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

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

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





import math






def  snake_case(    __magic_name__   )			->							bool:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

     return exponent == int(__magic_name__   )






def  snake_case(    __magic_name__ = 1 / 1_23_45   )			->							int:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			0
     lowercase :       str       =			0

     lowercase :       Optional[int]       =			3
     while True:
          lowercase :       Any       =			(integer**2 - 1) / 4
          # if candidate is an integer, then there is a partition for k
          if partition_candidate == int(__magic_name__   ):
               lowercase :       Any       =			int(__magic_name__   )
               total_partitions += 1
               if check_partition_perfect(__magic_name__   ):
                    perfect_partitions += 1
          if perfect_partitions > 0:
               if perfect_partitions / total_partitions < max_proportion:
                    return int(__magic_name__   )
          integer += 1


if __name__ == "__main__":
 print(f'''{solution() = }''')