






'''simple docstring'''






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        _SCREAMING_SNAKE_CASE  (				A  :       Dict  )    -> Optional[int]:







   """simple docstring"""
   __snake_case				:      Optional[int]     							= {k: k for k in unet_state_dict.keys()}
   for sd_name, hf_name in unet_conversion_map:
      __snake_case				:      Tuple     							= sd_name
   for k, v in mapping.items():
      if "resnets" in k:
         for sd_part, hf_part in unet_conversion_map_resnet:
            __snake_case				:      Any     							= v.replace(lowercase__  ,		lowercase__  )
         __snake_case				:      str     							= v
   for k, v in mapping.items():
      for sd_part, hf_part in unet_conversion_map_layer:
         __snake_case				:      Optional[int]     							= v.replace(lowercase__  ,		lowercase__  )
      __snake_case				:      Optional[Any]     							= v
   __snake_case				:      List[Any]     							= {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        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  )    -> Union[str, Any]:







   """simple docstring"""
   return w.reshape(*w.shape  ,		1  ,		1  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  )    -> Optional[int]:







   """simple docstring"""
   __snake_case				:      Optional[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:
         __snake_case				:      int     							= v.replace(lowercase__  ,		lowercase__  )
      __snake_case				:      Dict     							= v
   for k, v in mapping.items():
      if "attentions" in k:
         for sd_part, hf_part in vae_conversion_map_attn:
            __snake_case				:      Tuple     							= v.replace(lowercase__  ,		lowercase__  )
         __snake_case				:      str     							= v
   __snake_case				:      Dict     							= {v: vae_state_dict[k] for k, v in mapping.items()}
   __snake_case				:      Tuple     							= ['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"""  )
            __snake_case				:      Dict     							= reshape_weight_for_sd(lowercase__  )
   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        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> Optional[int]:







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

      __snake_case				:      Dict     							= textenc_pattern.sub(lambda A  : protected[re.escape(m.group(0  )  )]  ,		lowercase__  )
      __snake_case				:      str     							= 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'  )
      __snake_case				:      Union[str, Any]     							= textenc_pattern.sub(lambda A  : protected[re.escape(m.group(0  )  )]  ,		lowercase__  )
      __snake_case				:      Optional[Any]     							= torch.cat(lowercase__  )

   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'  )
      __snake_case				:      str     							= textenc_pattern.sub(lambda A  : protected[re.escape(m.group(0  )  )]  ,		lowercase__  )
      __snake_case				:      Any     							= torch.cat(lowercase__  )

   return new_state_dict




def        _SCREAMING_SNAKE_CASE  (				A  :       List[str]  )    -> Tuple:







   """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)







'''simple docstring'''






def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       list[float]  )    -> float:







   """simple docstring"""
   if discount_rate < 0:
      raise ValueError('Discount rate cannot be negative'  )
   if not cash_flows:
      raise ValueError('Cash flows list cannot be empty'  )
   __snake_case				:      List[str]     							= sum(
       cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(A  )  )
   return round(A  ,		ndigits=2  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()