'''simple docstring'''






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

_UpperCAmelCase    :     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'''),
]

_UpperCAmelCase    :     Tuple         =       [
    # (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'''),
]

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

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

for j in range(2):
   _UpperCAmelCase    :     List[str]         =       F'''mid_block.resnets.{j}.'''
   _UpperCAmelCase    :     List[str]         =       F'''middle_block.{2*j}.'''
   unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]				):
     # 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.
     _A    		=  {k: k for k in unet_state_dict.keys()}
     for sd_name, hf_name in unet_conversion_map:
          _A    		=  sd_name
     for k, v in mapping.items():
          if "resnets" in k:
               for sd_part, hf_part in unet_conversion_map_resnet:
                    _A    		=  v.replace(__snake_case   ,		__snake_case				)
               _A    		=  v
     for k, v in mapping.items():
          for sd_part, hf_part in unet_conversion_map_layer:
               _A    		=  v.replace(__snake_case   ,		__snake_case				)
          _A    		=  v
     _A    		=  {v: unet_state_dict[k] for k, v in mapping.items()}
     return new_state_dict


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

_UpperCAmelCase    :     Tuple         =       [
    # (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):
      _UpperCAmelCase    :     int         =       F'''encoder.down_blocks.{i}.resnets.{j}.'''
      _UpperCAmelCase    :     List[Any]         =       F'''encoder.down.{i}.block.{j}.'''
      vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

   if i < 3:
      _UpperCAmelCase    :     int         =       F'''down_blocks.{i}.downsamplers.0.'''
      _UpperCAmelCase    :     List[Any]         =       F'''down.{i}.downsample.'''
      vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

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


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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[Any]				):
     # convert HF linear weights to SD conv2d weights
     return w.reshape(*w.shape   ,		1   ,		1				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict				):
     _A    		=  {k: k for k in vae_state_dict.keys()}
     for k, v in mapping.items():
          for sd_part, hf_part in vae_conversion_map:
               _A    		=  v.replace(__snake_case   ,		__snake_case				)
          _A    		=  v
     for k, v in mapping.items():
          if "attentions" in k:
               for sd_part, hf_part in vae_conversion_map_attn:
                    _A    		=  v.replace(__snake_case   ,		__snake_case				)
               _A    		=  v
     _A    		=  {v: vae_state_dict[k] for k, v in mapping.items()}
     _A    		=  ['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'				)
                    _A    		=  reshape_weight_for_sd(__snake_case				)
     return new_state_dict


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


_UpperCAmelCase    :     Optional[int]         =       [
    # (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'''),
]
_UpperCAmelCase    :     List[str]         =       {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_UpperCAmelCase    :     Optional[Any]         =       re.compile('''|'''.join(protected.keys()))

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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[Any]				):
     _A    		=  {}
     _A    		=  {}
     _A    		=  {}
     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'				)
          ):
               _A    		=  k[: -len('.q_proj.weight'				)]
               _A    		=  k[-len('q_proj.weight'				)]
               if k_pre not in capture_qkv_weight:
                    _A    		=  [None, None, None]
               _A    		=  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'				)
          ):
               _A    		=  k[: -len('.q_proj.bias'				)]
               _A    		=  k[-len('q_proj.bias'				)]
               if k_pre not in capture_qkv_bias:
                    _A    		=  [None, None, None]
               _A    		=  v
               continue

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

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

     return new_state_dict





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict				):
     return text_enc_dict


if __name__ == "__main__":
   _UpperCAmelCase    :     List[str]         =       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.'''
   )

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

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

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

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

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

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

   if args.use_safetensors:
      save_file(state_dict, args.checkpoint_path)
   else:
      _UpperCAmelCase    :     Optional[Any]         =       {'''state_dict''': state_dict}
      torch.save(state_dict, args.checkpoint_path)











def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     len(lowerCamelCase_							)
				lowercase__  					=     [[False] * (required_sum + 1) for _ in range(arr_len + 1							)]

				# for each arr value, a sum of zero(0) can be formed by not taking any element
				# hence True/1
				for i in range(arr_len + 1							):
								lowercase__  					=     True

				# sum is not zero and set is empty then false
				for i in range(1						,  required_sum + 1							):
								lowercase__  					=     False

				for i in range(1						,  arr_len + 1							):
								for j in range(1						,  required_sum + 1							):
												if arr[i - 1] > j:
																lowercase__  					=     subset[i - 1][j]
												if arr[i - 1] <= j:
																lowercase__  					=     subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

				return subset[arr_len][required_sum]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




