

"""simple docstring"""






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 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    {k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        _lowerCAmelCase       :   Any  =    sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                _lowerCAmelCase       :   Tuple  =    v.replace(__snake_case ,   __snake_case   )
            _lowerCAmelCase       :   Tuple  =    v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            _lowerCAmelCase       :   List[Any]  =    v.replace(__snake_case ,   __snake_case   )
        _lowerCAmelCase       :   Union[str, Any]  =    v
    _lowerCAmelCase       :   Dict  =    {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 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



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

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   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:
            _lowerCAmelCase       :   str  =    v.replace(__snake_case ,   __snake_case   )
        _lowerCAmelCase       :   List[Any]  =    v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                _lowerCAmelCase       :   Optional[Any]  =    v.replace(__snake_case ,   __snake_case   )
            _lowerCAmelCase       :   Optional[Any]  =    v
    _lowerCAmelCase       :   str  =    {v: vae_state_dict[k] for k, v in mapping.items()}
    _lowerCAmelCase       :   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"""   )
                _lowerCAmelCase       :   Optional[int]  =    reshape_weight_for_sd(__snake_case   )
    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 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



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

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

    return new_state_dict

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''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)








"""simple docstring"""






import logging
import os

from .state import PartialState







class 	__UpperCamelCase    (    logging.LoggerAdapter						):

  @staticmethod
  def __lowerCamelCase		(		_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    PartialState()
      return not main_process_only or (main_process_only and state.is_main_process)







  def __lowerCamelCase		(		self    ,_A    ,_A    ,*_A    ,**_A      ):
      '''simple docstring'''

      if PartialState._shared_state == {}:
          raise RuntimeError(
              'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'      )
      _lowerCAmelCase       :   Tuple  =    kwargs.pop('main_process_only'    ,_A      )
      _lowerCAmelCase       :   Any  =    kwargs.pop('in_order'    ,_A      )

      if self.isEnabledFor(_A      ):
          if self._should_log(_A      ):
              _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.process(_A    ,_A      )
              self.logger.log(_A    ,_A    ,*_A    ,**_A      )

          elif in_order:
              _lowerCAmelCase       :   str  =    PartialState()
              for i in range(state.num_processes      ):
                  if i == state.process_index:
                      _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.process(_A    ,_A      )
                      self.logger.log(_A    ,_A    ,*_A    ,**_A      )
                  state.wait_for_everyone()





def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase = None   ):

    '''simple docstring'''



    if log_level is None:
        _lowerCAmelCase       :   Union[str, Any]  =    os.environ.get('ACCELERATE_LOG_LEVEL' ,   _lowerCamelCase   )
    _lowerCAmelCase       :   int  =    logging.getLogger(_lowerCamelCase   )
    if log_level is not None:
        logger.setLevel(log_level.upper()   )
        logger.root.setLevel(log_level.upper()   )
    return MultiProcessAdapter(_lowerCamelCase ,   {}   )





