





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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

UpperCAmelCase_     							= """mid_block.attentions.0."""
UpperCAmelCase_     							= """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]      )      ->	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.
    UpperCamelCase__							:				Optional[Any]          =      {k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        UpperCamelCase__							:				Any          =      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(UpperCAmelCase_		,   UpperCAmelCase_      )
            UpperCamelCase__							:				Optional[int]          =      v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            UpperCamelCase__							:				List[str]          =      v.replace(UpperCAmelCase_		,   UpperCAmelCase_      )
        UpperCamelCase__							:				List[Any]          =      v
    UpperCamelCase__							:				int          =      {v: unet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


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

UpperCAmelCase_     							= [
    # (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_     							= F'''encoder.down_blocks.{i}.resnets.{j}.'''
    UpperCAmelCase_     							= F'''encoder.down.{i}.block.{j}.'''
    vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

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


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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[int]      )      ->	Optional[Any]:
    # convert HF linear weights to SD conv2d weights
    return w.reshape(*w.shape		,   1		,   1      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]      )      ->	Union[str, Any]:
    UpperCamelCase__							:				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:
            UpperCamelCase__							:				str          =      v.replace(UpperCAmelCase_		,   UpperCAmelCase_      )
        UpperCamelCase__							:				str          =      v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                UpperCamelCase__							:				int          =      v.replace(UpperCAmelCase_		,   UpperCAmelCase_      )
            UpperCamelCase__							:				List[str]          =      v
    UpperCamelCase__							:				str          =      {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__							:				Any          =      reshape_weight_for_sd(UpperCAmelCase_      )
    return new_state_dict


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


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

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple      )      ->	Tuple:
    UpperCamelCase__							:				Dict          =      {}
    UpperCamelCase__							:				Dict          =      {}
    UpperCamelCase__							:				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'''      )
        ):
            UpperCamelCase__							:				Any          =      k[: -len('''.q_proj.weight'''      )]
            UpperCamelCase__							:				Optional[Any]          =      k[-len('''q_proj.weight'''      )]
            if k_pre not in capture_qkv_weight:
                UpperCamelCase__							:				str          =      [None, None, None]
            UpperCamelCase__							:				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'''      )
        ):
            UpperCamelCase__							:				Tuple          =      k[: -len('''.q_proj.bias'''      )]
            UpperCamelCase__							:				int          =      k[-len('''q_proj.bias'''      )]
            if k_pre not in capture_qkv_bias:
                UpperCamelCase__							:				Any          =      [None, None, None]
            UpperCamelCase__							:				Dict          =      v
            continue

        UpperCamelCase__							:				List[str]          =      textenc_pattern.sub(lambda __UpperCAmelCase      : protected[re.escape(m.group(0      )      )]		,   UpperCAmelCase_      )
        UpperCamelCase__							:				Union[str, 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__							:				List[str]          =      textenc_pattern.sub(lambda __UpperCAmelCase      : protected[re.escape(m.group(0      )      )]		,   UpperCAmelCase_      )
        UpperCamelCase__							:				Union[str, Any]          =      torch.cat(UpperCAmelCase_      )

    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__							:				List[str]          =      textenc_pattern.sub(lambda __UpperCAmelCase      : protected[re.escape(m.group(0      )      )]		,   UpperCAmelCase_      )
        UpperCamelCase__							:				List[Any]          =      torch.cat(UpperCAmelCase_      )

    return new_state_dict




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[Any]      )      ->	Optional[Any]:
    return text_enc_dict


if __name__ == "__main__":
  UpperCAmelCase_     							= 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_     							= 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_     							= osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
  UpperCAmelCase_     							= osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
  UpperCAmelCase_     							= 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_     							= load_file(unet_path, device='cpu')
  else:
    UpperCAmelCase_     							= osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
    UpperCAmelCase_     							= torch.load(unet_path, map_location='cpu')

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

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

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

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

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

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





'''simple docstring'''

a_  :				str						= """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

a_  :				Any						= [{"""type""": """code""", """content""": INSTALL_CONTENT}]
a_  :				int						= {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}



