





'''simple docstring'''

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 							snake_case					(     a_							: int	)       ->				Optional[Any]:





      """simple docstring"""

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





      """simple docstring"""

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



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





      """simple docstring"""

      UpperCamelCase_				:   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(a_    ,   a_	)
            UpperCamelCase_				:   List[Any]					    =							v
      for k, v in mapping.items():
            if "attentions" in k:
                  for sd_part, hf_part in vae_conversion_map_attn:
                        UpperCamelCase_				:   Optional[Any]					    =							v.replace(a_    ,   a_	)
                  UpperCamelCase_				:   List[str]					    =							v
      UpperCamelCase_				:   Optional[Any]					    =							{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_				:   Tuple					    =							reshape_weight_for_sd(a_	)
      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 							snake_case					(     a_							: List[str]	)       ->				Optional[Any]:





      """simple docstring"""

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

            UpperCamelCase_				:   Optional[Any]					    =							textenc_pattern.sub(lambda a_	: protected[re.escape(m.group(0	)	)]    ,   a_	)
            UpperCamelCase_				:   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"""	)
            UpperCamelCase_				:   Dict					    =							textenc_pattern.sub(lambda a_	: protected[re.escape(m.group(0	)	)]    ,   a_	)
            UpperCamelCase_				:   Dict					    =							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"""	)
            UpperCamelCase_				:   Dict					    =							textenc_pattern.sub(lambda a_	: protected[re.escape(m.group(0	)	)]    ,   a_	)
            UpperCamelCase_				:   Tuple					    =							torch.cat(a_	)

      return new_state_dict



def 							snake_case					(     a_							: List[Any]	)       ->				int:





      """simple docstring"""

      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'''

import random



def 							snake_case					(     a_							: int    ,   a_							: float    ,   a_							: bool = False	)       ->				dict:





      """simple docstring"""

      UpperCamelCase_				:   dict					    =							{i: [] for i in range(a_	)}

      # if probability is greater or equal than 1, then generate a complete graph
      if probability >= 1:
            return complete_graph(a_	)
      # if probability is lower or equal than 0, then return a graph without edges
      if probability <= 0:
            return graph

      # for each couple of nodes, add an edge from u to v
      # if the number randomly generated is greater than probability probability
      for i in range(a_	):
            for j in range(i + 1    ,   a_	):
                  if random.random() < probability:
                        graph[i].append(a_	)
                        if not directed:
                              # if the graph is undirected, add an edge in from j to i, either
                              graph[j].append(a_	)
      return graph



def 							snake_case					(     a_							: int	)       ->				dict:





      """simple docstring"""

      return {
          i: [j for j in range(a_	) if i != j] for i in range(a_	)
      }


if __name__ == "__main__":
      import doctest

      doctest.testmod()






