

'''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 						UpperCamelCase	(			a			)				->     Dict:






     '''simple docstring'''
     # 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.
     __magic_name__						       =    {k: k for k in unet_state_dict.keys()}
     for sd_name, hf_name in unet_conversion_map:
          __magic_name__						       =    sd_name
     for k, v in mapping.items():
          if "resnets" in k:
               for sd_part, hf_part in unet_conversion_map_resnet:
                    __magic_name__						       =    v.replace(a		,						a			)
               __magic_name__						       =    v
     for k, v in mapping.items():
          for sd_part, hf_part in unet_conversion_map_layer:
               __magic_name__						       =    v.replace(a		,						a			)
          __magic_name__						       =    v
     __magic_name__						       =    {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 						UpperCamelCase	(			a			)				->     Dict:






     '''simple docstring'''
     # convert HF linear weights to SD conv2d weights
     return w.reshape(*w.shape		,						1		,						1			)




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    {k: k for k in vae_state_dict.keys()}
     for k, v in mapping.items():
          for sd_part, hf_part in vae_conversion_map:
               __magic_name__						       =    v.replace(a		,						a			)
          __magic_name__						       =    v
     for k, v in mapping.items():
          if "attentions" in k:
               for sd_part, hf_part in vae_conversion_map_attn:
                    __magic_name__						       =    v.replace(a		,						a			)
               __magic_name__						       =    v
     __magic_name__						       =    {v: vae_state_dict[k] for k, v in mapping.items()}
     __magic_name__						       =    ['''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'''			)
                    __magic_name__						       =    reshape_weight_for_sd(a			)
     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 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    {}
     __magic_name__						       =    {}
     __magic_name__						       =    {}
     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'''			)
          ):
               __magic_name__						       =    k[: -len('''.q_proj.weight'''			)]
               __magic_name__						       =    k[-len('''q_proj.weight'''			)]
               if k_pre not in capture_qkv_weight:
                    __magic_name__						       =    [None, None, None]
               __magic_name__						       =    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'''			)
          ):
               __magic_name__						       =    k[: -len('''.q_proj.bias'''			)]
               __magic_name__						       =    k[-len('''q_proj.bias'''			)]
               if k_pre not in capture_qkv_bias:
                    __magic_name__						       =    [None, None, None]
               __magic_name__						       =    v
               continue

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

     return new_state_dict




def 						UpperCamelCase	(			a			)				->     int:






     '''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'''




from pathlib import Path

import fire
from tqdm import tqdm




def 						UpperCamelCase	(			a="ro"		,						a="en"		,						a="wmt16"		,						a=None			)				->     None:






     '''simple docstring'''
     try:
          import datasets
     except (ModuleNotFoundError, ImportError):
          raise ImportError('''run pip install datasets'''			)
     __magic_name__						       =    F'''{src_lang}-{tgt_lang}'''
     print(F'''Converting {dataset}-{pair}'''			)
     __magic_name__						       =    datasets.load_dataset(a		,						a			)
     if save_dir is None:
          __magic_name__						       =    F'''{dataset}-{pair}'''
     __magic_name__						       =    Path(a			)
     save_dir.mkdir(exist_ok=a			)

     for split in ds.keys():
          print(F'''Splitting {split} with {ds[split].num_rows} records'''			)

          # to save to val.source, val.target like summary datasets
          __magic_name__						       =    '''val''' if split == '''validation''' else split
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.source'''			)
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.target'''			)
          __magic_name__						       =    src_path.open('''w+'''			)
          __magic_name__						       =    tgt_path.open('''w+'''			)

          # reader is the bottleneck so writing one record at a time doesn't slow things down
          for x in tqdm(ds[split]			):
               __magic_name__						       =    x['''translation''']
               src_fp.write(ex[src_lang] + '''\n'''			)
               tgt_fp.write(ex[tgt_lang] + '''\n'''			)

     print(F'''Saved {dataset} dataset to {save_dir}'''			)


if __name__ == "__main__":
      fire.Fire(download_wmt_dataset)






