




'''simple docstring'''





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

UpperCamelCase_						:      Optional[int]       =						[
    # (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_						:      Optional[int]       =						[
    # (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_						:      Optional[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_						:      str       =						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_						:      Tuple       =						F'down_blocks.{i}.attentions.{j}.'
         UpperCamelCase_						:      Tuple       =						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_						:      str       =						F'up_blocks.{i}.resnets.{j}.'
      UpperCamelCase_						:      List[str]       =						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_						:      Tuple       =						F'up_blocks.{i}.attentions.{j}.'
         UpperCamelCase_						:      List[Any]       =						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_						:      Optional[int]       =						F'down_blocks.{i}.downsamplers.0.conv.'
      UpperCamelCase_						:      List[Any]       =						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_						:      Union[str, 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_						:      List[str]       =						'''mid_block.attentions.0.'''
UpperCamelCase_						:      Dict       =						'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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





def      __a     (       _UpperCamelCase:   Union[str, Any]   )						->			int:

     """simple docstring"""




     _snake_case							=				{k: k for k in unet_state_dict.keys()}
     for sd_name, hf_name in unet_conversion_map:
          _snake_case							=				sd_name
     for k, v in mapping.items():
          if "resnets" in k:
               for sd_part, hf_part in unet_conversion_map_resnet:
                    _snake_case							=				v.replace(_UpperCamelCase ,						_UpperCamelCase   )
               _snake_case							=				v
     for k, v in mapping.items():
          for sd_part, hf_part in unet_conversion_map_layer:
               _snake_case							=				v.replace(_UpperCamelCase ,						_UpperCamelCase   )
          _snake_case							=				v
     _snake_case							=				{v: unet_state_dict[k] for k, v in mapping.items()}
     return new_state_dict


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

UpperCamelCase_						:      Dict       =						[
    # (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_						:      List[str]       =						F'encoder.down_blocks.{i}.resnets.{j}.'
      UpperCamelCase_						:      Optional[Any]       =						F'encoder.down.{i}.block.{j}.'
      vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

      UpperCamelCase_						:      str       =						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_						:      Optional[Any]       =						F'decoder.up_blocks.{i}.resnets.{j}.'
      UpperCamelCase_						:      Dict       =						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_						:      Tuple       =						F'mid_block.resnets.{i}.'
   UpperCamelCase_						:      List[Any]       =						F'mid.block_{i+1}.'
   vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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





def      __a     (       _UpperCamelCase:   Tuple   )						->			int:

     """simple docstring"""




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





def      __a     (       _UpperCamelCase:   Optional[int]   )						->			Tuple:

     """simple docstring"""




     _snake_case							=				{k: k for k in vae_state_dict.keys()}
     for k, v in mapping.items():
          for sd_part, hf_part in vae_conversion_map:
               _snake_case							=				v.replace(_UpperCamelCase ,						_UpperCamelCase   )
          _snake_case							=				v
     for k, v in mapping.items():
          if "attentions" in k:
               for sd_part, hf_part in vae_conversion_map_attn:
                    _snake_case							=				v.replace(_UpperCamelCase ,						_UpperCamelCase   )
               _snake_case							=				v
     _snake_case							=				{v: vae_state_dict[k] for k, v in mapping.items()}
     _snake_case							=				["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"""   )
                    _snake_case							=				reshape_weight_for_sd(_UpperCamelCase   )
     return new_state_dict


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


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

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





def      __a     (       _UpperCamelCase:   str   )						->			Union[str, Any]:

     """simple docstring"""




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

          _snake_case							=				textenc_pattern.sub(lambda _UpperCamelCase   : protected[re.escape(m.group(0   )   )] ,						_UpperCamelCase   )
          _snake_case							=				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"   )
          _snake_case							=				textenc_pattern.sub(lambda _UpperCamelCase   : protected[re.escape(m.group(0   )   )] ,						_UpperCamelCase   )
          _snake_case							=				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"   )
          _snake_case							=				textenc_pattern.sub(lambda _UpperCamelCase   : protected[re.escape(m.group(0   )   )] ,						_UpperCamelCase   )
          _snake_case							=				torch.cat(_UpperCamelCase   )

     return new_state_dict





def      __a     (       _UpperCamelCase:   Tuple   )						->			Dict:

     """simple docstring"""




     return text_enc_dict


if __name__ == "__main__":
   UpperCamelCase_						:      Optional[int]       =						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_						:      Dict       =						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_						:      Optional[int]       =						osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
   UpperCamelCase_						:      Optional[int]       =						osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
   UpperCamelCase_						:      Union[str, Any]       =						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_						:      Dict       =						load_file(unet_path, device='''cpu''')
   else:
      UpperCamelCase_						:      Tuple       =						osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
      UpperCamelCase_						:      Optional[int]       =						torch.load(unet_path, map_location='''cpu''')

   if osp.exists(vae_path):
      UpperCamelCase_						:      Dict       =						load_file(vae_path, device='''cpu''')
   else:
      UpperCamelCase_						:      int       =						osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
      UpperCamelCase_						:      List[str]       =						torch.load(vae_path, map_location='''cpu''')

   if osp.exists(text_enc_path):
      UpperCamelCase_						:      Tuple       =						load_file(text_enc_path, device='''cpu''')
   else:
      UpperCamelCase_						:      Optional[int]       =						osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
      UpperCamelCase_						:      Optional[int]       =						torch.load(text_enc_path, map_location='''cpu''')

   # Convert the UNet model
   UpperCamelCase_						:      List[str]       =						convert_unet_state_dict(unet_state_dict)
   UpperCamelCase_						:      int       =						{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

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

   # Put together new checkpoint
   UpperCamelCase_						:      Dict       =						{**unet_state_dict, **vae_state_dict, **text_enc_dict}
   if args.half:
      UpperCamelCase_						:      Union[str, Any]       =						{k: v.half() for k, v in state_dict.items()}

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








'''simple docstring'''





import fire

from utils import calculate_rouge, save_json





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[int]=None ,						**_UpperCamelCase:   Any   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()]
     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()][: len(_UpperCamelCase   )]
     _snake_case							=				calculate_rouge(_UpperCamelCase ,						_UpperCamelCase ,						**_UpperCamelCase   )
     if save_path is not None:
          save_json(_UpperCamelCase ,						_UpperCamelCase ,						indent=_UpperCamelCase   )
     return metrics  # these print nicely


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


