


'''simple docstring'''

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

a_  :				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"""),
]

a_  :				Optional[Any]						= [
    # (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"""),
]

a_  :				Union[str, 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
          a_  :				Union[str, Any]						= f'''down_blocks.{i}.resnets.{j}.'''
          a_  :				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
               a_  :				Optional[Any]						= f'''down_blocks.{i}.attentions.{j}.'''
               a_  :				Any						= 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
          a_  :				Optional[int]						= f'''up_blocks.{i}.resnets.{j}.'''
          a_  :				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
               a_  :				str						= f'''up_blocks.{i}.attentions.{j}.'''
               a_  :				int						= 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
          a_  :				Union[str, Any]						= f'''down_blocks.{i}.downsamplers.0.conv.'''
          a_  :				str						= 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
          a_  :				Optional[int]						= f'''up_blocks.{i}.upsamplers.0.'''
          a_  :				Any						= f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
          unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

a_  :				Optional[Any]						= """mid_block.attentions.0."""
a_  :				Any						= """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]		):
 # 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.
 lowerCamelCase_      =  {k: k for k in unet_state_dict.keys()}
 for sd_name, hf_name in unet_conversion_map:
  lowerCamelCase_      =  sd_name
 for k, v in mapping.items():
  if "resnets" in k:
   for sd_part, hf_part in unet_conversion_map_resnet:
    lowerCamelCase_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
   lowerCamelCase_      =  v
 for k, v in mapping.items():
  for sd_part, hf_part in unet_conversion_map_layer:
   lowerCamelCase_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
  lowerCamelCase_      =  v
 lowerCamelCase_      =  {v: unet_state_dict[k] for k, v in mapping.items()}
 return new_state_dict


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

a_  :				int						= [
    # (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):
          a_  :				Dict						= f'''encoder.down_blocks.{i}.resnets.{j}.'''
          a_  :				List[str]						= f'''encoder.down.{i}.block.{j}.'''
          vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

          a_  :				str						= f'''up_blocks.{i}.upsamplers.0.'''
          a_  :				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):
          a_  :				List[Any]						= f'''decoder.up_blocks.{i}.resnets.{j}.'''
          a_  :				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):
     a_  :				Optional[int]						= f'''mid_block.resnets.{i}.'''
     a_  :				List[Any]						= f'''mid.block_{i+1}.'''
     vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):
 # convert HF linear weights to SD conv2d weights
 return w.reshape(*w.shape					,				1					,				1		)





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]		):
 lowerCamelCase_      =  {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_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
  lowerCamelCase_      =  v
 for k, v in mapping.items():
  if "attentions" in k:
   for sd_part, hf_part in vae_conversion_map_attn:
    lowerCamelCase_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
   lowerCamelCase_      =  v
 lowerCamelCase_      =  {v: vae_state_dict[k] for k, v in mapping.items()}
 lowerCamelCase_      =  ["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_      =  reshape_weight_for_sd(UpperCAmelCase_		)
 return new_state_dict


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


a_  :				int						= [
    # (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"""),
]
a_  :				str						= {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
a_  :				int						= re.compile("""|""".join(protected.keys()))

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





def __snake_case					(      UpperCAmelCase_						:						Tuple		):
 lowerCamelCase_      =  {}
 lowerCamelCase_      =  {}
 lowerCamelCase_      =  {}
 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_      =  k[: -len(".q_proj.weight"		)]
   lowerCamelCase_      =  k[-len("q_proj.weight"		)]
   if k_pre not in capture_qkv_weight:
    lowerCamelCase_      =  [None, None, None]
   lowerCamelCase_      =  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_      =  k[: -len(".q_proj.bias"		)]
   lowerCamelCase_      =  k[-len("q_proj.bias"		)]
   if k_pre not in capture_qkv_bias:
    lowerCamelCase_      =  [None, None, None]
   lowerCamelCase_      =  v
   continue

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

 return new_state_dict





def __snake_case					(      UpperCAmelCase_						:						List[Any]		):
 return text_enc_dict


if __name__ == "__main__":
     a_  :				List[str]						= 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."""
     )

     a_  :				List[Any]						= 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
     a_  :				str						= osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
     a_  :				Optional[int]						= osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
     a_  :				Optional[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):
          a_  :				int						= load_file(unet_path, device="""cpu""")
     else:
          a_  :				str						= osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
          a_  :				int						= torch.load(unet_path, map_location="""cpu""")

     if osp.exists(vae_path):
          a_  :				List[Any]						= load_file(vae_path, device="""cpu""")
     else:
          a_  :				Dict						= osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
          a_  :				Tuple						= torch.load(vae_path, map_location="""cpu""")

     if osp.exists(text_enc_path):
          a_  :				Union[str, Any]						= load_file(text_enc_path, device="""cpu""")
     else:
          a_  :				Tuple						= osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
          a_  :				int						= torch.load(text_enc_path, map_location="""cpu""")

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

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

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

     if args.use_safetensors:
          save_file(state_dict, args.checkpoint_path)
     else:
          a_  :				Optional[int]						= {"""state_dict""": state_dict}
          torch.save(state_dict, args.checkpoint_path)








# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A__								=						abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				from transformers.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(__lowerCAmelCase      )





def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				from transformers.testing_utils import pytest_terminal_summary_main

				snake_case__	:      Dict     		=	terminalreporter.config.getoption('''--make-reports'''      )
				if make_reports:
								pytest_terminal_summary_main(__lowerCAmelCase  ,  id=__lowerCAmelCase      )

