





'''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      __A						(							a_						:  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.
		lowerCAmelCase     :    Union[str, Any]						=   {k: k for k in unet_state_dict.keys()}
		for sd_name, hf_name in unet_conversion_map:
				lowerCAmelCase     :    Optional[Any]						=   sd_name
		for k, v in mapping.items():
				if "resnets" in k:
						for sd_part, hf_part in unet_conversion_map_resnet:
								lowerCAmelCase     :    Tuple						=   v.replace(a_      ,a_	)
						lowerCAmelCase     :    Union[str, Any]						=   v
		for k, v in mapping.items():
				for sd_part, hf_part in unet_conversion_map_layer:
						lowerCAmelCase     :    Optional[Any]						=   v.replace(a_      ,a_	)
				lowerCAmelCase     :    Any						=   v
		lowerCAmelCase     :    Optional[Any]						=   {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      __A						(							a_						:  Optional[int]	):
		# convert HF linear weights to SD conv2d weights
		return w.reshape(*w.shape      ,1      ,1	)






def      __A						(							a_						:  Any	):
		lowerCAmelCase     :    int						=   {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     :    Any						=   v.replace(a_      ,a_	)
				lowerCAmelCase     :    List[str]						=   v
		for k, v in mapping.items():
				if "attentions" in k:
						for sd_part, hf_part in vae_conversion_map_attn:
								lowerCAmelCase     :    Union[str, Any]						=   v.replace(a_      ,a_	)
						lowerCAmelCase     :    Dict						=   v
		lowerCAmelCase     :    str						=   {v: vae_state_dict[k] for k, v in mapping.items()}
		lowerCAmelCase     :    Union[str, Any]						=   ["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     :    Any						=   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      __A						(							a_						:  str	):
		lowerCAmelCase     :    Any						=   {}
		lowerCAmelCase     :    Optional[int]						=   {}
		lowerCAmelCase     :    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"	)
				):
						lowerCAmelCase     :    Union[str, Any]						=   k[: -len(".q_proj.weight"	)]
						lowerCAmelCase     :    Optional[Any]						=   k[-len("q_proj.weight"	)]
						if k_pre not in capture_qkv_weight:
								lowerCAmelCase     :    str						=   [None, None, None]
						lowerCAmelCase     :    Dict						=   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     :    Optional[int]						=   k[: -len(".q_proj.bias"	)]
						lowerCAmelCase     :    Dict						=   k[-len("q_proj.bias"	)]
						if k_pre not in capture_qkv_bias:
								lowerCAmelCase     :    Optional[Any]						=   [None, None, None]
						lowerCAmelCase     :    Tuple						=   v
						continue

				lowerCAmelCase     :    Union[str, Any]						=   textenc_pattern.sub(lambda a_	: protected[re.escape(m.group(0	)	)]      ,a_	)
				lowerCAmelCase     :    List[str]						=   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     :    int						=   textenc_pattern.sub(lambda a_	: protected[re.escape(m.group(0	)	)]      ,a_	)
				lowerCAmelCase     :    Any						=   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"	)
				lowerCAmelCase     :    Dict						=   textenc_pattern.sub(lambda a_	: protected[re.escape(m.group(0	)	)]      ,a_	)
				lowerCAmelCase     :    List[Any]						=   torch.cat(a_	)

		return new_state_dict






def      __A						(							a_						:  Tuple	):
		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'''






import numpy as np






def      __A						(							a_						:  np.array	):
		return (2 / (1 + np.exp(-2 * vector	))) - 1


if __name__ == "__main__":
					import doctest

					doctest.testmod()



