




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowercase__  		=		[
    # (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'''),
]

lowercase__  		=		[
    # (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'''),
]

lowercase__  		=		[]
# 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
						lowercase__  		=		F"down_blocks.{i}.resnets.{j}."
						lowercase__  		=		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
									lowercase__  		=		F"down_blocks.{i}.attentions.{j}."
									lowercase__  		=		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
						lowercase__  		=		F"up_blocks.{i}.resnets.{j}."
						lowercase__  		=		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
									lowercase__  		=		F"up_blocks.{i}.attentions.{j}."
									lowercase__  		=		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
						lowercase__  		=		F"down_blocks.{i}.downsamplers.0.conv."
						lowercase__  		=		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
						lowercase__  		=		F"up_blocks.{i}.upsamplers.0."
						lowercase__  		=		F"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
						unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowercase__  		=		'''mid_block.attentions.0.'''
lowercase__  		=		'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
			lowercase__  		=		F"mid_block.resnets.{j}."
			lowercase__  		=		F"middle_block.{2*j}."
			unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Optional[Any]					=					{k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								_lowerCamelCase  :    str					=					sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																_lowerCamelCase  :    str					=					v.replace(_lowercase							,					_lowercase	)
												_lowerCamelCase  :    Tuple					=					v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												_lowerCamelCase  :    Any					=					v.replace(_lowercase							,					_lowercase	)
								_lowerCamelCase  :    Any					=					v
				_lowerCamelCase  :    Any					=					{v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


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

lowercase__  		=		[
    # (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):
						lowercase__  		=		F"encoder.down_blocks.{i}.resnets.{j}."
						lowercase__  		=		F"encoder.down.{i}.block.{j}."
						vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

			if i < 3:
						lowercase__  		=		F"down_blocks.{i}.downsamplers.0."
						lowercase__  		=		F"down.{i}.downsample."
						vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

						lowercase__  		=		F"up_blocks.{i}.upsamplers.0."
						lowercase__  		=		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):
						lowercase__  		=		F"decoder.up_blocks.{i}.resnets.{j}."
						lowercase__  		=		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):
			lowercase__  		=		F"mid_block.resnets.{i}."
			lowercase__  		=		F"mid.block_{i+1}."
			vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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







def   _snake_case    (       lowercase__	):
				return w.reshape(*w.shape							,					1							,					1	)







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Optional[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  :    Tuple					=					v.replace(_lowercase							,					_lowercase	)
								_lowerCamelCase  :    int					=					v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																_lowerCamelCase  :    int					=					v.replace(_lowercase							,					_lowercase	)
												_lowerCamelCase  :    int					=					v
				_lowerCamelCase  :    Dict					=					{v: vae_state_dict[k] for k, v in mapping.items()}
				_lowerCamelCase  :    int					=					["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  :    Optional[int]					=					reshape_weight_for_sd(_lowercase	)
				return new_state_dict


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


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

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







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    int					=					{}
				_lowerCamelCase  :    Optional[Any]					=					{}
				_lowerCamelCase  :    Dict					=					{}
				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  :    Dict					=					k[: -len('.q_proj.weight'	)]
												_lowerCamelCase  :    str					=					k[-len('q_proj.weight'	)]
												if k_pre not in capture_qkv_weight:
																_lowerCamelCase  :    List[Any]					=					[None, None, None]
												_lowerCamelCase  :    Union[str, Any]					=					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  :    str					=					k[: -len('.q_proj.bias'	)]
												_lowerCamelCase  :    Tuple					=					k[-len('q_proj.bias'	)]
												if k_pre not in capture_qkv_bias:
																_lowerCamelCase  :    List[Any]					=					[None, None, None]
												_lowerCamelCase  :    str					=					v
												continue

								_lowerCamelCase  :    str					=					textenc_pattern.sub(lambda lowercase__	: protected[re.escape(m.group(0	)	)]							,					_lowercase	)
								_lowerCamelCase  :    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'	)
								_lowerCamelCase  :    str					=					textenc_pattern.sub(lambda lowercase__	: protected[re.escape(m.group(0	)	)]							,					_lowercase	)
								_lowerCamelCase  :    Dict					=					torch.cat(_lowercase	)

				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  :    Optional[Any]					=					textenc_pattern.sub(lambda lowercase__	: protected[re.escape(m.group(0	)	)]							,					_lowercase	)
								_lowerCamelCase  :    str					=					torch.cat(_lowercase	)

				return new_state_dict







def   _snake_case    (       lowercase__	):
				return text_enc_dict


if __name__ == "__main__":
			lowercase__  		=		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."""
			)

			lowercase__  		=		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
			lowercase__  		=		osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
			lowercase__  		=		osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
			lowercase__  		=		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):
						lowercase__  		=		load_file(unet_path, device="""cpu""")
			else:
						lowercase__  		=		osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
						lowercase__  		=		torch.load(unet_path, map_location="""cpu""")

			if osp.exists(vae_path):
						lowercase__  		=		load_file(vae_path, device="""cpu""")
			else:
						lowercase__  		=		osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
						lowercase__  		=		torch.load(vae_path, map_location="""cpu""")

			if osp.exists(text_enc_path):
						lowercase__  		=		load_file(text_enc_path, device="""cpu""")
			else:
						lowercase__  		=		osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
						lowercase__  		=		torch.load(text_enc_path, map_location="""cpu""")

			# Convert the UNet model
			lowercase__  		=		convert_unet_state_dict(unet_state_dict)
			lowercase__  		=		{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

			# Convert the VAE model
			lowercase__  		=		convert_vae_state_dict(vae_state_dict)
			lowercase__  		=		{'''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
			lowercase__  		=		'''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
						lowercase__  		=		{'''transformer.''' + k: v for k, v in text_enc_dict.items()}
						lowercase__  		=		convert_text_enc_state_dict_vaa(text_enc_dict)
						lowercase__  		=		{'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
			else:
						lowercase__  		=		convert_text_enc_state_dict(text_enc_dict)
						lowercase__  		=		{'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

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

			if args.use_safetensors:
						save_file(state_dict, args.checkpoint_path)
			else:
						lowercase__  		=		{'''state_dict''': state_dict}
						torch.save(state_dict, args.checkpoint_path)





"""simple docstring"""






from math import pow, sqrt







def   _snake_case    (       *lowercase__	):
				_lowerCamelCase  :    Dict					=					len(lowercase__	) > 0 and all(value > 0.0 for value in values	)
				return result







def   _snake_case    (       lowercase__							,					lowercase__	):
				return (
				    round(sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__	)
				    else ValueError('Input Error: Molar mass values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(molar_mass / pow(effusion_rate_a / effusion_rate_a							,					2	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(pow(effusion_rate_a / effusion_rate_a							,					2	) / molar_mass							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)