





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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__								=   {k: k for k in unet_state_dict.keys()}
		for sd_name, hf_name in unet_conversion_map:
				SCREAMING_SNAKE_CASE__								=   sd_name
		for k, v in mapping.items():
				if "resnets" in k:
						for sd_part, hf_part in unet_conversion_map_resnet:
								SCREAMING_SNAKE_CASE__								=   v.replace(__a       , __a     )
						SCREAMING_SNAKE_CASE__								=   v
		for k, v in mapping.items():
				for sd_part, hf_part in unet_conversion_map_layer:
						SCREAMING_SNAKE_CASE__								=   v.replace(__a       , __a     )
				SCREAMING_SNAKE_CASE__								=   v
		SCREAMING_SNAKE_CASE__								=   {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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any     ):
		return w.reshape(*w.shape       , 1       , 1     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int     ):
		SCREAMING_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:
						SCREAMING_SNAKE_CASE__								=   v.replace(__a       , __a     )
				SCREAMING_SNAKE_CASE__								=   v
		for k, v in mapping.items():
				if "attentions" in k:
						for sd_part, hf_part in vae_conversion_map_attn:
								SCREAMING_SNAKE_CASE__								=   v.replace(__a       , __a     )
						SCREAMING_SNAKE_CASE__								=   v
		SCREAMING_SNAKE_CASE__								=   {v: vae_state_dict[k] for k, v in mapping.items()}
		SCREAMING_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'''     )
								SCREAMING_SNAKE_CASE__								=   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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int     ):
		SCREAMING_SNAKE_CASE__								=   {}
		SCREAMING_SNAKE_CASE__								=   {}
		SCREAMING_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"""     )
				):
						SCREAMING_SNAKE_CASE__								=   k[: -len(""".q_proj.weight"""     )]
						SCREAMING_SNAKE_CASE__								=   k[-len("""q_proj.weight"""     )]
						if k_pre not in capture_qkv_weight:
								SCREAMING_SNAKE_CASE__								=   [None, None, None]
						SCREAMING_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"""     )
				):
						SCREAMING_SNAKE_CASE__								=   k[: -len(""".q_proj.bias"""     )]
						SCREAMING_SNAKE_CASE__								=   k[-len("""q_proj.bias"""     )]
						if k_pre not in capture_qkv_bias:
								SCREAMING_SNAKE_CASE__								=   [None, None, None]
						SCREAMING_SNAKE_CASE__								=   v
						continue

				SCREAMING_SNAKE_CASE__								=   textenc_pattern.sub(lambda UpperCamelCase__     : protected[re.escape(m.group(0     )     )]       , __a     )
				SCREAMING_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"""     )
				SCREAMING_SNAKE_CASE__								=   textenc_pattern.sub(lambda UpperCamelCase__     : protected[re.escape(m.group(0     )     )]       , __a     )
				SCREAMING_SNAKE_CASE__								=   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"""     )
				SCREAMING_SNAKE_CASE__								=   textenc_pattern.sub(lambda UpperCamelCase__     : protected[re.escape(m.group(0     )     )]       , __a     )
				SCREAMING_SNAKE_CASE__								=   torch.cat(__a     )

		return new_state_dict






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any     ):
		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"""







def     UpperCAmelCase_					(				__a   :			float      ,							__a   :			float						):




			'''simple docstring'''



			if density <= 0:
						raise ValueError('Impossible fluid density'						)
			if bulk_modulus <= 0:
						raise ValueError('Impossible bulk modulus'						)

			return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
	import doctest

	doctest.testmod()





