


import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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

A							:					Dict     						=     'mid_block.attentions.0.'
A							:					Tuple     						=     '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							:					List[str]     						=     f'''middle_block.{2*j}.'''
	unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





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


						'''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(_lowerCAmelCase      ,	_lowerCAmelCase )
																		__snake_case				=		v
						for k, v in mapping.items():
												for sd_part, hf_part in unet_conversion_map_layer:
																		__snake_case				=		v.replace(_lowerCAmelCase      ,	_lowerCAmelCase )
												__snake_case				=		v
						__snake_case				=		{v: unet_state_dict[k] for k, v in mapping.items()}
						return new_state_dict


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

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

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

		A							:					Optional[Any]     						=     f'''up_blocks.{i}.upsamplers.0.'''
		A							:					Dict     						=     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							:					Tuple     						=     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[Any]     						=     f'''mid_block.resnets.{i}.'''
	A							:					Optional[Any]     						=     f'''mid.block_{i+1}.'''
	vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Optional[int]:


						'''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(_lowerCAmelCase      ,	_lowerCAmelCase )
												__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(_lowerCAmelCase      ,	_lowerCAmelCase )
																		__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(_lowerCAmelCase )
						return new_state_dict


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


A							:					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'),
]
A							:					List[str]     						=     {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A							:					Union[str, Any]     						=     re.compile('|'.join(protected.keys()))

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





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	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 _lowerCAmelCase : protected[re.escape(m.group(0 ) )]      ,	_lowerCAmelCase )
												__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 _lowerCAmelCase : protected[re.escape(m.group(0 ) )]      ,	_lowerCAmelCase )
												__snake_case				=		torch.cat(_lowerCAmelCase )

						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 _lowerCAmelCase : protected[re.escape(m.group(0 ) )]      ,	_lowerCAmelCase )
												__snake_case				=		torch.cat(_lowerCAmelCase )

						return new_state_dict





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Optional[int]:


						'''simple docstring'''






						return text_enc_dict


if __name__ == "__main__":
	A							:					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.'
	)

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

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

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

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

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

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

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









class     UpperCamelCase:


					def __init__(						self	:		Any						)			->	Any:




											'''simple docstring'''

											__snake_case				=		0
											__snake_case				=		0
											__snake_case				=		{}


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	Optional[Any]:




											'''simple docstring'''

											if vertex not in self.adjacency:
																	__snake_case				=		{}
																	self.num_vertices += 1


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Optional[Any]:




											'''simple docstring'''

											self.add_vertex(SCREAMING_SNAKE_CASE						)
											self.add_vertex(SCREAMING_SNAKE_CASE						)

											if head == tail:
																	return

											__snake_case				=		weight
											__snake_case				=		weight


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		self.get_edges()
											for edge in edges:
																	__snake_case		,					__snake_case		,					__snake_case				=		edge
																	edges.remove((tail, head, weight)						)
											for i in range(len(SCREAMING_SNAKE_CASE						)						):
																	__snake_case				=		list(edges[i]						)

											edges.sort(key=lambda SCREAMING_SNAKE_CASE						: e[2]						)
											for i in range(len(SCREAMING_SNAKE_CASE						) - 1						):
																	if edges[i][2] >= edges[i + 1][2]:
																							__snake_case				=		edges[i][2] + 1
											for edge in edges:
																	__snake_case		,					__snake_case		,					__snake_case				=		edge
																	__snake_case				=		weight
																	__snake_case				=		weight


					def __str__(						self	:		Tuple						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		""
											for tail in self.adjacency:
																	for head in self.adjacency[tail]:
																							__snake_case				=		self.adjacency[head][tail]
																							string += f'''{head} -> {tail} == {weight}\n'''
											return string.rstrip("\n"						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		[]
											for tail in self.adjacency:
																	for head in self.adjacency[tail]:
																							output.append((tail, head, self.adjacency[head][tail])						)
											return output


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[Any]:




											'''simple docstring'''

											return self.adjacency.keys()


					@staticmethod
					def 							SCREAMING_SNAKE_CASE_  (						SCREAMING_SNAKE_CASE	:		Any=None     ,    SCREAMING_SNAKE_CASE	:		List[Any]=None						)			->	int:




											'''simple docstring'''

											__snake_case				=		Graph()
											if vertices is None:
																	__snake_case				=		[]
											if edges is None:
																	__snake_case				=		[]
											for vertex in vertices:
																	g.add_vertex(SCREAMING_SNAKE_CASE						)
											for edge in edges:
																	g.add_edge(*SCREAMING_SNAKE_CASE						)
											return g






					class     UpperCamelCase:


										def __init__(						self	:		Union[str, Any]						)			->	Union[str, Any]:




																'''simple docstring'''

																__snake_case				=		{}
																__snake_case				=		{}


										def __len__(						self	:		List[str]						)			->	Dict:




																'''simple docstring'''

																return len(self.parent						)


										def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	List[str]:




																'''simple docstring'''

																if item in self.parent:
																						return self.find(SCREAMING_SNAKE_CASE						)

																__snake_case				=		item
																__snake_case				=		0
																return item


										def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	Any:




																'''simple docstring'''

																if item not in self.parent:
																						return self.make_set(SCREAMING_SNAKE_CASE						)
																if item != self.parent[item]:
																						__snake_case				=		self.find(self.parent[item]						)
																return self.parent[item]






										def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Dict:




																'''simple docstring'''

																__snake_case				=		self.find(SCREAMING_SNAKE_CASE						)
																__snake_case				=		self.find(SCREAMING_SNAKE_CASE						)

																if roota == roota:
																						return roota

																if self.rank[roota] > self.rank[roota]:
																						__snake_case				=		roota
																						return roota

																if self.rank[roota] < self.rank[roota]:
																						__snake_case				=		roota
																						return roota

																if self.rank[roota] == self.rank[roota]:
																						self.rank[roota] += 1
																						__snake_case				=		roota
																						return roota
																return None




					@staticmethod
					def 							SCREAMING_SNAKE_CASE_  (						SCREAMING_SNAKE_CASE	:		str						)			->	Any:




											'''simple docstring'''

											__snake_case				=		graph.num_vertices

											__snake_case				=		Graph.UnionFind()
											__snake_case				=		[]
											while num_components > 1:
																	__snake_case				=		{}
																	for vertex in graph.get_vertices():
																							__snake_case				=		-1

																	__snake_case				=		graph.get_edges()
																	for edge in edges:
																							__snake_case		,					__snake_case		,					__snake_case				=		edge
																							edges.remove((tail, head, weight)						)
																	for edge in edges:
																							__snake_case		,					__snake_case		,					__snake_case				=		edge
																							__snake_case				=		union_find.find(SCREAMING_SNAKE_CASE						)
																							__snake_case				=		union_find.find(SCREAMING_SNAKE_CASE						)
																							if seta != seta:
																													if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																			__snake_case				=		[head, tail, weight]

																													if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																			__snake_case				=		[head, tail, weight]
																	for vertex in cheap_edge:
																							if cheap_edge[vertex] != -1:
																													__snake_case		,					__snake_case		,					__snake_case				=		cheap_edge[vertex]
																													if union_find.find(SCREAMING_SNAKE_CASE						) != union_find.find(SCREAMING_SNAKE_CASE						):
																																			union_find.union(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																																			mst_edges.append(cheap_edge[vertex]						)
																																			__snake_case				=		num_components - 1
											__snake_case				=		Graph.build(edges=SCREAMING_SNAKE_CASE						)
											return mst





