






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

A__			:    List[str]         =				[
    # (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[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__			:    Tuple         =				F"down_blocks.{i}.attentions.{j}."
																					A__			:    List[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__			:    Dict         =				F"up_blocks.{i}.resnets.{j}."
														A__			:    List[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__			:    Optional[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__			:    Optional[Any]         =				F"down_blocks.{i}.downsamplers.0.conv."
														A__			:    int         =				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__			:    Dict         =				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__			:    str         =				'mid_block.attentions.0.'
A__			:    List[str]         =				'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
							A__			:    Optional[Any]         =				F"mid_block.resnets.{j}."
							A__			:    str         =				F"middle_block.{2*j}."
							unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				# 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.
				lowercase__     				=    {k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								lowercase__     				=    sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																lowercase__     				=    v.replace(lowerCamelCase_	,     lowerCamelCase_       )
												lowercase__     				=    v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												lowercase__     				=    v.replace(lowerCamelCase_	,     lowerCamelCase_       )
								lowercase__     				=    v
				lowercase__     				=    {v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


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

A__			:    Tuple         =				[
    # (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__			:    List[str]         =				F"encoder.down_blocks.{i}.resnets.{j}."
														A__			:    Dict         =				F"encoder.down.{i}.block.{j}."
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

							if i < 3:
														A__			:    int         =				F"down_blocks.{i}.downsamplers.0."
														A__			:    Optional[int]         =				F"down.{i}.downsample."
														vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

														A__			:    Any         =				F"up_blocks.{i}.upsamplers.0."
														A__			:    List[str]         =				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__			:    Union[str, Any]         =				F"decoder.up_blocks.{i}.resnets.{j}."
														A__			:    int         =				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__			:    List[str]         =				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__			:    Union[str, Any]         =				[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				# convert HF linear weights to SD conv2d weights
				return w.reshape(*w.shape	,     1	,     1       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    {k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												lowercase__     				=    v.replace(lowerCamelCase_	,     lowerCamelCase_       )
								lowercase__     				=    v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																lowercase__     				=    v.replace(lowerCamelCase_	,     lowerCamelCase_       )
												lowercase__     				=    v
				lowercase__     				=    {v: vae_state_dict[k] for k, v in mapping.items()}
				lowercase__     				=    ['''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"""       )
																lowercase__     				=    reshape_weight_for_sd(lowerCamelCase_       )
				return new_state_dict


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


A__			:    Any         =				[
    # (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__			:    Optional[int]         =				{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A__			:    Dict         =				re.compile('|'.join(protected.keys()))

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






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    {}
				lowercase__     				=    {}
				lowercase__     				=    {}
				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'''       )
								):
												lowercase__     				=    k[: -len('''.q_proj.weight'''       )]
												lowercase__     				=    k[-len('''q_proj.weight'''       )]
												if k_pre not in capture_qkv_weight:
																lowercase__     				=    [None, None, None]
												lowercase__     				=    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'''       )
								):
												lowercase__     				=    k[: -len('''.q_proj.bias'''       )]
												lowercase__     				=    k[-len('''q_proj.bias'''       )]
												if k_pre not in capture_qkv_bias:
																lowercase__     				=    [None, None, None]
												lowercase__     				=    v
												continue

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

				return new_state_dict






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return text_enc_dict


if __name__ == "__main__":
							A__			:    Any         =				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__			:    Union[str, 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__			:    Any         =				osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
							A__			:    Optional[int]         =				osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
							A__			:    str         =				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__			:    Optional[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__			:    Optional[Any]         =				load_file(vae_path, device='cpu')
							else:
														A__			:    Union[str, Any]         =				osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
														A__			:    Dict         =				torch.load(vae_path, map_location='cpu')

							if osp.exists(text_enc_path):
														A__			:    Any         =				load_file(text_enc_path, device='cpu')
							else:
														A__			:    str         =				osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
														A__			:    List[str]         =				torch.load(text_enc_path, map_location='cpu')

							# Convert the UNet model
							A__			:    Optional[Any]         =				convert_unet_state_dict(unet_state_dict)
							A__			:    Optional[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__			:    Tuple         =				{'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__			:    str         =				'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__			:    Optional[int]         =				{'transformer.' + k: v for k, v in text_enc_dict.items()}
														A__			:    Any         =				convert_text_enc_state_dict_vaa(text_enc_dict)
														A__			:    Tuple         =				{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
							else:
														A__			:    Optional[Any]         =				convert_text_enc_state_dict(text_enc_dict)
														A__			:    Optional[Any]         =				{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

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

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












from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    [[] for _ in range(lowerCamelCase						)]
								lowercase__     				=    size




				def __getitem__(  self		:					Optional[Any],						lowerCamelCase		:					int						):

								'''simple docstring'''
								return iter(self._graph[vertex]						)




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return self._size




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								if weight not in (0, 1):
												raise ValueError('''Edge weight must be either 0 or 1.'''						)

								if to_vertex < 0 or to_vertex >= self.size:
												raise ValueError('''Vertex indexes must be in [0; size).'''						)

								self._graph[from_vertex].append(Edge(lowerCamelCase,						lowerCamelCase						)						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    deque([start_vertex]						)
								lowercase__     				=    [None] * self.size
								lowercase__     				=    0

								while queue:
												lowercase__     				=    queue.popleft()
												lowercase__     				=    distances[current_vertex]
												if current_distance is None:
																continue

												for edge in self[current_vertex]:
																lowercase__     				=    current_distance + edge.weight
																lowercase__     				=    distances[edge.destination_vertex]
																if (
																    isinstance(lowerCamelCase,						lowerCamelCase						)
																    and new_distance >= dest_vertex_distance
																):
																				continue
																lowercase__     				=    new_distance
																if edge.weight == 0:
																				queue.appendleft(edge.destination_vertex						)
																else:
																				queue.append(edge.destination_vertex						)

								if distances[finish_vertex] is None:
												raise ValueError('''No path from start_vertex to finish_vertex.'''						)

								return distances[finish_vertex]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




