"""simple docstring"""






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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

_A        =     """mid_block.attentions.0."""
_A        =     """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> str:
				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(_UpperCAmelCase	,       _UpperCAmelCase							)
												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(_UpperCAmelCase	,       _UpperCAmelCase							)
								SCREAMING_SNAKE_CASE__			=					v
				SCREAMING_SNAKE_CASE__			=					{v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


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

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

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

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


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




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> Dict:
				return w.reshape(*w.shape	,       1	,       1							)




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> List[str]:
				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(_UpperCAmelCase	,       _UpperCAmelCase							)
								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(_UpperCAmelCase	,       _UpperCAmelCase							)
												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(_UpperCAmelCase							)
				return new_state_dict


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


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

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




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> Any:
				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							)							)]	,       _UpperCAmelCase							)
								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							)							)]	,       _UpperCAmelCase							)
								SCREAMING_SNAKE_CASE__			=					torch.cat(_UpperCAmelCase							)

				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							)							)]	,       _UpperCAmelCase							)
								SCREAMING_SNAKE_CASE__			=					torch.cat(_UpperCAmelCase							)

				return new_state_dict




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> int:
				return text_enc_dict


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

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

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

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

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

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

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






import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union




@dataclass
class   __lowercase						:
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   None
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   True
	_A       =   None
	_A       =   1
	_A       =   None
	_A       =   False
	_A       =   None
	_A       =   None






	def       _a(self				: str    ) ->			"DownloadConfig":
							return self.__class__(**{k: copy.deepcopy(snake_case    ) for k, v in self.__dict__.items()}    )




