


"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
SCREAMING_SNAKE_CASE :    Any									=				"""src/diffusers"""
SCREAMING_SNAKE_CASE :    str									=				"""."""


# This is to make sure the diffusers module imported is the one in the repo.
SCREAMING_SNAKE_CASE :    List[Any]									=				importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
SCREAMING_SNAKE_CASE :    str									=				spec.loader.load_module()






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      Dict     )     ->Dict:
	"""simple docstring"""





	return line.startswith(_snake_case     ) or len(_snake_case     ) <= 1 or re.search(r'''^\s*\)(\s*->.*:|:)\s*$'''     ,   _snake_case     ) is not None






def 		lowercase							(     _snake_case						:      Optional[int]     )     ->Any:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				object_name.split('''.'''     )
	__snake_case	:						Optional[Any]						=				0

	# First let's find the module where our object lives.
	__snake_case	:						Tuple						=				parts[i]
	while i < len(_snake_case     ) and not os.path.isfile(os.path.join(_snake_case     ,   f"""{module}.py"""     )     ):
		i += 1
		if i < len(_snake_case     ):
			__snake_case	:						Any						=				os.path.join(_snake_case     ,   parts[i]     )
	if i >= len(_snake_case     ):
		raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""     )

	with open(os.path.join(_snake_case     ,   f"""{module}.py"""     )     ,   '''r'''     ,   encoding='''utf-8'''     ,   newline='''\n'''     ) as f:
		__snake_case	:						List[Any]						=				f.readlines()

	# Now let's find the class / func in the code!
	__snake_case	:						int						=				''''''
	__snake_case	:						List[Any]						=				0
	for name in parts[i + 1 :]:
		while (
		    line_index < len(_snake_case     ) and re.search(rf"""^{indent}(class|def)\s+{name}(\(|\:)"""     ,   lines[line_index]     ) is None
		):
			line_index += 1
		indent += "    "
		line_index += 1

	if line_index >= len(_snake_case     ):
		raise ValueError(f""" {object_name} does not match any function or class in {module}."""     )

	# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
	__snake_case	:						Union[str, Any]						=				line_index
	while line_index < len(_snake_case     ) and _should_continue(lines[line_index]     ,   _snake_case     ):
		line_index += 1
	# Clean up empty lines at the end (if any).
	while len(lines[line_index - 1]     ) <= 1:
		line_index -= 1

	__snake_case	:						Dict						=				lines[start_index:line_index]
	return "".join(_snake_case     )


SCREAMING_SNAKE_CASE :    List[Any]									=				re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
SCREAMING_SNAKE_CASE :    Optional[Any]									=				re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
SCREAMING_SNAKE_CASE :    Optional[Any]									=				re.compile(r"""<FILL\s+[^>]*>""")






def 		lowercase							(     _snake_case						:      str     )     ->int:
	"""simple docstring"""





	__snake_case	:						Any						=				code.split('''\n'''     )
	__snake_case	:						int						=				0
	while idx < len(_snake_case     ) and len(lines[idx]     ) == 0:
		idx += 1
	if idx < len(_snake_case     ):
		return re.search(r'''^(\s*)\S'''     ,   lines[idx]     ).groups()[0]
	return ""






def 		lowercase							(     _snake_case						:      Tuple     )     ->List[Any]:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				len(get_indent(_snake_case     )     ) > 0
	if has_indent:
		__snake_case	:						List[str]						=				f"""class Bla:\n{code}"""
	__snake_case	:						Optional[int]						=				black.Mode(target_versions={black.TargetVersion.PYaa}     ,   line_length=119     ,   preview=_snake_case     )
	__snake_case	:						Tuple						=				black.format_str(_snake_case     ,   mode=_snake_case     )
	__snake_case	,							__snake_case	:						int						=				style_docstrings_in_code(_snake_case     )
	return result[len('''class Bla:\n'''     ) :] if has_indent else result






def 		lowercase							(     _snake_case						:      Any     ,   _snake_case						:      List[Any]=False     )     ->Dict:
	"""simple docstring"""





	with open(_snake_case     ,   '''r'''     ,   encoding='''utf-8'''     ,   newline='''\n'''     ) as f:
		__snake_case	:						Any						=				f.readlines()
	__snake_case	:						int						=				[]
	__snake_case	:						Dict						=				0
	# Not a for loop cause `lines` is going to change (if `overwrite=True`).
	while line_index < len(_snake_case     ):
		__snake_case	:						str						=				_re_copy_warning.search(lines[line_index]     )
		if search is None:
			line_index += 1
			continue

		# There is some copied code here, let's retrieve the original.
		__snake_case	,							__snake_case	,							__snake_case	:						Optional[Any]						=				search.groups()
		__snake_case	:						List[Any]						=				find_code_in_diffusers(_snake_case     )
		__snake_case	:						Optional[int]						=				get_indent(_snake_case     )

		__snake_case	:						Any						=				line_index + 1 if indent == theoretical_indent else line_index + 2
		__snake_case	:						Any						=				theoretical_indent
		__snake_case	:						Optional[int]						=				start_index

		# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
		__snake_case	:						Dict						=				True
		while line_index < len(_snake_case     ) and should_continue:
			line_index += 1
			if line_index >= len(_snake_case     ):
				break
			__snake_case	:						str						=				lines[line_index]
			__snake_case	:						List[str]						=				_should_continue(_snake_case     ,   _snake_case     ) and re.search(f"""^{indent}# End copy"""     ,   _snake_case     ) is None
		# Clean up empty lines at the end (if any).
		while len(lines[line_index - 1]     ) <= 1:
			line_index -= 1

		__snake_case	:						Any						=				lines[start_index:line_index]
		__snake_case	:						Dict						=				''''''.join(_snake_case     )

		# Remove any nested `Copied from` comments to avoid circular copies
		__snake_case	:						Optional[int]						=				[line for line in theoretical_code.split('''\n'''     ) if _re_copy_warning.search(_snake_case     ) is None]
		__snake_case	:						Any						=				'''\n'''.join(_snake_case     )

		# Before comparing, use the `replace_pattern` on the original code.
		if len(_snake_case     ) > 0:
			__snake_case	:						Tuple						=				replace_pattern.replace('''with'''     ,   ''''''     ).split(''','''     )
			__snake_case	:						Optional[Any]						=				[_re_replace_pattern.search(_snake_case     ) for p in patterns]
			for pattern in patterns:
				if pattern is None:
					continue
				__snake_case	,							__snake_case	,							__snake_case	:						Optional[int]						=				pattern.groups()
				__snake_case	:						Optional[int]						=				re.sub(_snake_case     ,   _snake_case     ,   _snake_case     )
				if option.strip() == "all-casing":
					__snake_case	:						int						=				re.sub(obja.lower()     ,   obja.lower()     ,   _snake_case     )
					__snake_case	:						Tuple						=				re.sub(obja.upper()     ,   obja.upper()     ,   _snake_case     )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
			__snake_case	:						Tuple						=				blackify(lines[start_index - 1] + theoretical_code     )
			__snake_case	:						Optional[int]						=				theoretical_code[len(lines[start_index - 1]     ) :]

		# Test for a diff and act accordingly.
		if observed_code != theoretical_code:
			diffs.append([object_name, start_index]     )
			if overwrite:
				__snake_case	:						Union[str, Any]						=				lines[:start_index] + [theoretical_code] + lines[line_index:]
				__snake_case	:						Any						=				start_index + 1

	if overwrite and len(_snake_case     ) > 0:
		# Warn the user a file has been modified.
		print(f"""Detected changes, rewriting {filename}."""     )
		with open(_snake_case     ,   '''w'''     ,   encoding='''utf-8'''     ,   newline='''\n'''     ) as f:
			f.writelines(_snake_case     )
	return diffs






def 		lowercase							(     _snake_case						:      bool = False     )     ->Union[str, Any]:
	"""simple docstring"""





	__snake_case	:						Any						=				glob.glob(os.path.join(_snake_case     ,   '''**/*.py'''     )     ,   recursive=_snake_case     )
	__snake_case	:						List[str]						=				[]
	for filename in all_files:
		__snake_case	:						Any						=				is_copy_consistent(_snake_case     ,   _snake_case     )
		diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
	if not overwrite and len(_snake_case     ) > 0:
		__snake_case	:						List[str]						=				'''\n'''.join(_snake_case     )
		raise Exception(
		    '''Found the following copy inconsistencies:\n'''
		    + diff
		    + '''\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'''     )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    int									=				argparse.ArgumentParser()
			parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
			SCREAMING_SNAKE_CASE :    Tuple									=				parser.parse_args()

			check_copies(args.fix_and_overwrite)






"""simple docstring"""






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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

lowerCAmelCase__  :  str						     =	'mid_block.attentions.0.'
lowerCAmelCase__  :  Union[str, Any]						     =	'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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



def   a_						(					lowerCamelCase       ):
				# 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.
				UpperCAmelCase__             =							{k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								UpperCAmelCase__             =							sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
												UpperCAmelCase__             =							v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
								UpperCAmelCase__             =							v
				UpperCAmelCase__             =							{v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


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

lowerCAmelCase__  :  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):
												lowerCAmelCase__  :  List[str]						     =	F"""encoder.down_blocks.{i}.resnets.{j}."""
												lowerCAmelCase__  :  List[Any]						     =	F"""encoder.down.{i}.block.{j}."""
												vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

												lowerCAmelCase__  :  int						     =	F"""up_blocks.{i}.upsamplers.0."""
												lowerCAmelCase__  :  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):
												lowerCAmelCase__  :  Dict						     =	F"""decoder.up_blocks.{i}.resnets.{j}."""
												lowerCAmelCase__  :  Optional[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):
						lowerCAmelCase__  :  Any						     =	F"""mid_block.resnets.{i}."""
						lowerCAmelCase__  :  Any						     =	F"""mid.block_{i+1}."""
						vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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



def   a_						(					lowerCamelCase       ):
				# convert HF linear weights to SD conv2d weights
				return w.reshape(*w.shape					,			1					,			1       )



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							{k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
								UpperCAmelCase__             =							v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
												UpperCAmelCase__             =							v
				UpperCAmelCase__             =							{v: vae_state_dict[k] for k, v in mapping.items()}
				UpperCAmelCase__             =							['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'''       )
																UpperCAmelCase__             =							reshape_weight_for_sd(lowerCamelCase       )
				return new_state_dict


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


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

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



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							{}
				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'       )
								):
												UpperCAmelCase__             =							k[: -len('.q_proj.weight'       )]
												UpperCAmelCase__             =							k[-len('q_proj.weight'       )]
												if k_pre not in capture_qkv_weight:
																UpperCAmelCase__             =							[None, None, None]
												UpperCAmelCase__             =							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'       )
								):
												UpperCAmelCase__             =							k[: -len('.q_proj.bias'       )]
												UpperCAmelCase__             =							k[-len('q_proj.bias'       )]
												if k_pre not in capture_qkv_bias:
																UpperCAmelCase__             =							[None, None, None]
												UpperCAmelCase__             =							v
												continue

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

				return new_state_dict



def   a_						(					lowerCamelCase       ):
				return text_enc_dict


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

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

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

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

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

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

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



