





"""simple docstring"""




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

a		:				Tuple  			=    [
    # (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		:				Tuple  			=    [
    # (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		:				Optional[int]  			=    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		:				Dict  			=    F'''down_blocks.{i}.attentions.{j}.'''
						a		:				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
				a		:				Dict  			=    F'''up_blocks.{i}.resnets.{j}.'''
				a		:				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
						a		:				Any  			=    F'''up_blocks.{i}.attentions.{j}.'''
						a		:				Optional[int]  			=    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		:				Dict  			=    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		:				Tuple  			=    F'''up_blocks.{i}.upsamplers.0.'''
				a		:				Optional[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		:				Optional[Any]  			=    '''mid_block.attentions.0.'''
a		:				Optional[int]  			=    '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]      )				->Any:






	'''simple docstring'''






	a   :	Optional[Any]			=       {k: k for k in unet_state_dict.keys()}
	for sd_name, hf_name in unet_conversion_map:
		a   :	str			=       sd_name
	for k, v in mapping.items():
		if "resnets" in k:
			for sd_part, hf_part in unet_conversion_map_resnet:
				a   :	str			=       v.replace(_lowercase					, _lowercase      )
			a   :	Tuple			=       v
	for k, v in mapping.items():
		for sd_part, hf_part in unet_conversion_map_layer:
			a   :	Any			=       v.replace(_lowercase					, _lowercase      )
		a   :	Any			=       v
	a   :	Any			=       {v: unet_state_dict[k] for k, v in mapping.items()}
	return new_state_dict


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

a		:				Any  			=    [
    # (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		:				List[Any]  			=    F'''down.{i}.downsample.'''
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				a		:				Tuple  			=    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		:				Optional[Any]  			=    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		:				Dict  			=    F'''mid_block.resnets.{i}.'''
		a		:				Tuple  			=    F'''mid.block_{i+1}.'''
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple      )				->int:






	'''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]      )				->Optional[int]:






	'''simple docstring'''






	a   :	Optional[int]			=       {k: k for k in vae_state_dict.keys()}
	for k, v in mapping.items():
		for sd_part, hf_part in vae_conversion_map:
			a   :	Tuple			=       v.replace(_lowercase					, _lowercase      )
		a   :	int			=       v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				a   :	int			=       v.replace(_lowercase					, _lowercase      )
			a   :	int			=       v
	a   :	Dict			=       {v: vae_state_dict[k] for k, v in mapping.items()}
	a   :	int			=       ["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"""      )
				a   :	Optional[int]			=       reshape_weight_for_sd(_lowercase      )
	return new_state_dict


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


a		:				Optional[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		:				Dict  			=    {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
a		:				str  			=    re.compile('''|'''.join(protected.keys()))

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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int      )				->Union[str, Any]:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Optional[Any]			=       {}
	a   :	Dict			=       {}
	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"      )
		):
			a   :	Dict			=       k[: -len(".q_proj.weight"      )]
			a   :	str			=       k[-len("q_proj.weight"      )]
			if k_pre not in capture_qkv_weight:
				a   :	List[Any]			=       [None, None, None]
			a   :	Union[str, Any]			=       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"      )
		):
			a   :	str			=       k[: -len(".q_proj.bias"      )]
			a   :	Tuple			=       k[-len("q_proj.bias"      )]
			if k_pre not in capture_qkv_bias:
				a   :	List[Any]			=       [None, None, None]
			a   :	str			=       v
			continue

		a   :	str			=       textenc_pattern.sub(lambda _lowercase      : protected[re.escape(m.group(0      )      )]					, _lowercase      )
		a   :	Any			=       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"      )
		a   :	str			=       textenc_pattern.sub(lambda _lowercase      : protected[re.escape(m.group(0      )      )]					, _lowercase      )
		a   :	Dict			=       torch.cat(_lowercase      )

	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"      )
		a   :	Optional[Any]			=       textenc_pattern.sub(lambda _lowercase      : protected[re.escape(m.group(0      )      )]					, _lowercase      )
		a   :	str			=       torch.cat(_lowercase      )

	return new_state_dict






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]      )				->Optional[int]:






	'''simple docstring'''






	return text_enc_dict


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

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

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

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

		# Convert the VAE model
		a		:				Optional[int]  			=    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		:				Optional[int]  			=    '''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		:				int  			=    {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
				a		:				List[Any]  			=    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		:				List[Any]  			=    {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

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

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				a		:				str  			=    {'''state_dict''': state_dict}
				torch.save(state_dict, args.checkpoint_path)









"""simple docstring"""




import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


a		:				Any  			=    logging.get_logger(__name__)

a		:				Tuple  			=    {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   )			->				str:
								logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."   )
								a   :	Optional[int]			=       model
								a   :	int			=       kwargs.get("model_save_dir"   ,      lowerCAmelCase__   )
								a   :	Tuple			=       kwargs.get("latest_model_name"   ,      lowerCAmelCase__   )






							def __call__(     self   ,      **lowerCAmelCase__   )			->				Dict:
								a   :	List[str]			=       {k: np.array(lowerCAmelCase__   ) for k, v in kwargs.items()}
								return self.model.run(lowerCAmelCase__   ,      lowerCAmelCase__   )






							@staticmethod
							def 					__a     (     lowerCAmelCase__   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   )			->				Union[str, Any]:
								if provider is None:
									logger.info("No onnxruntime provider specified, using CPUExecutionProvider"   )
									a   :	List[str]			=       "CPUExecutionProvider"

								return ort.InferenceSession(lowerCAmelCase__   ,      providers=[provider]   ,      sess_options=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   )			->				int:
								a   :	List[str]			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME

								a   :	Optional[int]			=       self.model_save_dir.joinpath(self.latest_model_name   )
								a   :	List[str]			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
								try:
									shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
								except shutil.SameFileError:
									pass

								# copy external weights (for models >2GB)
								a   :	str			=       self.model_save_dir.joinpath(lowerCAmelCase__   )
								if src_path.exists():
									a   :	Any			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
									try:
										shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
									except shutil.SameFileError:
										pass






							def 					__a     (     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )			->				str:
								if os.path.isfile(lowerCAmelCase__   ):
									logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""   )
									return

								os.makedirs(lowerCAmelCase__   ,      exist_ok=lowerCAmelCase__   )

								# saving model weights/files
								self._save_pretrained(lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				Optional[int]:
								a   :	Tuple			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME
								# load model from local directory
								if os.path.isdir(lowerCAmelCase__   ):
									a   :	Tuple			=       OnnxRuntimeModel.load_model(
									    os.path.join(lowerCAmelCase__   ,      lowerCAmelCase__   )   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
									a   :	Tuple			=       Path(lowerCAmelCase__   )
								# load model from hub
								else:
									# download model
									a   :	Optional[Any]			=       hf_hub_download(
									    repo_id=lowerCAmelCase__   ,      filename=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      )
									a   :	Optional[int]			=       Path(lowerCAmelCase__   ).parent
									a   :	List[Any]			=       Path(lowerCAmelCase__   ).name
									a   :	int			=       OnnxRuntimeModel.load_model(lowerCAmelCase__   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
								return cls(model=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				List[str]:
								a   :	Any			=       None
								if len(str(lowerCAmelCase__   ).split("@"   )   ) == 2:
									a, a   :	Tuple			=       model_id.split("@"   )

								return cls._from_pretrained(
								    model_id=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )


