





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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

_lowerCamelCase   				=			'mid_block.attentions.0.'
_lowerCamelCase   				=			'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		# 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.
		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 #
# ================#

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

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

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


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






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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]    ):
		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 #
# =========================#


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

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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, 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__:							Optional[Any]    ):
		return text_enc_dict


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

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

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

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

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

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

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase   				=			logging.get_logger(__name__)




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"timm_backbone"


							def __init__(							self    :Union[str, Any]							,				__A    :str=None							,				__A    :Union[str, Any]=3							,				__A    :str=True							,				__A    :Any=True							,				__A    :Optional[Any]=None							,				**__A    :List[str]							,				)   ->   Tuple:



									"""simple docstring"""



									super().__init__(**__A		)
									SCREAMING_SNAKE_CASE__			=      backbone
									SCREAMING_SNAKE_CASE__			=      num_channels
									SCREAMING_SNAKE_CASE__			=      features_only
									SCREAMING_SNAKE_CASE__			=      use_pretrained_backbone
									SCREAMING_SNAKE_CASE__			=      True
									SCREAMING_SNAKE_CASE__			=      out_indices if out_indices is not None else (-1,)