






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

_UpperCamelCase			:	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'),
]

_UpperCamelCase			:	List[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'),
]

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

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

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



def  a__ (__lowercase							:Optional[int]   )		-> Any:
				# 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.
				_A	:				int   =      {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	:				Tuple   =      v.replace(__lowercase					,  __lowercase   )
												_A	:				Dict   =      v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												_A	:				List[str]   =      v.replace(__lowercase					,  __lowercase   )
								_A	:				Union[str, Any]   =      v
				_A	:				Optional[Any]   =      {v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


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

_UpperCamelCase			:	List[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):
		_UpperCamelCase			:	List[str]								=f'''encoder.down_blocks.{i}.resnets.{j}.'''
		_UpperCamelCase			:	Tuple								=f'''encoder.down.{i}.block.{j}.'''
		vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

		_UpperCamelCase			:	Union[str, Any]								=f'''up_blocks.{i}.upsamplers.0.'''
		_UpperCamelCase			:	Union[str, Any]								=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):
		_UpperCamelCase			:	List[Any]								=f'''decoder.up_blocks.{i}.resnets.{j}.'''
		_UpperCamelCase			:	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):
	_UpperCamelCase			:	List[str]								=f'''mid_block.resnets.{i}.'''
	_UpperCamelCase			:	List[str]								=f'''mid.block_{i+1}.'''
	vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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



def  a__ (__lowercase							:Any   )		-> Tuple:
				# convert HF linear weights to SD conv2d weights
				return w.reshape(*w.shape					,  1					,  1   )



def  a__ (__lowercase							:Optional[Any]   )		-> List[Any]:
				_A	:				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	:				Dict   =      v.replace(__lowercase					,  __lowercase   )
								_A	:				str   =      v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																_A	:				List[str]   =      v.replace(__lowercase					,  __lowercase   )
												_A	:				str   =      v
				_A	:				Union[str, Any]   =      {v: vae_state_dict[k] for k, v in mapping.items()}
				_A	:				str   =      ['''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	:				Dict   =      reshape_weight_for_sd(__lowercase   )
				return new_state_dict


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


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

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



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

								_A	:				Tuple   =      textenc_pattern.sub(lambda __lowercase   : protected[re.escape(m.group(0   )   )]					,  __lowercase   )
								_A	:				Union[str, 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	:				Optional[Any]   =      textenc_pattern.sub(lambda __lowercase   : protected[re.escape(m.group(0   )   )]					,  __lowercase   )
								_A	:				Optional[int]   =      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	:				List[Any]   =      textenc_pattern.sub(lambda __lowercase   : protected[re.escape(m.group(0   )   )]					,  __lowercase   )
								_A	:				List[str]   =      torch.cat(__lowercase   )

				return new_state_dict



def  a__ (__lowercase							:List[str]   )		-> List[str]:
				return text_enc_dict


if __name__ == "__main__":
	_UpperCamelCase			:	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.'
	)

	_UpperCamelCase			:	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
	_UpperCamelCase			:	Union[str, Any]								=osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
	_UpperCamelCase			:	List[Any]								=osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
	_UpperCamelCase			:	Any								=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):
		_UpperCamelCase			:	List[str]								=load_file(unet_path, device='cpu')
	else:
		_UpperCamelCase			:	Union[str, Any]								=osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
		_UpperCamelCase			:	str								=torch.load(unet_path, map_location='cpu')

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

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

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

	# Convert the VAE model
	_UpperCamelCase			:	int								=convert_vae_state_dict(vae_state_dict)
	_UpperCamelCase			:	List[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
	_UpperCamelCase			:	Union[str, 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
		_UpperCamelCase			:	Dict								={'transformer.' + k: v for k, v in text_enc_dict.items()}
		_UpperCamelCase			:	Dict								=convert_text_enc_state_dict_vaa(text_enc_dict)
		_UpperCamelCase			:	Dict								={'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
	else:
		_UpperCamelCase			:	Dict								=convert_text_enc_state_dict(text_enc_dict)
		_UpperCamelCase			:	str								={'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

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

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











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


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Optional[Any]								={
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   "pegasus"
		__snake_case      :       List[str]     =   ["past_key_values"]
		__snake_case      :       Union[str, Any]     =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







		def __init__(					self       ,A__=50265       ,A__=1024       ,A__=12       ,A__=4096       ,A__=16       ,A__=12       ,A__=4096       ,A__=16       ,A__=0.0       ,A__=0.0       ,A__=True       ,A__=True       ,A__="gelu"       ,A__=1024       ,A__=0.1       ,A__=0.0       ,A__=0.0       ,A__=0.02       ,A__=0       ,A__=False       ,A__=0       ,A__=1       ,A__=1       ,**A__       ,):
						_A	:				List[str]   =      vocab_size
						_A	:				List[Any]   =      max_position_embeddings
						_A	:				Union[str, Any]   =      d_model
						_A	:				Union[str, Any]   =      encoder_ffn_dim
						_A	:				Union[str, Any]   =      encoder_layers
						_A	:				int   =      encoder_attention_heads
						_A	:				List[Any]   =      decoder_ffn_dim
						_A	:				Optional[int]   =      decoder_layers
						_A	:				Tuple   =      decoder_attention_heads
						_A	:				List[str]   =      dropout
						_A	:				Tuple   =      attention_dropout
						_A	:				Union[str, Any]   =      activation_dropout
						_A	:				int   =      activation_function
						_A	:				Union[str, Any]   =      init_std
						_A	:				Any   =      encoder_layerdrop
						_A	:				int   =      decoder_layerdrop
						_A	:				Tuple   =      use_cache
						_A	:				Dict   =      encoder_layers
						_A	:				List[str]   =      scale_embedding  # scale factor will be sqrt(d_model) if True
						super().__init__(
						    pad_token_id=A__       ,eos_token_id=A__       ,is_encoder_decoder=A__       ,decoder_start_token_id=A__       ,forced_eos_token_id=A__       ,**A__       ,)







		@property
		def  A__      (					self		):
						return self.encoder_attention_heads







		@property
		def  A__      (					self		):
						return self.d_model



