





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

_SCREAMING_SNAKE_CASE      	=		[
    # (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"""),
]

_SCREAMING_SNAKE_CASE      	=		[
    # (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"""),
]

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

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

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


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	# 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.
	snake_case_		:			Union[str, Any]											=	{k: k for k in unet_state_dict.keys()}
	for sd_name, hf_name in unet_conversion_map:
		snake_case_		:			int											=	sd_name
	for k, v in mapping.items():
		if "resnets" in k:
			for sd_part, hf_part in unet_conversion_map_resnet:
				snake_case_		:			str											=	v.replace(__a   , __a   )
			snake_case_		:			List[str]											=	v
	for k, v in mapping.items():
		for sd_part, hf_part in unet_conversion_map_layer:
			snake_case_		:			str											=	v.replace(__a   , __a   )
		snake_case_		:			List[str]											=	v
	snake_case_		:			Any											=	{v: unet_state_dict[k] for k, v in mapping.items()}
	return new_state_dict


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

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

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

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


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


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


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			str											=	{k: k for k in vae_state_dict.keys()}
	for k, v in mapping.items():
		for sd_part, hf_part in vae_conversion_map:
			snake_case_		:			Any											=	v.replace(__a   , __a   )
		snake_case_		:			Optional[Any]											=	v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				snake_case_		:			Any											=	v.replace(__a   , __a   )
			snake_case_		:			Optional[Any]											=	v
	snake_case_		:			str											=	{v: vae_state_dict[k] for k, v in mapping.items()}
	snake_case_		:			Union[str, Any]											=	['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"""   )
				snake_case_		:			Optional[Any]											=	reshape_weight_for_sd(__a   )
	return new_state_dict


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


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

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


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Optional[Any]											=	{}
	snake_case_		:			List[str]											=	{}
	snake_case_		:			Optional[int]											=	{}
	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'   )
		):
			snake_case_		:			Any											=	k[: -len('.q_proj.weight'   )]
			snake_case_		:			List[str]											=	k[-len('q_proj.weight'   )]
			if k_pre not in capture_qkv_weight:
				snake_case_		:			Union[str, Any]											=	[None, None, None]
			snake_case_		:			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'   )
		):
			snake_case_		:			Optional[Any]											=	k[: -len('.q_proj.bias'   )]
			snake_case_		:			List[Any]											=	k[-len('q_proj.bias'   )]
			if k_pre not in capture_qkv_bias:
				snake_case_		:			List[Any]											=	[None, None, None]
			snake_case_		:			List[str]											=	v
			continue

		snake_case_		:			str											=	textenc_pattern.sub(lambda __a   : protected[re.escape(m.group(0   )   )]   , __a   )
		snake_case_		:			str											=	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'   )
		snake_case_		:			Any											=	textenc_pattern.sub(lambda __a   : protected[re.escape(m.group(0   )   )]   , __a   )
		snake_case_		:			List[str]											=	torch.cat(__a   )

	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'   )
		snake_case_		:			List[str]											=	textenc_pattern.sub(lambda __a   : protected[re.escape(m.group(0   )   )]   , __a   )
		snake_case_		:			List[Any]											=	torch.cat(__a   )

	return new_state_dict


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	return text_enc_dict


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		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."""
						)

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

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

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

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

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

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

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








import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		"""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

						print("""Googling.....""")

						_SCREAMING_SNAKE_CASE      	=		F'''https://www.google.com/search?q={query}&num=100'''

						_SCREAMING_SNAKE_CASE      	=		requests.get(
						    url,
						    headers={"""User-Agent""": str(UserAgent().random)},
						)

						try:
												_SCREAMING_SNAKE_CASE      	=		(
												    BeautifulSoup(res.text, """html.parser""")
												    .find("""div""", attrs={"""class""": """yuRUbf"""})
												    .find("""a""")
												    .get("""href""")
												)

						except AttributeError:
												_SCREAMING_SNAKE_CASE      	=		parse_qs(
												    BeautifulSoup(res.text, """html.parser""")
												    .find("""div""", attrs={"""class""": """kCrYT"""})
												    .find("""a""")
												    .get("""href""")
												)["""url"""][0]

						webbrowser.open(link)

