
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 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_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__	=							[
    # (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 							__A(lowerCAmelCase )		->				Union[str, Any]:

		"""simple docstring"""






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


def 							__A(lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_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__	=							[
    # (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 							__A(lowerCAmelCase )		->				Dict:

		"""simple docstring"""






		_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 )		->				Dict:

		"""simple docstring"""






		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 math import isclose, sqrt


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				tuple[float, float, float]:

		"""simple docstring"""






		_UpperCamelCase						    =	point_y / 4 / point_x
		_UpperCamelCase						    =	2 * normal_gradient / (1 + normal_gradient * normal_gradient)
		_UpperCamelCase						    =	(1 - normal_gradient * normal_gradient) / (
		    1 + normal_gradient * normal_gradient
		)
		_UpperCamelCase						    =	(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

		# to find the next point, solve the simultaeneous equations:
		# y^2 + 4x^2 = 100
		# y - b = m * (x - a)
		# ==> A x^2 + B x + C = 0
		_UpperCamelCase						    =	outgoing_gradient**2 + 4
		_UpperCamelCase						    =	2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
		_UpperCamelCase						    =	(point_y - outgoing_gradient * point_x) ** 2 - 1_0_0

		_UpperCamelCase						    =	(
		    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)
		_UpperCamelCase						    =	(
		    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)

		# two solutions, one of which is our input point
		_UpperCamelCase						    =	x_minus if isclose(lowerCAmelCase						,  lowerCAmelCase ) else x_plus
		_UpperCamelCase						    =	point_y + outgoing_gradient * (next_x - point_x)

		return next_x, next_y, outgoing_gradient


def 							__A(lowerCAmelCase = 1.4						,  lowerCAmelCase = -9.6 )		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	0
		_UpperCamelCase						    =	first_x_coord
		_UpperCamelCase						    =	first_y_coord
		_UpperCamelCase						    =	(10.1 - point_y) / (0.0 - point_x)

		while not (-0.01 <= point_x <= 0.01 and point_y > 0):
				_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	next_point(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
				num_reflections += 1

		return num_reflections


if __name__ == "__main__":
		print(F"""{solution() = }""")



