



'''simple docstring'''

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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"""),
]

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"""),
]

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
		snake_case_			=     f'''down_blocks.{i}.resnets.{j}.'''
		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
			snake_case_			=     f'''down_blocks.{i}.attentions.{j}.'''
			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
		snake_case_			=     f'''up_blocks.{i}.resnets.{j}.'''
		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
			snake_case_			=     f'''up_blocks.{i}.attentions.{j}.'''
			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
		snake_case_			=     f'''down_blocks.{i}.downsamplers.0.conv.'''
		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
		snake_case_			=     f'''up_blocks.{i}.upsamplers.0.'''
		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))

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

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
				SCREAMING_SNAKE_CASE  :						int      					= {k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								SCREAMING_SNAKE_CASE  :						Dict      					= 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  :						Optional[int]      					= v.replace(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						int      					= v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												SCREAMING_SNAKE_CASE  :						int      					= v.replace(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						List[Any]      					= v
				SCREAMING_SNAKE_CASE  :						List[str]      					= {v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


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

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):
		snake_case_			=     f'''encoder.down_blocks.{i}.resnets.{j}.'''
		snake_case_			=     f'''encoder.down.{i}.block.{j}.'''
		vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

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


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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict       ):
				return w.reshape(*w.shape    ,	1    ,	1       )






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


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


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

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






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

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

				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  :						Tuple      					= textenc_pattern.sub(lambda _SCREAMING_SNAKE_CASE       : protected[re.escape(m.group(0       )       )]    ,	_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= torch.cat(_SCREAMING_SNAKE_CASE       )

				return new_state_dict






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Tuple       ):
				return text_enc_dict


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

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

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

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

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

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

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

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






'''simple docstring'''

from __future__ import annotations






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int | str       ):
				SCREAMING_SNAKE_CASE  :						int      					= str(_SCREAMING_SNAKE_CASE       )
				return n == n[::-1]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int = 1_00_00_00       ):
				SCREAMING_SNAKE_CASE  :						int      					= 0

				for i in range(1    ,	_SCREAMING_SNAKE_CASE       ):
								if is_palindrome(_SCREAMING_SNAKE_CASE       ) and is_palindrome(bin(_SCREAMING_SNAKE_CASE       ).split('''b'''       )[1]       ):
												total += i
				return total


if __name__ == "__main__":
	print(solution(int(str(input().strip()))))




