"""simple docstring"""




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   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							{k: k for k in unet_state_dict.keys()}
							for sd_name, hf_name in unet_conversion_map:
														A__	     =							sd_name
							for k, v in mapping.items():
														if "resnets" in k:
																					for sd_part, hf_part in unet_conversion_map_resnet:
																												A__	     =							v.replace(UpperCamelCase__       ,    UpperCamelCase__				)
																					A__	     =							v
							for k, v in mapping.items():
														for sd_part, hf_part in unet_conversion_map_layer:
																					A__	     =							v.replace(UpperCamelCase__       ,    UpperCamelCase__				)
														A__	     =							v
							A__	     =							{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   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							{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__	     =							v.replace(UpperCamelCase__       ,    UpperCamelCase__				)
														A__	     =							v
							for k, v in mapping.items():
														if "attentions" in k:
																					for sd_part, hf_part in vae_conversion_map_attn:
																												A__	     =							v.replace(UpperCamelCase__       ,    UpperCamelCase__				)
																					A__	     =							v
							A__	     =							{v: vae_state_dict[k] for k, v in mapping.items()}
							A__	     =							['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__	     =							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   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							{}
							A__	     =							{}
							A__	     =							{}
							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__	     =							k[: -len('.q_proj.weight'				)]
																					A__	     =							k[-len('q_proj.weight'				)]
																					if k_pre not in capture_qkv_weight:
																												A__	     =							[None, None, None]
																					A__	     =							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__	     =							k[: -len('.q_proj.bias'				)]
																					A__	     =							k[-len('q_proj.bias'				)]
																					if k_pre not in capture_qkv_bias:
																												A__	     =							[None, None, None]
																					A__	     =							v
																					continue

														A__	     =							textenc_pattern.sub(lambda UpperCamelCase__				: protected[re.escape(m.group(0				)				)]       ,    UpperCamelCase__				)
														A__	     =							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__	     =							textenc_pattern.sub(lambda UpperCamelCase__				: protected[re.escape(m.group(0				)				)]       ,    UpperCamelCase__				)
														A__	     =							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'				)
														A__	     =							textenc_pattern.sub(lambda UpperCamelCase__				: protected[re.escape(m.group(0				)				)]       ,    UpperCamelCase__				)
														A__	     =							torch.cat(UpperCamelCase__				)

							return new_state_dict






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""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)






"""simple docstring"""




def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							if len(UpperCamelCase__				) <= 1:
														return [tuple(UpperCamelCase__				)]

							A__	     =							[]

							def generate(UpperCamelCase__       ,    UpperCamelCase__				):
														if k == 1:
																					res.append(tuple(arr[:]				)				)
																					return

														generate(k - 1       ,    UpperCamelCase__				)

														for i in range(k - 1				):
																					if k % 2 == 0:  # k is even
																												A__     ,  A__	     =							arr[k - 1], arr[i]
																					else:  # k is odd
																												A__     ,  A__	     =							arr[k - 1], arr[0]
																					generate(k - 1       ,    UpperCamelCase__				)

							generate(len(UpperCamelCase__				)       ,    UpperCamelCase__				)
							return res


if __name__ == "__main__":
				__lowerCamelCase											=  input("Enter numbers separated by a comma:\n").strip()
				__lowerCamelCase											=  [int(item) for item in user_input.split(",")]
				print(heaps(arr))





