



import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			checkpoint

			lowercase__   					=			{}

			lowercase__   					=			vae_state_dict["encoder.conv_in.weight"]
			lowercase__   					=			vae_state_dict["encoder.conv_in.bias"]
			lowercase__   					=			vae_state_dict["encoder.conv_out.weight"]
			lowercase__   					=			vae_state_dict["encoder.conv_out.bias"]
			lowercase__   					=			vae_state_dict["encoder.norm_out.weight"]
			lowercase__   					=			vae_state_dict["encoder.norm_out.bias"]

			lowercase__   					=			vae_state_dict["decoder.conv_in.weight"]
			lowercase__   					=			vae_state_dict["decoder.conv_in.bias"]
			lowercase__   					=			vae_state_dict["decoder.conv_out.weight"]
			lowercase__   					=			vae_state_dict["decoder.conv_out.bias"]
			lowercase__   					=			vae_state_dict["decoder.norm_out.weight"]
			lowercase__   					=			vae_state_dict["decoder.norm_out.bias"]

			lowercase__   					=			vae_state_dict["quant_conv.weight"]
			lowercase__   					=			vae_state_dict["quant_conv.bias"]
			lowercase__   					=			vae_state_dict["post_quant_conv.weight"]
			lowercase__   					=			vae_state_dict["post_quant_conv.bias"]

			# Retrieves the keys for the encoder down blocks only
			lowercase__   					=			len({".".join(layer.split("."					)[:3]					) for layer in vae_state_dict if "encoder.down" in layer}					)
			lowercase__   					=			{
			    layer_id: [key for key in vae_state_dict if f'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_					)
			}

			# Retrieves the keys for the decoder up blocks only
			lowercase__   					=			len({".".join(layer.split("."					)[:3]					) for layer in vae_state_dict if "decoder.up" in layer}					)
			lowercase__   					=			{
			    layer_id: [key for key in vae_state_dict if f'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_					)
			}

			for i in range(SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			[key for key in down_blocks[i] if f'''down.{i}''' in key and f'''down.{i}.downsample''' not in key]

						if f'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
									lowercase__   					=			vae_state_dict.pop(
									    f'''encoder.down.{i}.downsample.conv.weight'''					)
									lowercase__   					=			vae_state_dict.pop(
									    f'''encoder.down.{i}.downsample.conv.bias'''					)

						lowercase__   					=			renew_vae_resnet_paths(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			{"old": f'''down.{i}.block''', "new": f'''down_blocks.{i}.resnets'''}
						assign_to_checkpoint(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					additional_replacements=[meta_path]  ,					config=SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			[key for key in vae_state_dict if "encoder.mid.block" in key]
			lowercase__   					=			2
			for i in range(1  ,					num_mid_res_blocks + 1					):
						lowercase__   					=			[key for key in mid_resnets if f'''encoder.mid.block_{i}''' in key]

						lowercase__   					=			renew_vae_resnet_paths(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			{"old": f'''mid.block_{i}''', "new": f'''mid_block.resnets.{i - 1}'''}
						assign_to_checkpoint(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					additional_replacements=[meta_path]  ,					config=SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			[key for key in vae_state_dict if "encoder.mid.attn" in key]
			lowercase__   					=			renew_vae_attention_paths(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			{"old": "mid.attn_1", "new": "mid_block.attentions.0"}
			assign_to_checkpoint(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					additional_replacements=[meta_path]  ,					config=SCREAMING_SNAKE_CASE_					)
			conv_attn_to_linear(SCREAMING_SNAKE_CASE_					)

			for i in range(SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			num_up_blocks - 1 - i
						lowercase__   					=			[
						    key for key in up_blocks[block_id] if f'''up.{block_id}''' in key and f'''up.{block_id}.upsample''' not in key
						]

						if f'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
									lowercase__   					=			vae_state_dict[
									    f'''decoder.up.{block_id}.upsample.conv.weight'''
									]
									lowercase__   					=			vae_state_dict[
									    f'''decoder.up.{block_id}.upsample.conv.bias'''
									]

						lowercase__   					=			renew_vae_resnet_paths(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			{"old": f'''up.{block_id}.block''', "new": f'''up_blocks.{i}.resnets'''}
						assign_to_checkpoint(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					additional_replacements=[meta_path]  ,					config=SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			[key for key in vae_state_dict if "decoder.mid.block" in key]
			lowercase__   					=			2
			for i in range(1  ,					num_mid_res_blocks + 1					):
						lowercase__   					=			[key for key in mid_resnets if f'''decoder.mid.block_{i}''' in key]

						lowercase__   					=			renew_vae_resnet_paths(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			{"old": f'''mid.block_{i}''', "new": f'''mid_block.resnets.{i - 1}'''}
						assign_to_checkpoint(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					additional_replacements=[meta_path]  ,					config=SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			[key for key in vae_state_dict if "decoder.mid.attn" in key]
			lowercase__   					=			renew_vae_attention_paths(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			{"old": "mid.attn_1", "new": "mid_block.attentions.0"}
			assign_to_checkpoint(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					additional_replacements=[meta_path]  ,					config=SCREAMING_SNAKE_CASE_					)
			conv_attn_to_linear(SCREAMING_SNAKE_CASE_					)
			return new_checkpoint



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					):
			# Only support V1
			lowercase__   					=			requests.get(
			    " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"					)
			lowercase__   					=			io.BytesIO(r.content					)

			lowercase__   					=			OmegaConf.load(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			512
			lowercase__   					=			"cuda" if torch.cuda.is_available() else "cpu"
			if checkpoint_path.endswith("safetensors"					):
						from safetensors import safe_open

						lowercase__   					=			{}
						with safe_open(SCREAMING_SNAKE_CASE_  ,					framework="pt"  ,					device="cpu"					) as f:
									for key in f.keys():
												lowercase__   					=			f.get_tensor(SCREAMING_SNAKE_CASE_					)
			else:
						lowercase__   					=			torch.load(SCREAMING_SNAKE_CASE_  ,					map_location=SCREAMING_SNAKE_CASE_					)["state_dict"]

			# Convert the VAE model.
			lowercase__   					=			create_vae_diffusers_config(SCREAMING_SNAKE_CASE_  ,					image_size=SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			custom_convert_ldm_vae_checkpoint(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			AutoencoderKL(**SCREAMING_SNAKE_CASE_					)
			vae.load_state_dict(SCREAMING_SNAKE_CASE_					)
			vae.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
							lowercase_						=       argparse.ArgumentParser()

							parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")

							lowercase_						=       parser.parse_args()

							vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)









from __future__ import annotations

from typing import TypedDict
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   str
				UpperCamelCase__			:   int



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)

			return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE_					)					)]



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)
			if not s:
						raise ValueError("The parameter s must not be empty."					)

			lowercase__   					=			all_rotations(SCREAMING_SNAKE_CASE_					)
			rotations.sort()  # sort the list of rotations in alphabetically order
			# make a string composed of the last char of each rotation
			lowercase__   					=			{
			    "bwt_string": "".join([word[-1] for word in rotations]					),
			    "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE_					),
			}
			return response



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter bwt_string type must be str."					)
			if not bwt_string:
						raise ValueError("The parameter bwt_string must not be empty."					)
			try:
						lowercase__   					=			int(SCREAMING_SNAKE_CASE_					)
			except ValueError:
						raise TypeError(
						    "The parameter idx_original_string type must be int or passive"
						    " of cast to int."					)
			if idx_original_string < 0:
						raise ValueError("The parameter idx_original_string must not be lower than 0."					)
			if idx_original_string >= len(SCREAMING_SNAKE_CASE_					):
						raise ValueError(
						    "The parameter idx_original_string must be lower than" " len(bwt_string)."					)

			lowercase__   					=			[""] * len(SCREAMING_SNAKE_CASE_					)
			for _ in range(len(SCREAMING_SNAKE_CASE_					)					):
						for i in range(len(SCREAMING_SNAKE_CASE_					)					):
									lowercase__   					=			bwt_string[i] + ordered_rotations[i]
						ordered_rotations.sort()
			return ordered_rotations[idx_original_string]


if __name__ == "__main__":
							lowercase_						=       """Provide a string that I will generate its BWT transform: """
							lowercase_						=       input(entry_msg).strip()
							lowercase_						=       bwt_transform(s)
							print(
							    F'Burrows Wheeler transform for string \'{s}\' results '
							    F'in \'{result["bwt_string"]}\''
							)
							lowercase_						=       reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
							print(
							    F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
							    F'we get original string \'{original_string}\''
							)




