



'''simple docstring'''





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	(					_snake_case	:    Union[str, Any]  ,_snake_case	:    List[str]							):





    '''simple docstring'''


    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(_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(_snake_case							)
    }

    for i in range(_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(_snake_case							)
        lowercase__									=    {"old": f'''down.{i}.block''', "new": f'''down_blocks.{i}.resnets'''}
        assign_to_checkpoint(_snake_case  ,_snake_case  ,_snake_case  ,additional_replacements=[meta_path]  ,config=_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(_snake_case							)
        lowercase__									=    {"old": f'''mid.block_{i}''', "new": f'''mid_block.resnets.{i - 1}'''}
        assign_to_checkpoint(_snake_case  ,_snake_case  ,_snake_case  ,additional_replacements=[meta_path]  ,config=_snake_case							)

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

    for i in range(_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(_snake_case							)
        lowercase__									=    {"old": f'''up.{block_id}.block''', "new": f'''up_blocks.{i}.resnets'''}
        assign_to_checkpoint(_snake_case  ,_snake_case  ,_snake_case  ,additional_replacements=[meta_path]  ,config=_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(_snake_case							)
        lowercase__									=    {"old": f'''mid.block_{i}''', "new": f'''mid_block.resnets.{i - 1}'''}
        assign_to_checkpoint(_snake_case  ,_snake_case  ,_snake_case  ,additional_replacements=[meta_path]  ,config=_snake_case							)

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




def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    str  ,):





    '''simple docstring'''


    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(_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(_snake_case  ,framework="pt"  ,device="cpu"							) as f:
            for key in f.keys():
                lowercase__									=    f.get_tensor(_snake_case							)
    else:
        lowercase__									=    torch.load(_snake_case  ,map_location=_snake_case							)["state_dict"]

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

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


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

 SCREAMING_SNAKE_CASE__          =  parser.parse_args()

 vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)









'''simple docstring'''





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_ = "▁"  ,UpperCAmelCase_ = True  ,UpperCAmelCase_ = "<unk>"  ,UpperCAmelCase_ = "</s>"  ,UpperCAmelCase_ = "<pad>"  ,)	->				Optional[int]:
           lowercase__									=    {
               "pad": {"id": 0, "token": pad_token},
               "eos": {"id": 1, "token": eos_token},
               "unk": {"id": 2, "token": unk_token},
           }

           lowercase__									=    [None] * len(self.special_tokens   )
           for token_dict in self.special_tokens.values():
               lowercase__									=    token_dict["token"]

           lowercase__									=    Tokenizer(Unigram()   )

           lowercase__									=    normalizers.Sequence(
               [
                   normalizers.Nmt(),
                   normalizers.NFKC(),
                   normalizers.Replace(Regex(" {2,}"   )  ," "   ),
                   normalizers.Lowercase(),
               ]   )
           lowercase__									=    pre_tokenizers.Sequence(
               [
                   pre_tokenizers.Metaspace(replacement=UpperCAmelCase_  ,add_prefix_space=UpperCAmelCase_   ),
                   pre_tokenizers.Digits(individual_digits=UpperCAmelCase_   ),
                   pre_tokenizers.Punctuation(),
               ]   )
           lowercase__									=    decoders.Metaspace(replacement=UpperCAmelCase_  ,add_prefix_space=UpperCAmelCase_   )

           lowercase__									=    TemplateProcessing(
               single=F'''$A {self.special_tokens['eos']['token']}'''  ,special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]  ,)

           lowercase__									=    {
               "model": "SentencePieceUnigram",
               "replacement": replacement,
               "add_prefix_space": add_prefix_space,
           }

           super().__init__(UpperCAmelCase_  ,UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = 8_000  ,UpperCAmelCase_ = True  ,)	->				List[str]:
           lowercase__									=    trainers.UnigramTrainer(
               vocab_size=UpperCAmelCase_  ,special_tokens=self.special_tokens_list  ,show_progress=UpperCAmelCase_  ,)

           if isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ):
               lowercase__									=    [files]
           self._tokenizer.train(UpperCAmelCase_  ,trainer=UpperCAmelCase_   )

           self.add_unk_id()



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = 8_000  ,UpperCAmelCase_ = True  ,)	->				Union[str, Any]:
           lowercase__									=    trainers.UnigramTrainer(
               vocab_size=UpperCAmelCase_  ,special_tokens=self.special_tokens_list  ,show_progress=UpperCAmelCase_  ,)

           self._tokenizer.train_from_iterator(UpperCAmelCase_  ,trainer=UpperCAmelCase_   )

           self.add_unk_id()







       def 				_a   (					self   )	->				str:
           lowercase__									=    json.loads(self._tokenizer.to_str()   )

           lowercase__									=    self.special_tokens["unk"]["id"]

           lowercase__									=    Tokenizer.from_str(json.dumps(UpperCAmelCase_   )   )




