import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase      :			Tuple							       =						logging.get_logger(__name__)

__lowerCamelCase      :			Union[str, Any]							       =						'''▁'''

__lowerCamelCase      :			str							       =						{'''vocab_file''': '''sentencepiece.bpe.model'''}

__lowerCamelCase      :			Optional[Any]							       =						{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
        ),
    }
}

__lowerCamelCase      :			Any							       =						{
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
__lowerCamelCase      :			Tuple							       =						['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							["input_ids", "attention_mask"]

   lowerCAmelCase_														=							[]
   lowerCAmelCase_														=							[]




   def __init__(   self      :    List[str]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Union[str, Any]="<s>"	,     _lowercase      :    Any="</s>"	,     _lowercase      :    Optional[int]="</s>"	,     _lowercase      :    Dict="<s>"	,     _lowercase      :    Tuple="<unk>"	,     _lowercase      :    Tuple="<pad>"	,     _lowercase      :    Optional[Any]="<mask>"	,     _lowercase      :    Any=None	,     _lowercase      :    Optional[int]=None	,     _lowercase      :    Union[str, Any]=None	,     _lowercase      :    Optional[Dict[str, Any]] = None	,     _lowercase      :    List[str]=None	,     _lowercase      :    int=False	,     **_lowercase      :    Optional[int]	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AddedToken(_lowercase	,     lstrip=_lowercase	,     rstrip=_lowercase				) if isinstance(_lowercase	,     _lowercase				) else mask_token

     SCREAMING_SNAKE_CASE__      							=				{} if sp_model_kwargs is None else sp_model_kwargs
     SCREAMING_SNAKE_CASE__      							=				legacy_behaviour

     super().__init__(
         bos_token=_lowercase	,     eos_token=_lowercase	,     unk_token=_lowercase	,     sep_token=_lowercase	,     cls_token=_lowercase	,     pad_token=_lowercase	,     mask_token=_lowercase	,     tokenizer_file=_lowercase	,     src_lang=_lowercase	,     tgt_lang=_lowercase	,     additional_special_tokens=_lowercase	,     sp_model_kwargs=self.sp_model_kwargs	,     legacy_behaviour=_lowercase	,     **_lowercase	,     )

     SCREAMING_SNAKE_CASE__      							=				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
     self.sp_model.Load(str(_lowercase				)				)
     SCREAMING_SNAKE_CASE__      							=				vocab_file
     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
     # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
     # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

     # Mimic fairseq token-to-id alignment for the first 4 token
     SCREAMING_SNAKE_CASE__      							=				{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     SCREAMING_SNAKE_CASE__      							=				1

     SCREAMING_SNAKE_CASE__      							=				len(self.sp_model				)
     SCREAMING_SNAKE_CASE__      							=				{
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowercase				)
     }
     SCREAMING_SNAKE_CASE__      							=				{v: k for k, v in self.lang_code_to_id.items()}
     SCREAMING_SNAKE_CASE__      							=				len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset

     self.fairseq_tokens_to_ids.update(self.lang_code_to_id				)
     SCREAMING_SNAKE_CASE__      							=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
     SCREAMING_SNAKE_CASE__      							=				list(self.lang_code_to_id.keys()				)

     if additional_special_tokens is not None:
       # Only add those special tokens if they are not already there.
       self._additional_special_tokens.extend(
           [t for t in additional_special_tokens if t not in self._additional_special_tokens]				)

     SCREAMING_SNAKE_CASE__      							=				src_lang if src_lang is not None else """eng_Latn"""
     SCREAMING_SNAKE_CASE__      							=				self.lang_code_to_id[self._src_lang]
     SCREAMING_SNAKE_CASE__      							=				tgt_lang
     self.set_src_lang_special_tokens(self._src_lang				)




   def __getstate__(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.__dict__.copy()
     SCREAMING_SNAKE_CASE__      							=				None
     SCREAMING_SNAKE_CASE__      							=				self.sp_model.serialized_model_proto()
     return state




   def __setstate__(   self      :    int	,     _lowercase      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				d

     # for backward compatibility
     if not hasattr(self	,     """sp_model_kwargs"""				):
       SCREAMING_SNAKE_CASE__      							=				{}

     SCREAMING_SNAKE_CASE__      							=				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)




   @property
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     return len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset + 1  # Plus 1 for the mask token




   @property
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     return self._src_lang




   @src_lang.setter
   def      __a						(   self      :    int	,     _lowercase      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				new_src_lang
     self.set_src_lang_special_tokens(self._src_lang				)




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    List[int]	,     _lowercase      :    Optional[List[int]] = None	,     _lowercase      :    bool = False				):



     """simple docstring"""






     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowercase	,     token_ids_a=_lowercase	,     already_has_special_tokens=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				[1] * len(self.prefix_tokens				)
     SCREAMING_SNAKE_CASE__      							=				[1] * len(self.suffix_tokens				)
     if token_ids_a is None:
       return prefix_ones + ([0] * len(_lowercase				)) + suffix_ones
     return prefix_ones + ([0] * len(_lowercase				)) + ([0] * len(_lowercase				)) + suffix_ones




   def      __a						(   self      :    List[str]	,     _lowercase      :    List[int]	,     _lowercase      :    Optional[List[int]] = None				):



     """simple docstring"""






     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + self.suffix_tokens
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




   def      __a						(   self      :    List[str]	,     _lowercase      :    List[int]	,     _lowercase      :    Optional[List[int]] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				[self.sep_token_id]
     SCREAMING_SNAKE_CASE__      							=				[self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep				) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]




   def      __a						(   self      :    Dict	,     _lowercase      :    List[Any]	,     _lowercase      :    str	,     _lowercase      :    Optional[str]	,     _lowercase      :    Optional[str]	,     **_lowercase      :    int				):



     """simple docstring"""






     if src_lang is None or tgt_lang is None:
       raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""				)
     SCREAMING_SNAKE_CASE__      							=				src_lang
     SCREAMING_SNAKE_CASE__      							=				self(_lowercase	,     add_special_tokens=_lowercase	,     return_tensors=_lowercase	,     **_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				self.convert_tokens_to_ids(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				tgt_lang_id
     return inputs




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				{self.convert_ids_to_tokens(_lowercase				): i for i in range(self.vocab_size				)}
     vocab.update(self.added_tokens_encoder				)
     return vocab




   def      __a						(   self      :    Any	,     _lowercase      :    str				):



     """simple docstring"""






     return self.sp_model.encode(_lowercase	,     out_type=_lowercase				)




   def      __a						(   self      :    Tuple	,     _lowercase      :    Any				):



     """simple docstring"""






     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     SCREAMING_SNAKE_CASE__      							=				self.sp_model.PieceToId(_lowercase				)

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




   def      __a						(   self      :    str	,     _lowercase      :    int				):



     """simple docstring"""






     if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset				)




   def      __a						(   self      :    List[str]	,     _lowercase      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""""".join(_lowercase				).replace(_lowercase	,     """ """				).strip()
     return out_string




   def      __a						(   self      :    List[Any]	,     _lowercase      :    str	,     _lowercase      :    Optional[str] = None				):



     """simple docstring"""






     if not os.path.isdir(_lowercase				):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
       return
     SCREAMING_SNAKE_CASE__      							=				os.path.join(
         _lowercase	,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

     if os.path.abspath(self.vocab_file				) != os.path.abspath(_lowercase				) and os.path.isfile(self.vocab_file				):
       copyfile(self.vocab_file	,     _lowercase				)
     elif not os.path.isfile(self.vocab_file				):
       with open(_lowercase	,     """wb"""				) as fi:
         SCREAMING_SNAKE_CASE__      							=				self.sp_model.serialized_model_proto()
         fi.write(_lowercase				)

     return (out_vocab_file,)




   def      __a						(   self      :    Any	,     _lowercase      :    List[str]	,     _lowercase      :    str = "eng_Latn"	,     _lowercase      :    Optional[List[str]] = None	,     _lowercase      :    str = "fra_Latn"	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				src_lang
     SCREAMING_SNAKE_CASE__      							=				tgt_lang
     return super().prepare_seqaseq_batch(_lowercase	,     _lowercase	,     **_lowercase				)




   def      __a						(   self      :    Any				):



     """simple docstring"""






     return self.set_src_lang_special_tokens(self.src_lang				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     return self.set_tgt_lang_special_tokens(self.tgt_lang				)




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.lang_code_to_id[src_lang]
     if self.legacy_behaviour:
       SCREAMING_SNAKE_CASE__      							=				[]
       SCREAMING_SNAKE_CASE__      							=				[self.eos_token_id, self.cur_lang_code]
     else:
       SCREAMING_SNAKE_CASE__      							=				[self.cur_lang_code]
       SCREAMING_SNAKE_CASE__      							=				[self.eos_token_id]




   def      __a						(   self      :    int	,     _lowercase      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.lang_code_to_id[lang]
     if self.legacy_behaviour:
       SCREAMING_SNAKE_CASE__      							=				[]
       SCREAMING_SNAKE_CASE__      							=				[self.eos_token_id, self.cur_lang_code]
     else:
       SCREAMING_SNAKE_CASE__      							=				[self.cur_lang_code]
       SCREAMING_SNAKE_CASE__      							=				[self.eos_token_id]

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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str     )							-> Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				checkpoint

  SCREAMING_SNAKE_CASE__      							=				{}

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

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

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

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

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

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				[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:
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.weight"""     )
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.bias"""     )

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

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

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

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

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				num_up_blocks - 1 - i
    SCREAMING_SNAKE_CASE__      							=				[
        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:
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.weight"""
      ]
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.bias"""
      ]

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

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

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

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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str			,					__UpperCamelCase   :	str			,					)							-> Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				requests.get(
      """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""     )
  SCREAMING_SNAKE_CASE__      							=				io.BytesIO(r.content     )

  SCREAMING_SNAKE_CASE__      							=				OmegaConf.load(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				5_12
  SCREAMING_SNAKE_CASE__      							=				"""cuda""" if torch.cuda.is_available() else """cpu"""
  if checkpoint_path.endswith("""safetensors"""     ):
    from safetensors import safe_open

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

  # Convert the VAE model.
  SCREAMING_SNAKE_CASE__      							=				create_vae_diffusers_config(__UpperCamelCase			,					image_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				custom_convert_ldm_vae_checkpoint(__UpperCamelCase			,					__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				AutoencoderKL(**__UpperCamelCase     )
  vae.load_state_dict(__UpperCamelCase     )
  vae.save_pretrained(__UpperCamelCase     )


if __name__ == "__main__":
   __lowerCamelCase      :			List[Any]							       =		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.''')

   __lowerCamelCase      :			Optional[int]							       =		parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
