



'''simple docstring'''





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase											=						logging.get_logger(__name__)

_UpperCamelCase											=						{'vocab_file': 'vocab.json'}

_UpperCamelCase											=						{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_UpperCamelCase											=						{'mgp-str': 27}






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=VOCAB_FILES_NAMES
  a_												=PRETRAINED_VOCAB_FILES_MAP
  a_												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




  def __init__( self      :	Optional[int]			,	_a      :	Optional[int]			,	_a      :	Dict="[GO]"			,	_a      :	Dict="[GO]"			,	_a      :	List[str]="[s]"			,	_a      :	Dict="[GO]"			,	**_a      :	Tuple   )					->					List[Any]:
    super().__init__(
        unk_token=_a			,	bos_token=_a			,	eos_token=_a			,	pad_token=_a			,	**_a			,	)

    with open(_a			,	encoding='utf-8'   ) as vocab_handle:
      __lowerCamelCase       :       str           = json.load(_a   )
    __lowerCamelCase       :       Optional[Any]           = {v: k for k, v in self.vocab.items()}




  @property
  def 						_lowercase     ( self      :	Any   )					->					List[Any]:
    return len(self.vocab   )




  def 						_lowercase     ( self      :	str   )					->					int:
    return dict(self.vocab			,	**self.added_tokens_encoder   )




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	str   )					->					Optional[int]:
    __lowerCamelCase       :       List[str]           = []
    for s in text:
      char_tokens.extend(_a   )
    return char_tokens




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Tuple   )					->					Optional[Any]:
    return self.vocab.get(_a			,	self.vocab.get(self.unk_token   )   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	Optional[int]   )					->					Dict:
    return self.decoder.get(_a   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	str			,	_a      :	Optional[str] = None   )					->					Tuple[str]:
    if not os.path.isdir(_a   ):
      logger.error('Vocabulary path ({}) should be a directory'.format(_a   )   )
      return
    __lowerCamelCase       :       Dict           = os.path.join(
        _a			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

    with open(_a			,	'w'			,	encoding='utf-8'   ) as f:
      f.write(json.dumps(self.vocab			,	indent=2			,	sort_keys=_a			,	ensure_ascii=_a   ) + '\n'   )

    return (vocab_file,)











'''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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Dict:
  __lowerCamelCase       :       Any           = checkpoint

  __lowerCamelCase       :       List[str]           = {}

  __lowerCamelCase       :       List[str]           = vae_state_dict['encoder.conv_in.weight']
  __lowerCamelCase       :       Dict           = vae_state_dict['encoder.conv_in.bias']
  __lowerCamelCase       :       Optional[Any]           = vae_state_dict['encoder.conv_out.weight']
  __lowerCamelCase       :       Optional[int]           = vae_state_dict['encoder.conv_out.bias']
  __lowerCamelCase       :       Optional[int]           = vae_state_dict['encoder.norm_out.weight']
  __lowerCamelCase       :       Optional[int]           = vae_state_dict['encoder.norm_out.bias']

  __lowerCamelCase       :       Dict           = vae_state_dict['decoder.conv_in.weight']
  __lowerCamelCase       :       List[str]           = vae_state_dict['decoder.conv_in.bias']
  __lowerCamelCase       :       Tuple           = vae_state_dict['decoder.conv_out.weight']
  __lowerCamelCase       :       List[str]           = vae_state_dict['decoder.conv_out.bias']
  __lowerCamelCase       :       Dict           = vae_state_dict['decoder.norm_out.weight']
  __lowerCamelCase       :       Union[str, Any]           = vae_state_dict['decoder.norm_out.bias']

  __lowerCamelCase       :       Any           = vae_state_dict['quant_conv.weight']
  __lowerCamelCase       :       Union[str, Any]           = vae_state_dict['quant_conv.bias']
  __lowerCamelCase       :       Tuple           = vae_state_dict['post_quant_conv.weight']
  __lowerCamelCase       :       List[Any]           = vae_state_dict['post_quant_conv.bias']

  # Retrieves the keys for the encoder down blocks only
  __lowerCamelCase       :       str           = len({'.'.join(layer.split('.'       )[:3]       ) for layer in vae_state_dict if 'encoder.down' in layer}       )
  __lowerCamelCase       :       int           = {
      layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(_lowerCAmelCase       )
  }

  # Retrieves the keys for the decoder up blocks only
  __lowerCamelCase       :       Dict           = len({'.'.join(layer.split('.'       )[:3]       ) for layer in vae_state_dict if 'decoder.up' in layer}       )
  __lowerCamelCase       :       Dict           = {
      layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(_lowerCAmelCase       )
  }

  for i in range(_lowerCAmelCase       ):
    __lowerCamelCase       :       List[str]           = [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:
      __lowerCamelCase       :       Any           = vae_state_dict.pop(
          F'encoder.down.{i}.downsample.conv.weight'       )
      __lowerCamelCase       :       Any           = vae_state_dict.pop(
          F'encoder.down.{i}.downsample.conv.bias'       )

    __lowerCamelCase       :       Optional[int]           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = {'old': F'down.{i}.block', 'new': F'down_blocks.{i}.resnets'}
    assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )

  __lowerCamelCase       :       List[Any]           = [key for key in vae_state_dict if 'encoder.mid.block' in key]
  __lowerCamelCase       :       str           = 2
  for i in range(1       ,num_mid_res_blocks + 1       ):
    __lowerCamelCase       :       int           = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

    __lowerCamelCase       :       Optional[Any]           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       Dict           = {'old': F'mid.block_{i}', 'new': F'mid_block.resnets.{i - 1}'}
    assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )

  __lowerCamelCase       :       Optional[Any]           = [key for key in vae_state_dict if 'encoder.mid.attn' in key]
  __lowerCamelCase       :       int           = renew_vae_attention_paths(_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[Any]           = {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )
  conv_attn_to_linear(_lowerCAmelCase       )

  for i in range(_lowerCAmelCase       ):
    __lowerCamelCase       :       Optional[Any]           = num_up_blocks - 1 - i
    __lowerCamelCase       :       Any           = [
        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:
      __lowerCamelCase       :       Optional[int]           = vae_state_dict[
          F'decoder.up.{block_id}.upsample.conv.weight'
      ]
      __lowerCamelCase       :       Optional[Any]           = vae_state_dict[
          F'decoder.up.{block_id}.upsample.conv.bias'
      ]

    __lowerCamelCase       :       Tuple           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = {'old': F'up.{block_id}.block', 'new': F'up_blocks.{i}.resnets'}
    assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )

  __lowerCamelCase       :       Optional[Any]           = [key for key in vae_state_dict if 'decoder.mid.block' in key]
  __lowerCamelCase       :       Union[str, Any]           = 2
  for i in range(1       ,num_mid_res_blocks + 1       ):
    __lowerCamelCase       :       List[str]           = [key for key in mid_resnets if F'decoder.mid.block_{i}' in key]

    __lowerCamelCase       :       str           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = {'old': F'mid.block_{i}', 'new': F'mid_block.resnets.{i - 1}'}
    assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )

  __lowerCamelCase       :       Any           = [key for key in vae_state_dict if 'decoder.mid.attn' in key]
  __lowerCamelCase       :       List[str]           = renew_vae_attention_paths(_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[Any]           = {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )
  conv_attn_to_linear(_lowerCAmelCase       )
  return new_checkpoint

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,)      ->      List[Any]:
  # Only support V1
  __lowerCamelCase       :       Any           = requests.get(
      ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'       )
  __lowerCamelCase       :       str           = io.BytesIO(r.content       )

  __lowerCamelCase       :       Any           = OmegaConf.load(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = 512
  __lowerCamelCase       :       Dict           = 'cuda' if torch.cuda.is_available() else 'cpu'
  if checkpoint_path.endswith('safetensors'       ):
    from safetensors import safe_open

    __lowerCamelCase       :       Optional[Any]           = {}
    with safe_open(_lowerCAmelCase       ,framework='pt'       ,device='cpu'       ) as f:
      for key in f.keys():
        __lowerCamelCase       :       int           = f.get_tensor(_lowerCAmelCase       )
  else:
    __lowerCamelCase       :       int           = torch.load(_lowerCAmelCase       ,map_location=_lowerCAmelCase       )['state_dict']

  # Convert the VAE model.
  __lowerCamelCase       :       int           = create_vae_diffusers_config(_lowerCAmelCase       ,image_size=_lowerCAmelCase       )
  __lowerCamelCase       :       int           = custom_convert_ldm_vae_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       )

  __lowerCamelCase       :       Any           = AutoencoderKL(**_lowerCAmelCase       )
  vae.load_state_dict(_lowerCAmelCase       )
  vae.save_pretrained(_lowerCAmelCase       )


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

   _UpperCamelCase											=						parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)






