'''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 __UpperCamelCase( _A       :					Tuple	,      _A       :					Any     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[Any] 					=	checkpoint

   UpperCAmelCase__				:					Dict 					=	{}

   UpperCAmelCase__				:					str 					=	vae_state_dict['''encoder.conv_in.weight''']
   UpperCAmelCase__				:					Any 					=	vae_state_dict['''encoder.conv_in.bias''']
   UpperCAmelCase__				:					Any 					=	vae_state_dict['''encoder.conv_out.weight''']
   UpperCAmelCase__				:					Optional[Any] 					=	vae_state_dict['''encoder.conv_out.bias''']
   UpperCAmelCase__				:					Any 					=	vae_state_dict['''encoder.norm_out.weight''']
   UpperCAmelCase__				:					str 					=	vae_state_dict['''encoder.norm_out.bias''']

   UpperCAmelCase__				:					Optional[int] 					=	vae_state_dict['''decoder.conv_in.weight''']
   UpperCAmelCase__				:					Optional[int] 					=	vae_state_dict['''decoder.conv_in.bias''']
   UpperCAmelCase__				:					Optional[Any] 					=	vae_state_dict['''decoder.conv_out.weight''']
   UpperCAmelCase__				:					List[Any] 					=	vae_state_dict['''decoder.conv_out.bias''']
   UpperCAmelCase__				:					List[Any] 					=	vae_state_dict['''decoder.norm_out.weight''']
   UpperCAmelCase__				:					Union[str, Any] 					=	vae_state_dict['''decoder.norm_out.bias''']

   UpperCAmelCase__				:					Dict 					=	vae_state_dict['''quant_conv.weight''']
   UpperCAmelCase__				:					Dict 					=	vae_state_dict['''quant_conv.bias''']
   UpperCAmelCase__				:					int 					=	vae_state_dict['''post_quant_conv.weight''']
   UpperCAmelCase__				:					str 					=	vae_state_dict['''post_quant_conv.bias''']

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

   # Retrieves the keys for the decoder up blocks only
   UpperCAmelCase__				:					Union[str, Any] 					=	len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in vae_state_dict if '''decoder.up''' in layer}     )
   UpperCAmelCase__				:					str 					=	{
       layer_id: [key for key in vae_state_dict if F'''up.{layer_id}''' in key] for layer_id in range(_A     )
   }

   for i in range(_A     ):
      UpperCAmelCase__				:					Optional[int] 					=	[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:
         UpperCAmelCase__				:					int 					=	vae_state_dict.pop(
             F'''encoder.down.{i}.downsample.conv.weight'''     )
         UpperCAmelCase__				:					List[Any] 					=	vae_state_dict.pop(
             F'''encoder.down.{i}.downsample.conv.bias'''     )

      UpperCAmelCase__				:					List[str] 					=	renew_vae_resnet_paths(_A     )
      UpperCAmelCase__				:					str 					=	{'''old''': F'''down.{i}.block''', '''new''': F'''down_blocks.{i}.resnets'''}
      assign_to_checkpoint(_A	,      _A	,      _A	,      additional_replacements=[meta_path]	,      config=_A     )

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

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

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

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

      UpperCAmelCase__				:					Dict 					=	renew_vae_resnet_paths(_A     )
      UpperCAmelCase__				:					str 					=	{'''old''': F'''up.{block_id}.block''', '''new''': F'''up_blocks.{i}.resnets'''}
      assign_to_checkpoint(_A	,      _A	,      _A	,      additional_replacements=[meta_path]	,      config=_A     )

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

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

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







def __UpperCamelCase( _A       :					str	,      _A       :					str	,      ):
   '''simple docstring'''
   # Only support V1
   UpperCAmelCase__				:					Dict 					=	requests.get(
       ''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'''     )
   UpperCAmelCase__				:					str 					=	io.BytesIO(r.content     )

   UpperCAmelCase__				:					List[str] 					=	OmegaConf.load(_A     )
   UpperCAmelCase__				:					Optional[int] 					=	5_12
   UpperCAmelCase__				:					str 					=	'''cuda''' if torch.cuda.is_available() else '''cpu'''
   if checkpoint_path.endswith('''safetensors'''     ):
      from safetensors import safe_open

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

   # Convert the VAE model.
   UpperCAmelCase__				:					Optional[int] 					=	create_vae_diffusers_config(_A	,      image_size=_A     )
   UpperCAmelCase__				:					int 					=	custom_convert_ldm_vae_checkpoint(_A	,      _A     )

   UpperCAmelCase__				:					Optional[Any] 					=	AutoencoderKL(**_A     )
   vae.load_state_dict(_A     )
   vae.save_pretrained(_A     )


if __name__ == "__main__":
  UpperCamelCase__  :						int					=      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__  :						Union[str, Any]					=      parser.parse_args()

  vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__  :						List[str]					=      logging.get_logger(__name__)

UpperCamelCase__  :						str					=      {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Dict   				= '''camembert'''




      def __init__(					self       ,lowerCamelCase_=30522       ,lowerCamelCase_=768       ,lowerCamelCase_=12       ,lowerCamelCase_=12       ,lowerCamelCase_=3072       ,lowerCamelCase_="gelu"       ,lowerCamelCase_=0.1       ,lowerCamelCase_=0.1       ,lowerCamelCase_=512       ,lowerCamelCase_=2       ,lowerCamelCase_=0.02       ,lowerCamelCase_=1e-12       ,lowerCamelCase_=1       ,lowerCamelCase_=0       ,lowerCamelCase_=2       ,lowerCamelCase_="absolute"       ,lowerCamelCase_=True       ,lowerCamelCase_=None       ,**lowerCamelCase_       ,)      ->      str:



         '''simple docstring'''



         super().__init__(pad_token_id=lowerCamelCase_       ,bos_token_id=lowerCamelCase_       ,eos_token_id=lowerCamelCase_       ,**lowerCamelCase_				)

         UpperCAmelCase__				:					int 					=	vocab_size
         UpperCAmelCase__				:					List[Any] 					=	hidden_size
         UpperCAmelCase__				:					Tuple 					=	num_hidden_layers
         UpperCAmelCase__				:					List[str] 					=	num_attention_heads
         UpperCAmelCase__				:					int 					=	hidden_act
         UpperCAmelCase__				:					Tuple 					=	intermediate_size
         UpperCAmelCase__				:					List[Any] 					=	hidden_dropout_prob
         UpperCAmelCase__				:					Tuple 					=	attention_probs_dropout_prob
         UpperCAmelCase__				:					str 					=	max_position_embeddings
         UpperCAmelCase__				:					Union[str, Any] 					=	type_vocab_size
         UpperCAmelCase__				:					int 					=	initializer_range
         UpperCAmelCase__				:					Optional[Any] 					=	layer_norm_eps
         UpperCAmelCase__				:					Optional[Any] 					=	position_embedding_type
         UpperCAmelCase__				:					str 					=	use_cache
         UpperCAmelCase__				:					List[Any] 					=	classifier_dropout




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      @property
      def 		lowerCAmelCase__       (					self				)      ->      Mapping[str, Mapping[int, str]]:



         '''simple docstring'''



         if self.task == "multiple-choice":
            UpperCAmelCase__				:					Tuple 					=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
            UpperCAmelCase__				:					Union[str, Any] 					=	{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]				)




