





'''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  :			List[Any]			,							__snake_case  :			Dict      )					->					Union[str, Any]:
     __A						:			str								=	checkpoint

     __A						:			Optional[int]								=	{}

     __A						:			Optional[Any]								=	vae_state_dict['encoder.conv_in.weight']
     __A						:			Dict								=	vae_state_dict['encoder.conv_in.bias']
     __A						:			Optional[Any]								=	vae_state_dict['encoder.conv_out.weight']
     __A						:			Dict								=	vae_state_dict['encoder.conv_out.bias']
     __A						:			Dict								=	vae_state_dict['encoder.norm_out.weight']
     __A						:			Optional[Any]								=	vae_state_dict['encoder.norm_out.bias']

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

     __A						:			Optional[Any]								=	vae_state_dict['quant_conv.weight']
     __A						:			List[str]								=	vae_state_dict['quant_conv.bias']
     __A						:			List[str]								=	vae_state_dict['post_quant_conv.weight']
     __A						:			List[str]								=	vae_state_dict['post_quant_conv.bias']

     # Retrieves the keys for the encoder down blocks only
     __A						:			Tuple								=	len({'.'.join(layer.split('.'      )[:3]      ) for layer in vae_state_dict if 'encoder.down' in layer}      )
     __A						:			Optional[int]								=	{
         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
     __A						:			List[Any]								=	len({'.'.join(layer.split('.'      )[:3]      ) for layer in vae_state_dict if 'decoder.up' in layer}      )
     __A						:			Optional[int]								=	{
         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      ):
          __A						:			Any								=	[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:
               __A						:			Any								=	vae_state_dict.pop(
                   f'encoder.down.{i}.downsample.conv.weight'      )
               __A						:			List[Any]								=	vae_state_dict.pop(
                   f'encoder.down.{i}.downsample.conv.bias'      )

          __A						:			Optional[int]								=	renew_vae_resnet_paths(__snake_case      )
          __A						:			Optional[int]								=	{'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      )

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

          __A						:			Dict								=	renew_vae_resnet_paths(__snake_case      )
          __A						:			str								=	{'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      )

     __A						:			Union[str, Any]								=	[key for key in vae_state_dict if 'encoder.mid.attn' in key]
     __A						:			Tuple								=	renew_vae_attention_paths(__snake_case      )
     __A						:			Tuple								=	{'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      ):
          __A						:			Dict								=	num_up_blocks - 1 - i
          __A						:			Optional[int]								=	[
              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:
               __A						:			Dict								=	vae_state_dict[
                   f'decoder.up.{block_id}.upsample.conv.weight'
               ]
               __A						:			Union[str, Any]								=	vae_state_dict[
                   f'decoder.up.{block_id}.upsample.conv.bias'
               ]

          __A						:			int								=	renew_vae_resnet_paths(__snake_case      )
          __A						:			List[Any]								=	{'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      )

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

          __A						:			List[Any]								=	renew_vae_resnet_paths(__snake_case      )
          __A						:			List[str]								=	{'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      )

     __A						:			List[Any]								=	[key for key in vae_state_dict if 'decoder.mid.attn' in key]
     __A						:			Optional[Any]								=	renew_vae_attention_paths(__snake_case      )
     __A						:			int								=	{'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			,							)					->					Dict:
     # Only support V1
     __A						:			Union[str, Any]								=	requests.get(
         ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'      )
     __A						:			List[Any]								=	io.BytesIO(r.content      )

     __A						:			List[Any]								=	OmegaConf.load(__snake_case      )
     __A						:			List[str]								=	5_12
     __A						:			Dict								=	'cuda' if torch.cuda.is_available() else 'cpu'
     if checkpoint_path.endswith('safetensors'      ):
          from safetensors import safe_open

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

     # Convert the VAE model.
     __A						:			Optional[Any]								=	create_vae_diffusers_config(__snake_case			,							image_size=__snake_case      )
     __A						:			Any								=	custom_convert_ldm_vae_checkpoint(__snake_case			,							__snake_case      )

     __A						:			int								=	AutoencoderKL(**__snake_case      )
     vae.load_state_dict(__snake_case      )
     vae.save_pretrained(__snake_case      )


if __name__ == "__main__":
  lowercase__   :				Union[str, 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.''')

  lowercase__   :				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


lowercase__   :				Optional[Any]      =   logging.get_logger(__name__)

lowercase__   :				str      =   {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''roberta-prelayernorm'''


     def __init__(						self						,     _UpperCAmelCase=5_0265						,     _UpperCAmelCase=768						,     _UpperCAmelCase=12						,     _UpperCAmelCase=12						,     _UpperCAmelCase=3072						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-1_2						,     _UpperCAmelCase=1						,     _UpperCAmelCase=0						,     _UpperCAmelCase=2						,     _UpperCAmelCase="absolute"						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






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

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




class 			SCREAMING_SNAKE_CASE   (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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