

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     UNetaDModel(**_SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Union[str, Any]	   =     checkpoint['all_modules.0.W'].data
     _lowerCAmelCase :       Optional[Any]	   =     checkpoint['all_modules.0.W'].data
     _lowerCAmelCase :       int	   =     checkpoint['all_modules.1.weight'].data
     _lowerCAmelCase :       Dict	   =     checkpoint['all_modules.1.bias'].data

     _lowerCAmelCase :       Any	   =     checkpoint['all_modules.2.weight'].data
     _lowerCAmelCase :       List[Any]	   =     checkpoint['all_modules.2.bias'].data

     _lowerCAmelCase :       Tuple	   =     checkpoint['all_modules.3.weight'].data
     _lowerCAmelCase :       Optional[int]	   =     checkpoint['all_modules.3.bias'].data

     _lowerCAmelCase :       int	   =     checkpoint[list(checkpoint.keys()					)[-4]].data
     _lowerCAmelCase :       Optional[Any]	   =     checkpoint[list(checkpoint.keys()					)[-3]].data
     _lowerCAmelCase :       Any	   =     checkpoint[list(checkpoint.keys()					)[-2]].data
     _lowerCAmelCase :       int	   =     checkpoint[list(checkpoint.keys()					)[-1]].data

     _lowerCAmelCase :       Tuple	   =     4

     def set_attention_weights(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					):
          _lowerCAmelCase :       Dict	   =     old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
          _lowerCAmelCase :       Dict	   =     old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
          _lowerCAmelCase :       str	   =     old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

          _lowerCAmelCase :       List[str]	   =     old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
          _lowerCAmelCase :       Union[str, Any]	   =     old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
          _lowerCAmelCase :       str	   =     old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

          _lowerCAmelCase :       Union[str, Any]	   =     old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
          _lowerCAmelCase :       Optional[int]	   =     old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

          _lowerCAmelCase :       Optional[Any]	   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
          _lowerCAmelCase :       Dict	   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

     def set_resnet_weights(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					):
          _lowerCAmelCase :       int	   =     old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
          _lowerCAmelCase :       Dict	   =     old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
          _lowerCAmelCase :       str	   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
          _lowerCAmelCase :       Any	   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

          _lowerCAmelCase :       Any	   =     old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
          _lowerCAmelCase :       Union[str, Any]	   =     old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
          _lowerCAmelCase :       int	   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
          _lowerCAmelCase :       Optional[int]	   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

          _lowerCAmelCase :       str	   =     old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
          _lowerCAmelCase :       Union[str, Any]	   =     old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

          if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
               _lowerCAmelCase :       Union[str, Any]	   =     old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
               _lowerCAmelCase :       Any	   =     old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

     for i, block in enumerate(new_model_architecture.downsample_blocks					):
          _lowerCAmelCase :       Tuple	   =     hasattr(_SCREAMING_SNAKE_CASE		,				"attentions"					)
          for j in range(len(block.resnets					)					):
               set_resnet_weights(block.resnets[j]		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)
               module_index += 1
               if has_attentions:
                    set_attention_weights(block.attentions[j]		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)
                    module_index += 1

          if hasattr(_SCREAMING_SNAKE_CASE		,				"downsamplers"					) and block.downsamplers is not None:
               set_resnet_weights(block.resnet_down		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)
               module_index += 1
               _lowerCAmelCase :       Any	   =     checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
               _lowerCAmelCase :       str	   =     checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
               module_index += 1

     set_resnet_weights(new_model_architecture.mid_block.resnets[0]		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)
     module_index += 1
     set_attention_weights(new_model_architecture.mid_block.attentions[0]		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)
     module_index += 1
     set_resnet_weights(new_model_architecture.mid_block.resnets[1]		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)
     module_index += 1

     for i, block in enumerate(new_model_architecture.up_blocks					):
          _lowerCAmelCase :       str	   =     hasattr(_SCREAMING_SNAKE_CASE		,				"attentions"					)
          for j in range(len(block.resnets					)					):
               set_resnet_weights(block.resnets[j]		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)
               module_index += 1
          if has_attentions:
               set_attention_weights(
                   block.attentions[0]		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)  # why can there only be a single attention layer for up?
               module_index += 1

          if hasattr(_SCREAMING_SNAKE_CASE		,				"resnet_up"					) and block.resnet_up is not None:
               _lowerCAmelCase :       List[Any]	   =     checkpoint[f'''all_modules.{module_index}.weight'''].data
               _lowerCAmelCase :       Dict	   =     checkpoint[f'''all_modules.{module_index}.bias'''].data
               module_index += 1
               _lowerCAmelCase :       Dict	   =     checkpoint[f'''all_modules.{module_index}.weight'''].data
               _lowerCAmelCase :       Optional[int]	   =     checkpoint[f'''all_modules.{module_index}.bias'''].data
               module_index += 1
               set_resnet_weights(block.resnet_up		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE					)
               module_index += 1

     _lowerCAmelCase :       str	   =     checkpoint[f'''all_modules.{module_index}.weight'''].data
     _lowerCAmelCase :       str	   =     checkpoint[f'''all_modules.{module_index}.bias'''].data
     module_index += 1
     _lowerCAmelCase :       Union[str, Any]	   =     checkpoint[f'''all_modules.{module_index}.weight'''].data
     _lowerCAmelCase :       Tuple	   =     checkpoint[f'''all_modules.{module_index}.bias'''].data

     return new_model_architecture.state_dict()


if __name__ == "__main__":
  __UpperCAmelCase     					=  argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_path',
      default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
      type=str,
      required=False,
      help='Path to the checkpoint to convert.',
  )

  parser.add_argument(
      '--config_file',
      default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
      type=str,
      required=False,
      help='The config json file corresponding to the architecture.',
  )

  parser.add_argument(
      '--dump_path',
      default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
      type=str,
      required=False,
      help='Path to the output model.',
  )

  __UpperCAmelCase     					=  parser.parse_args()

  __UpperCAmelCase     					=  torch.load(args.checkpoint_path, map_location='cpu')

  with open(args.config_file) as f:
    __UpperCAmelCase     					=  json.loads(f.read())

  __UpperCAmelCase     					=  convert_ncsnpp_checkpoint(
      checkpoint,
      config,
  )

  if "sde" in config:
    del config["sde"]

  __UpperCAmelCase     					=  UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    __UpperCAmelCase     					=  ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

    __UpperCAmelCase     					=  ScoreSdeVePipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)







"""simple docstring"""
from typing import Dict, List, Optional, Tuple

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


lowercase__   			=      logging.get_logger(__name__)




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						["""input_ids""", """attention_mask"""]



      def __init__(		self       ,     lowercase="[PAD]"       ,     lowercase="[BOS]"       ,     lowercase="[EOS]"       ,     lowercase="[MASK]"       ,     lowercase="[CLS]"       ,     lowercase="[SEP]"       ,     lowercase=20_48       ,     **lowercase       ,     )      ->     None:





        '''simple docstring'''





        a__:	Any     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else pad_token
        a__:	Tuple     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else bos_token
        a__:	Union[str, Any]     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else eos_token
        a__:	Tuple     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else mask_token
        a__:	int     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else cls_token
        a__:	Any     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else sep_token

        super().__init__(
            pad_token=lowercase       ,     bos_token=lowercase       ,     eos_token=lowercase       ,     mask_token=lowercase       ,     cls_token=lowercase       ,     sep_token=lowercase       ,     model_max_length=lowercase       ,     **lowercase       ,     )

        a__:	Optional[Any]     				=      2**8  # utf is 8 bits

        # define special tokens dict
        a__:	Dict[str, int]     				=      {
            self.pad_token: 0,
            self.bos_token: 1,
            self.eos_token: 2,
            self.mask_token: 3,
            self.cls_token: 4,
            self.sep_token: 5,
        }
        a__:	Union[str, Any]     				=      len(self.special_tokens_encoder)
        a__:	Dict[int, str]     				=      {v: k for k, v in self.special_tokens_encoder.items()}



      def     lowerCamelCase_  (		self)      ->     Dict[str, int]:





        '''simple docstring'''





        a__:	Optional[int]     				=      self.special_tokens_encoder.copy()
        vocab.update(self.added_tokens_encoder)
        for i in range(self._utf_vocab_size):
          a__:	Optional[int]     				=      chr(lowercase)
          a__:	str     				=      i + len(self.special_tokens_encoder)
        return vocab



      @property
      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        return self._utf_vocab_size + self._num_special_tokens



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = False)      ->     List[int]:





        '''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)

        # normal case: some special tokens
        if token_ids_a is None:
          return [1] + [0] * len(lowercase) + [1]
        return [1] + ([0] * len(lowercase)) + [1] + ([0] * len(lowercase)) + [1]



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None)      ->     List[int]:





        '''simple docstring'''





        if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        else:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        a__:	Any     				=      [chr(lowercase) for i in text.encode('utf-8')]
        return tokens



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Union[str, Any]:





        '''simple docstring'''





        if token in self.special_tokens_encoder:
          a__:	Optional[int]     				=      self.special_tokens_encoder[token]
        elif token in self.added_tokens_encoder:
          a__:	Optional[int]     				=      self.added_tokens_encoder[token]
        elif len(lowercase) != 1:
          a__:	Dict     				=      self.unk_token_id
        else:
          a__:	Optional[Any]     				=      ord(lowercase) + self._num_special_tokens
        return token_id



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        if index in self.special_tokens_decoder:
          a__:	List[Any]     				=      self.special_tokens_decoder[index]
        elif index in self.added_tokens_decoder:
          a__:	Tuple     				=      self.added_tokens_decoder[index]
        else:
          a__:	List[str]     				=      chr(index - self._num_special_tokens)
        return token



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Any     				=      B''
        for token in tokens:
          if token in self.special_tokens_decoder:
            a__:	str     				=      self.special_tokens_decoder[token].encode('utf-8')
          elif token in self.added_tokens_decoder:
            a__:	Optional[Any]     				=      self.special_tokens_decoder[token].encode('utf-8')
          elif token in self.special_tokens_encoder:
            a__:	Optional[Any]     				=      token.encode('utf-8')
          elif token in self.added_tokens_encoder:
            a__:	str     				=      token.encode('utf-8')
          else:
            a__:	Tuple     				=      bytes([ord(lowercase)])
          bstring += tok_string
        a__:	List[str]     				=      bstring.decode('utf-8'       ,     errors='replace')
        return string



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None)      ->     Tuple[str]:





        '''simple docstring'''





        return ()


