'''simple docstring'''




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def 			A_   ( _lowerCamelCase			:							Optional[Any]  ,  _lowerCamelCase			:							List[str]    ):
      _lowerCAmelCase		   =   UNetaDModel(**_lowerCamelCase    )
      _lowerCAmelCase		   =   checkpoint['all_modules.0.W'].data
      _lowerCAmelCase		   =   checkpoint['all_modules.0.W'].data
      _lowerCAmelCase		   =   checkpoint['all_modules.1.weight'].data
      _lowerCAmelCase		   =   checkpoint['all_modules.1.bias'].data

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

      _lowerCAmelCase		   =   checkpoint['all_modules.3.weight'].data
      _lowerCAmelCase		   =   checkpoint['all_modules.3.bias'].data

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

      _lowerCAmelCase		   =   4

      def set_attention_weights(_lowerCamelCase			:							int  ,  _lowerCamelCase			:							str  ,  _lowerCamelCase			:							Optional[Any]    ):
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.NIN_0.W"].data.T
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.NIN_1.W"].data.T
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.NIN_2.W"].data.T

            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.NIN_0.b"].data
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.NIN_1.b"].data
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.NIN_2.b"].data

            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.NIN_3.W"].data.T
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.NIN_3.b"].data

            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

      def set_resnet_weights(_lowerCamelCase			:							List[str]  ,  _lowerCamelCase			:							List[Any]  ,  _lowerCamelCase			:							List[str]    ):
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.Conv_0.weight"].data
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.Conv_0.bias"].data
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.Conv_1.weight"].data
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.Conv_1.bias"].data
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.GroupNorm_1.weight"].data
            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.GroupNorm_1.bias"].data

            _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.Dense_0.weight"].data
            _lowerCAmelCase		   =   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		   =   old_checkpoint[F"all_modules.{index}.Conv_2.weight"].data
                  _lowerCAmelCase		   =   old_checkpoint[F"all_modules.{index}.Conv_2.bias"].data

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

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

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

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

            if hasattr(_lowerCamelCase  ,  'resnet_up'    ) and block.resnet_up is not None:
                  _lowerCAmelCase		   =   checkpoint[F"all_modules.{module_index}.weight"].data
                  _lowerCAmelCase		   =   checkpoint[F"all_modules.{module_index}.bias"].data
                  module_index += 1
                  _lowerCAmelCase		   =   checkpoint[F"all_modules.{module_index}.weight"].data
                  _lowerCAmelCase		   =   checkpoint[F"all_modules.{module_index}.bias"].data
                  module_index += 1
                  set_resnet_weights(block.resnet_up  ,  _lowerCamelCase  ,  _lowerCamelCase    )
                  module_index += 1

      _lowerCAmelCase		   =   checkpoint[F"all_modules.{module_index}.weight"].data
      _lowerCAmelCase		   =   checkpoint[F"all_modules.{module_index}.bias"].data
      module_index += 1
      _lowerCAmelCase		   =   checkpoint[F"all_modules.{module_index}.weight"].data
      _lowerCAmelCase		   =   checkpoint[F"all_modules.{module_index}.bias"].data

      return new_model_architecture.state_dict()


if __name__ == "__main__":
 snake_case  =			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.''',
 )

 snake_case  =			parser.parse_args()

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

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

 snake_case  =			convert_ncsnpp_checkpoint(
     checkpoint,
     config,
 )

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

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

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

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






'''simple docstring'''




from __future__ import annotations







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							dict[str, str]    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', '', ''
      _lowerCAmelCase		   =   []

      for character in message_part:
            tmp.append(character_to_number[character]    )

      for each in tmp:
            one += each[0]
            two += each[1]
            three += each[2]

      return one + two + three







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							dict[str, str]    ):
      _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', ''
      _lowerCAmelCase		   =   []

      for character in message_part:
            this_part += character_to_number[character]

      for digit in this_part:
            tmp += digit
            if len(_lowerCamelCase    ) == len(_lowerCamelCase    ):
                  result.append(_lowerCamelCase    )
                  _lowerCAmelCase		   =   ''

      return result[0], result[1], result[2]







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str    ):
      # Validate message and alphabet, set to upper and remove spaces
      _lowerCAmelCase		   =   alphabet.replace(' '  ,  ''    ).upper()
      _lowerCAmelCase		   =   message.replace(' '  ,  ''    ).upper()

      # Check length and characters
      if len(_lowerCamelCase    ) != 27:
            raise KeyError('Length of alphabet has to be 27.'    )
      for each in message:
            if each not in alphabet:
                  raise ValueError('Each message character has to be included in alphabet!'    )

    # Generate dictionares
      _lowerCAmelCase		   =   (
          '111',
          '112',
          '113',
          '121',
          '122',
          '123',
          '131',
          '132',
          '133',
          '211',
          '212',
          '213',
          '221',
          '222',
          '223',
          '231',
          '232',
          '233',
          '311',
          '312',
          '313',
          '321',
          '322',
          '323',
          '331',
          '332',
          '333',
      )
      _lowerCAmelCase		   =   {}
      _lowerCAmelCase		   =   {}
      for letter, number in zip(_lowerCamelCase  ,  _lowerCamelCase    ):
            _lowerCAmelCase		   =   number
            _lowerCAmelCase		   =   letter

      return message, alphabet, character_to_number, number_to_character







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."  ,  _lowerCamelCase			:							int = 5    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   __prepare(
          _lowerCamelCase  ,  _lowerCamelCase    )
      _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', ''

      for i in range(0  ,  len(_lowerCamelCase    ) + 1  ,  _lowerCamelCase    ):
            encrypted_numeric += __encrypt_part(
                message[i : i + period]  ,  _lowerCamelCase    )

      for i in range(0  ,  len(_lowerCamelCase    )  ,  3    ):
            encrypted += number_to_character[encrypted_numeric[i : i + 3]]

      return encrypted







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."  ,  _lowerCamelCase			:							int = 5    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   __prepare(
          _lowerCamelCase  ,  _lowerCamelCase    )
      _lowerCAmelCase		   =   []
      _lowerCAmelCase		   =   ''

      for i in range(0  ,  len(_lowerCamelCase    ) + 1  ,  _lowerCamelCase    ):
            _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   __decrypt_part(message[i : i + period]  ,  _lowerCamelCase    )

            for j in range(0  ,  len(_lowerCamelCase    )    ):
                  decrypted_numeric.append(a[j] + b[j] + c[j]    )

      for each in decrypted_numeric:
            decrypted += number_to_character[each]

      return decrypted


if __name__ == "__main__":
 snake_case  =			'''DEFEND THE EAST WALL OF THE CASTLE.'''
 snake_case  =			encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
 snake_case  =			decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
 print(f'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')





