code: 
  
  
  import functools
  import operator
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  lowerCAmelCase__   	=				{
      'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
      # See all Hubert models at https://huggingface.co/models?filter=hubert
  }
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				"hubert"
  
  
       def __init__(		self				,		__lowerCamelCase=3_2				,		__lowerCamelCase=7_6_8				,		__lowerCamelCase=1_2				,		__lowerCamelCase=1_2				,		__lowerCamelCase=3_0_7_2				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=True				,		__lowerCamelCase=0.0				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.0_2				,		__lowerCamelCase=1e-5				,		__lowerCamelCase="group"				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)				,		__lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)				,		__lowerCamelCase=(1_0, 3, 3, 3, 3, 2, 2)				,		__lowerCamelCase=False				,		__lowerCamelCase=1_2_8				,		__lowerCamelCase=1_6				,		__lowerCamelCase=False				,		__lowerCamelCase=True				,		__lowerCamelCase=0.0_5				,		__lowerCamelCase=1_0				,		__lowerCamelCase=2				,		__lowerCamelCase=0.0				,		__lowerCamelCase=1_0				,		__lowerCamelCase=0				,		__lowerCamelCase="sum"				,		__lowerCamelCase=False				,		__lowerCamelCase=False				,		__lowerCamelCase=2_5_6				,		__lowerCamelCase=0				,		__lowerCamelCase=1				,		__lowerCamelCase=2				,		**__lowerCamelCase				,		)    ->       List[Any]:
              super().__init__(**__lowerCamelCase				,		pad_token_id=__lowerCamelCase				,		bos_token_id=__lowerCamelCase				,		eos_token_id=__lowerCamelCase)
              _A   : Tuple       					=				hidden_size
              _A   : Tuple       					=				feat_extract_norm
              _A   : Tuple       					=				feat_extract_activation
              _A   : Tuple       					=				list(__lowerCamelCase)
              _A   : List[str]       					=				list(__lowerCamelCase)
              _A   : List[str]       					=				list(__lowerCamelCase)
              _A   : str       					=				conv_bias
              _A   : Dict       					=				num_conv_pos_embeddings
              _A   : Dict       					=				num_conv_pos_embedding_groups
              _A   : List[str]       					=				len(self.conv_dim)
              _A   : Any       					=				num_hidden_layers
              _A   : Dict       					=				intermediate_size
              _A   : str       					=				hidden_act
              _A   : Optional[int]       					=				num_attention_heads
              _A   : Optional[Any]       					=				hidden_dropout
              _A   : int       					=				attention_dropout
              _A   : Optional[Any]       					=				activation_dropout
              _A   : int       					=				feat_proj_layer_norm
              _A   : Union[str, Any]       					=				feat_proj_dropout
              _A   : Union[str, Any]       					=				final_dropout
              _A   : str       					=				layerdrop
              _A   : Tuple       					=				layer_norm_eps
              _A   : Dict       					=				initializer_range
              _A   : Optional[int]       					=				vocab_size
              _A   : List[str]       					=				do_stable_layer_norm
              _A   : Optional[int]       					=				use_weighted_layer_sum
              _A   : Tuple       					=				classifier_proj_size
  
              if (
                  (len(self.conv_stride) != self.num_feat_extract_layers)
                  or (len(self.conv_kernel) != self.num_feat_extract_layers)
                  or (len(self.conv_dim) != self.num_feat_extract_layers)
              ):
                     raise ValueError(
                         "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
                         " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
                         F" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
                         F" `len(config.conv_kernel) = {len(self.conv_kernel)}`.")
  
              # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
              _A   : Optional[Any]       					=				apply_spec_augment
              _A   : Optional[Any]       					=				mask_time_prob
              _A   : Tuple       					=				mask_time_length
              _A   : Optional[Any]       					=				mask_time_min_masks
              _A   : List[str]       					=				mask_feature_prob
              _A   : Dict       					=				mask_feature_length
              _A   : Optional[Any]       					=				mask_feature_min_masks
  
              # ctc loss
              _A   : Optional[Any]       					=				ctc_loss_reduction
              _A   : Any       					=				ctc_zero_infinity
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       int:
              return functools.reduce(operator.mul				,		self.conv_stride				,		1)
  
style_context: 
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Dict		):
         _A   : Dict       					=				UNetaDModel(**UpperCamelCase__		)
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.1.weight"].data
         _A   : Optional[Any]       					=				checkpoint["all_modules.1.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.2.weight"].data
         _A   : List[str]       					=				checkpoint["all_modules.2.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.3.weight"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.3.bias"].data
  
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-4]].data
         _A   : Any       					=				checkpoint[list(checkpoint.keys()		)[-3]].data
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-2]].data
         _A   : Optional[int]       					=				checkpoint[list(checkpoint.keys()		)[-1]].data
  
         _A   : str       					=				4
  
         def set_attention_weights(UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple		):
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
                _A   : List[str]       					=				old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
                _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
                _A   : Dict       					=				old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
         def set_resnet_weights(UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict		):
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
                _A   : str       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
                _A   : 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:
                       _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                       _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
         for i, block in enumerate(new_model_architecture.downsample_blocks		):
                _A   : Any       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       if has_attentions:
                              set_attention_weights(block.attentions[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                              module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"downsamplers"		) and block.downsamplers is not None:
                       set_resnet_weights(block.resnet_down       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       _A   : List[str]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
                       _A   : Optional[int]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
                       module_index += 1
  
         set_resnet_weights(new_model_architecture.mid_block.resnets[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_attention_weights(new_model_architecture.mid_block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_resnet_weights(new_model_architecture.mid_block.resnets[1]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
  
         for i, block in enumerate(new_model_architecture.up_blocks		):
                _A   : Dict       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                if has_attentions:
                       set_attention_weights(
                           block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)  # why can there only be a single attention layer for up?
                       module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"resnet_up"		) and block.resnet_up is not None:
                       _A   : Dict       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       _A   : int       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       set_resnet_weights(block.resnet_up       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
  
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
         module_index += 1
         _A   : Tuple       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
  
         return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
      lowerCAmelCase__   	=				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.',
      )
  
      lowerCAmelCase__   	=				parser.parse_args()
  
      lowerCAmelCase__   	=				torch.load(args.checkpoint_path, map_location='cpu')
  
      with open(args.config_file) as f:
          lowerCAmelCase__   	=				json.loads(f.read())
  
      lowerCAmelCase__   	=				convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )
  
      if "sde" in config:
          del config["sde"]
  
      lowerCAmelCase__   	=				UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)
  
      try:
          lowerCAmelCase__   	=				ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
          lowerCAmelCase__   	=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
          model.save_pretrained(args.dump_path)
  