code: 
  
  import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				str			,   _A   :				Any			,   _A   :				Dict=None			,   _A   :				str=None			,   _A   :				int=None							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    assert isinstance(_A			,   _A							), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
    # Splits the attention layers into three variables.
    if attention_paths_to_split is not None:
      for path, path_map in attention_paths_to_split.items():
        a__						=old_checkpoint[path]
        a__						=old_tensor.shape[0] // 3
  
        a__						=(-1, channels) if len(old_tensor.shape							) == 3 else (-1)
  
        a__						=old_tensor.shape[0] // config['''num_head_channels'''] // 3
  
        a__						=old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
        a__,   a__,   a__						=old_tensor.split(channels // num_heads			,   dim=1							)
  
        a__						=query.reshape(_A							)
        a__						=key.reshape(_A							)
        a__						=value.reshape(_A							)
  
    for path in paths:
      a__						=path['''new''']
  
      # These have already been assigned
      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
        continue
  
      if additional_replacements is not None:
        for replacement in additional_replacements:
          a__						=new_path.replace(replacement['''old''']			,   replacement['''new''']							)
  
          # proj_attn.weight has to be converted from conv 1D to linear
      a__						=old_checkpoint[path['''old''']]
      a__						=['''proj_attn.weight''']
      a__						=['''proj_out.weight''', '''proj_in.weight''']
      if any(k in new_path for k in names							):
        a__						=weight[:, :, 0]
      elif any(k in new_path for k in names_a							) and len(weight.shape							) > 2 and ".attentions." not in new_path:
        a__						=weight[:, :, 0]
      else:
        a__						=weight
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				Optional[int]=0							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[]
    for old_item in old_list:
      a__						=old_item
  
      #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
      #         new_item = new_item.replace('norm.bias', 'group_norm.bias')
  
      #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
      #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
  
      #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
  
      mapping.append({'''old''': old_item, '''new''': new_item}							)
  
    return mapping
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				List[str]=1							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    if n_shave_prefix_segments >= 0:
      return ".".join(path.split('''.'''							)[n_shave_prefix_segments:]							)
    else:
      return ".".join(path.split('''.'''							)[:n_shave_prefix_segments]							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Union[str, Any]			,   _A   :				Optional[int]=0							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[]
    for old_item in old_list:
      mapping.append({'''old''': old_item, '''new''': old_item}							)
  
    return mapping
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[int]			,   _A   :				Union[str, Any]=0							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[]
    for old_item in old_list:
      a__						=old_item.replace('''in_layers.0'''			,   '''norm1'''							)
      a__						=new_item.replace('''in_layers.2'''			,   '''conv1'''							)
  
      a__						=new_item.replace('''out_layers.0'''			,   '''norm2'''							)
      a__						=new_item.replace('''out_layers.3'''			,   '''conv2'''							)
  
      a__						=new_item.replace('''emb_layers.1'''			,   '''time_emb_proj'''							)
      a__						=new_item.replace('''skip_connection'''			,   '''conv_shortcut'''							)
  
      a__						=shave_segments(_A			,   n_shave_prefix_segments=_A							)
  
      if "temopral_conv" not in old_item:
        mapping.append({'''old''': old_item, '''new''': new_item}							)
  
    return mapping
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				str			,   _A   :				int			,   _A   :				Tuple=None			,   _A   :				str=False							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						={}
    a__						=list(checkpoint.keys()							)
  
    a__						='''model.diffusion_model.'''
  
    # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
    if sum(k.startswith('''model_ema'''							) for k in keys							) > 1_00 and extract_ema:
      print(F"""Checkpoint {path} has both EMA and non-EMA weights."""							)
      print(
          '''In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'''
          ''' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'''							)
      for key in keys:
        if key.startswith('''model.diffusion_model'''							):
          a__						='''model_ema.''' + ''''''.join(key.split('''.'''							)[1:]							)
          a__						=checkpoint.pop(_A							)
    else:
      if sum(k.startswith('''model_ema'''							) for k in keys							) > 1_00:
        print(
            '''In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'''
            ''' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'''							)
  
      for key in keys:
        a__						=checkpoint.pop(_A							)
  
    a__						={}
  
    a__						=unet_state_dict['''time_embed.0.weight''']
    a__						=unet_state_dict['''time_embed.0.bias''']
    a__						=unet_state_dict['''time_embed.2.weight''']
    a__						=unet_state_dict['''time_embed.2.bias''']
  
    if config["class_embed_type"] is None:
      # No parameters to port
      ...
    elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
      a__						=unet_state_dict['''label_emb.0.0.weight''']
      a__						=unet_state_dict['''label_emb.0.0.bias''']
      a__						=unet_state_dict['''label_emb.0.2.weight''']
      a__						=unet_state_dict['''label_emb.0.2.bias''']
    else:
      raise NotImplementedError(F"""Not implemented `class_embed_type`: {config["class_embed_type"]}"""							)
  
    a__						=unet_state_dict['''input_blocks.0.0.weight''']
    a__						=unet_state_dict['''input_blocks.0.0.bias''']
  
    a__						=[v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''							)]
    a__						=renew_attention_paths(_A							)
    a__						={'''old''': '''input_blocks.0.1''', '''new''': '''transformer_in'''}
    assign_to_checkpoint(_A			,   _A			,   _A			,   additional_replacements=[meta_path]			,   config=_A							)
  
    a__						=unet_state_dict['''out.0.weight''']
    a__						=unet_state_dict['''out.0.bias''']
    a__						=unet_state_dict['''out.2.weight''']
    a__						=unet_state_dict['''out.2.bias''']
  
    # Retrieves the keys for the input blocks only
    a__						=len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''input_blocks''' in layer}							)
    a__						={
        layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
        for layer_id in range(_A							)
    }
  
    # Retrieves the keys for the middle blocks only
    a__						=len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''middle_block''' in layer}							)
    a__						={
        layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
        for layer_id in range(_A							)
    }
  
    # Retrieves the keys for the output blocks only
    a__						=len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''output_blocks''' in layer}							)
    a__						={
        layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
        for layer_id in range(_A							)
    }
  
    for i in range(1			,   _A							):
      a__						=(i - 1) // (config['''layers_per_block'''] + 1)
      a__						=(i - 1) % (config['''layers_per_block'''] + 1)
  
      a__						=[
          key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
      ]
      a__						=[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
      a__						=[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]
  
      if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
        a__						=unet_state_dict.pop(
            F"""input_blocks.{i}.op.weight"""							)
        a__						=unet_state_dict.pop(
            F"""input_blocks.{i}.op.bias"""							)
  
      a__						=renew_resnet_paths(_A							)
      a__						={'''old''': F"""input_blocks.{i}.0""", '''new''': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      assign_to_checkpoint(
          _A			,   _A			,   _A			,   additional_replacements=[meta_path]			,   config=_A							)
  
      a__						=[key for key in resnets if '''temopral_conv''' in key]
      a__						=renew_temp_conv_paths(_A							)
      a__						={
          '''old''': F"""input_blocks.{i}.0.temopral_conv""",
          '''new''': F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
      }
      assign_to_checkpoint(
          _A			,   _A			,   _A			,   additional_replacements=[meta_path]			,   config=_A							)
  
      if len(_A							):
        a__						=renew_attention_paths(_A							)
        a__						={'''old''': F"""input_blocks.{i}.1""", '''new''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
        assign_to_checkpoint(
            _A			,   _A			,   _A			,   additional_replacements=[meta_path]			,   config=_A							)
  
      if len(_A							):
        a__						=renew_attention_paths(_A							)
        a__						={
            '''old''': F"""input_blocks.{i}.2""",
            '''new''': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
        }
        assign_to_checkpoint(
            _A			,   _A			,   _A			,   additional_replacements=[meta_path]			,   config=_A							)
  
    a__						=middle_blocks[0]
    a__						=[key for key in resnet_a if '''temopral_conv''' in key]
    a__						=middle_blocks[1]
    a__						=middle_blocks[2]
    a__						=middle_blocks[3]
    a__						=[key for key in resnet_a if '''temopral_conv''' in key]
  
    a__						=renew_resnet_paths(_A							)
    a__						={'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
    assign_to_checkpoint(
        _A			,   _A			,   _A			,   config=_A			,   additional_replacements=[meta_path]							)
  
    a__						=renew_temp_conv_paths(_A							)
    a__						={'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
    assign_to_checkpoint(
        _A			,   _A			,   _A			,   config=_A			,   additional_replacements=[meta_path]							)
  
    a__						=renew_resnet_paths(_A							)
    a__						={'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
    assign_to_checkpoint(
        _A			,   _A			,   _A			,   config=_A			,   additional_replacements=[meta_path]							)
  
    a__						=renew_temp_conv_paths(_A							)
    a__						={'''old''': '''middle_block.3.temopral_conv''', '''new''': '''mid_block.temp_convs.1'''}
    assign_to_checkpoint(
        _A			,   _A			,   _A			,   config=_A			,   additional_replacements=[meta_path]							)
  
    a__						=renew_attention_paths(_A							)
    a__						={'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
    assign_to_checkpoint(
        _A			,   _A			,   _A			,   additional_replacements=[meta_path]			,   config=_A							)
  
    a__						=renew_attention_paths(_A							)
    a__						={'''old''': '''middle_block.2''', '''new''': '''mid_block.temp_attentions.0'''}
    assign_to_checkpoint(
        _A			,   _A			,   _A			,   additional_replacements=[meta_path]			,   config=_A							)
  
    for i in range(_A							):
      a__						=i // (config['''layers_per_block'''] + 1)
      a__						=i % (config['''layers_per_block'''] + 1)
      a__						=[shave_segments(_A			,   2							) for name in output_blocks[i]]
      a__						={}
  
      for layer in output_block_layers:
        a__,   a__						=layer.split('''.'''							)[0], shave_segments(_A			,   1							)
        if layer_id in output_block_list:
          output_block_list[layer_id].append(_A							)
        else:
          a__						=[layer_name]
  
      if len(_A							) > 1:
        a__						=[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
        a__						=[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
        a__						=[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]
  
        a__						=renew_resnet_paths(_A							)
        a__						=renew_resnet_paths(_A							)
  
        a__						={'''old''': F"""output_blocks.{i}.0""", '''new''': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
        assign_to_checkpoint(
            _A			,   _A			,   _A			,   additional_replacements=[meta_path]			,   config=_A							)
  
        a__						=[key for key in resnets if '''temopral_conv''' in key]
        a__						=renew_temp_conv_paths(_A							)
        a__						={
            '''old''': F"""output_blocks.{i}.0.temopral_conv""",
            '''new''': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
        }
        assign_to_checkpoint(
            _A			,   _A			,   _A			,   additional_replacements=[meta_path]			,   config=_A							)
  
        a__						={k: sorted(_A							) for k, v in output_block_list.items()}
        if ["conv.bias", "conv.weight"] in output_block_list.values():
          a__						=list(output_block_list.values()							).index(['''conv.bias''', '''conv.weight''']							)
          a__						=unet_state_dict[
              F"""output_blocks.{i}.{index}.conv.weight"""
          ]
          a__						=unet_state_dict[
              F"""output_blocks.{i}.{index}.conv.bias"""
          ]
  
          # Clear attentions as they have been attributed above.
          if len(_A							) == 2:
            a__						=[]
  
        if len(_A							):
          a__						=renew_attention_paths(_A							)
          a__						={
              '''old''': F"""output_blocks.{i}.1""",
              '''new''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
          }
          assign_to_checkpoint(
              _A			,   _A			,   _A			,   additional_replacements=[meta_path]			,   config=_A							)
  
        if len(_A							):
          a__						=renew_attention_paths(_A							)
          a__						={
              '''old''': F"""output_blocks.{i}.2""",
              '''new''': F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
          }
          assign_to_checkpoint(
              _A			,   _A			,   _A			,   additional_replacements=[meta_path]			,   config=_A							)
      else:
        a__						=renew_resnet_paths(_A			,   n_shave_prefix_segments=1							)
        for path in resnet_0_paths:
          a__						='''.'''.join(['''output_blocks''', str(_A							), path['''old''']]							)
          a__						='''.'''.join(['''up_blocks''', str(_A							), '''resnets''', str(_A							), path['''new''']]							)
          a__						=unet_state_dict[old_path]
  
        a__						=[l for l in output_block_layers if '''temopral_conv''' in l]
        for path in temopral_conv_paths:
          a__						=path.split('''temopral_conv.'''							)[-1]
          a__						='''.'''.join(['''output_blocks''', str(_A							), str(_A							), '''temopral_conv''', pruned_path]							)
          a__						='''.'''.join(['''up_blocks''', str(_A							), '''temp_convs''', str(_A							), pruned_path]							)
          a__						=unet_state_dict[old_path]
  
    return new_checkpoint
  
  
  if __name__ == "__main__":
         lowerCamelCase     =      argparse.ArgumentParser()
  
         parser.add_argument(
             '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
         )
         parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
         lowerCamelCase     =      parser.parse_args()
  
         lowerCamelCase     =      torch.load(args.checkpoint_path, map_location='''cpu''')
         lowerCamelCase     =      UNetaDConditionModel()
  
         lowerCamelCase     =      convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
         lowerCamelCase     =      set(unet.state_dict().keys()) - set(converted_ckpt.keys())
         lowerCamelCase     =      set(converted_ckpt.keys()) - set(unet.state_dict().keys())
  
         assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"
  
         # load state_dict
         unet.load_state_dict(converted_ckpt)
  
         unet.save_pretrained(args.dump_path)
  
         # -- finish converting the unet --
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  lowerCamelCase     =      {
      '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
      '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
  }
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      ['''DebertaV2TokenizerFast''']
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''TFDebertaV2ForMaskedLM''',
             '''TFDebertaV2ForQuestionAnswering''',
             '''TFDebertaV2ForSequenceClassification''',
             '''TFDebertaV2ForTokenClassification''',
             '''TFDebertaV2Model''',
             '''TFDebertaV2PreTrainedModel''',
         ]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DebertaV2ForMaskedLM''',
             '''DebertaV2ForMultipleChoice''',
             '''DebertaV2ForQuestionAnswering''',
             '''DebertaV2ForSequenceClassification''',
             '''DebertaV2ForTokenClassification''',
             '''DebertaV2Model''',
             '''DebertaV2PreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_deberta_va import (
             DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             DebertaVaConfig,
             DebertaVaOnnxConfig,
         )
         from .tokenization_deberta_va import DebertaVaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .tokenization_deberta_va_fast import DebertaVaTokenizerFast
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_deberta_va import (
                    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    TFDebertaVaForMaskedLM,
                    TFDebertaVaForQuestionAnswering,
                    TFDebertaVaForSequenceClassification,
                    TFDebertaVaForTokenClassification,
                    TFDebertaVaModel,
                    TFDebertaVaPreTrainedModel,
                )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_deberta_va import (
                    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DebertaVaForMaskedLM,
                    DebertaVaForMultipleChoice,
                    DebertaVaForQuestionAnswering,
                    DebertaVaForSequenceClassification,
                    DebertaVaForTokenClassification,
                    DebertaVaModel,
                    DebertaVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase     =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  