




import argparse

import torch

from diffusers import UNetaDConditionModel


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase=None					,     UpperCamelCase=None					,     UpperCamelCase=None							)			->		Any:





  """simple docstring"""





  assert isinstance(UpperCamelCase					,     UpperCamelCase							), "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():
      _lowercase: Dict						     =    old_checkpoint[path]
      _lowercase: Optional[Any]						     =    old_tensor.shape[0] // 3

      _lowercase: Optional[int]						     =    (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

      _lowercase: Dict						     =    old_tensor.shape[0] // config['num_head_channels'] // 3

      _lowercase: Dict						     =    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
      _lowercase: Optional[Any]						     =    old_tensor.split(channels // num_heads					,     dim=1							)

      _lowercase: str						     =    query.reshape(UpperCamelCase							)
      _lowercase: Tuple						     =    key.reshape(UpperCamelCase							)
      _lowercase: Optional[int]						     =    value.reshape(UpperCamelCase							)

  for path in paths:
    _lowercase: Optional[Any]						     =    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:
        _lowercase: Optional[Any]						     =    new_path.replace(replacement['old']					,     replacement['new']							)

        # proj_attn.weight has to be converted from conv 1D to linear
    _lowercase: int						     =    old_checkpoint[path['old']]
    _lowercase: Any						     =    ['proj_attn.weight']
    _lowercase: Any						     =    ['proj_out.weight', 'proj_in.weight']
    if any(k in new_path for k in names							):
      _lowercase: Dict						     =    weight[:, :, 0]
    elif any(k in new_path for k in names_a							) and len(weight.shape							) > 2 and ".attentions." not in new_path:
      _lowercase: Tuple						     =    weight[:, :, 0]
    else:
      _lowercase: Union[str, Any]						     =    weight


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase=0							)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: str						     =    []
  for old_item in old_list:
    _lowercase: Union[str, Any]						     =    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 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase=1							)			->		Dict:





  """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 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase=0							)			->		Any:





  """simple docstring"""





  _lowercase: str						     =    []
  for old_item in old_list:
    mapping.append({'old': old_item, 'new': old_item}							)

  return mapping


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase=0							)			->		List[str]:





  """simple docstring"""





  _lowercase: List[str]						     =    []
  for old_item in old_list:
    _lowercase: Any						     =    old_item.replace('in_layers.0'					,     'norm1'							)
    _lowercase: Union[str, Any]						     =    new_item.replace('in_layers.2'					,     'conv1'							)

    _lowercase: Union[str, Any]						     =    new_item.replace('out_layers.0'					,     'norm2'							)
    _lowercase: Optional[int]						     =    new_item.replace('out_layers.3'					,     'conv2'							)

    _lowercase: Optional[int]						     =    new_item.replace('emb_layers.1'					,     'time_emb_proj'							)
    _lowercase: int						     =    new_item.replace('skip_connection'					,     'conv_shortcut'							)

    _lowercase: List[str]						     =    shave_segments(UpperCamelCase					,     n_shave_prefix_segments=UpperCamelCase							)

    if "temopral_conv" not in old_item:
      mapping.append({'old': old_item, 'new': new_item}							)

  return mapping


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase=None					,     UpperCamelCase=False							)			->		List[str]:





  """simple docstring"""





  _lowercase: str						     =    {}
  _lowercase: Optional[Any]						     =    list(checkpoint.keys()							)

  _lowercase: Any						     =    '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							) > 100 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'							):
        _lowercase: Any						     =    'model_ema.' + ''.join(key.split('.'							)[1:]							)
        _lowercase: Any						     =    checkpoint.pop(UpperCamelCase							)
  else:
    if sum(k.startswith('model_ema'							) for k in keys							) > 100:
      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:
      _lowercase: str						     =    checkpoint.pop(UpperCamelCase							)

  _lowercase: str						     =    {}

  _lowercase: Optional[int]						     =    unet_state_dict['time_embed.0.weight']
  _lowercase: str						     =    unet_state_dict['time_embed.0.bias']
  _lowercase: Dict						     =    unet_state_dict['time_embed.2.weight']
  _lowercase: Any						     =    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":
    _lowercase: List[Any]						     =    unet_state_dict['label_emb.0.0.weight']
    _lowercase: Union[str, Any]						     =    unet_state_dict['label_emb.0.0.bias']
    _lowercase: Union[str, Any]						     =    unet_state_dict['label_emb.0.2.weight']
    _lowercase: Optional[Any]						     =    unet_state_dict['label_emb.0.2.bias']
  else:
    raise NotImplementedError(F'Not implemented `class_embed_type`: {config["class_embed_type"]}'							)

  _lowercase: Optional[int]						     =    unet_state_dict['input_blocks.0.0.weight']
  _lowercase: str						     =    unet_state_dict['input_blocks.0.0.bias']

  _lowercase: Tuple						     =    [v for v in unet_state_dict if v.startswith('input_blocks.0.1'							)]
  _lowercase: int						     =    renew_attention_paths(UpperCamelCase							)
  _lowercase: Union[str, Any]						     =    {'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  assign_to_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[meta_path]					,     config=UpperCamelCase							)

  _lowercase: Any						     =    unet_state_dict['out.0.weight']
  _lowercase: Optional[int]						     =    unet_state_dict['out.0.bias']
  _lowercase: Dict						     =    unet_state_dict['out.2.weight']
  _lowercase: List[Any]						     =    unet_state_dict['out.2.bias']

  # Retrieves the keys for the input blocks only
  _lowercase: Optional[Any]						     =    len({'.'.join(layer.split('.'							)[:2]							) for layer in unet_state_dict if 'input_blocks' in layer}							)
  _lowercase: Union[str, Any]						     =    {
      layer_id: [key for key in unet_state_dict if F'input_blocks.{layer_id}' in key]
      for layer_id in range(UpperCamelCase							)
  }

  # Retrieves the keys for the middle blocks only
  _lowercase: int						     =    len({'.'.join(layer.split('.'							)[:2]							) for layer in unet_state_dict if 'middle_block' in layer}							)
  _lowercase: List[str]						     =    {
      layer_id: [key for key in unet_state_dict if F'middle_block.{layer_id}' in key]
      for layer_id in range(UpperCamelCase							)
  }

  # Retrieves the keys for the output blocks only
  _lowercase: Optional[int]						     =    len({'.'.join(layer.split('.'							)[:2]							) for layer in unet_state_dict if 'output_blocks' in layer}							)
  _lowercase: Dict						     =    {
      layer_id: [key for key in unet_state_dict if F'output_blocks.{layer_id}' in key]
      for layer_id in range(UpperCamelCase							)
  }

  for i in range(1					,     UpperCamelCase							):
    _lowercase: List[str]						     =    (i - 1) // (config['layers_per_block'] + 1)
    _lowercase: Dict						     =    (i - 1) % (config['layers_per_block'] + 1)

    _lowercase: Union[str, Any]						     =    [
        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
    ]
    _lowercase: Union[str, Any]						     =    [key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]
    _lowercase: int						     =    [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:
      _lowercase: int						     =    unet_state_dict.pop(
          F'input_blocks.{i}.op.weight'							)
      _lowercase: Any						     =    unet_state_dict.pop(
          F'input_blocks.{i}.op.bias'							)

    _lowercase: List[Any]						     =    renew_resnet_paths(UpperCamelCase							)
    _lowercase: int						     =    {'old': F'input_blocks.{i}.0', 'new': F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
    assign_to_checkpoint(
        UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[meta_path]					,     config=UpperCamelCase							)

    _lowercase: List[Any]						     =    [key for key in resnets if 'temopral_conv' in key]
    _lowercase: List[Any]						     =    renew_temp_conv_paths(UpperCamelCase							)
    _lowercase: Union[str, Any]						     =    {
        'old': F'input_blocks.{i}.0.temopral_conv',
        'new': F'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
    }
    assign_to_checkpoint(
        UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[meta_path]					,     config=UpperCamelCase							)

    if len(UpperCamelCase							):
      _lowercase: Tuple						     =    renew_attention_paths(UpperCamelCase							)
      _lowercase: List[str]						     =    {'old': F'input_blocks.{i}.1', 'new': F'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
      assign_to_checkpoint(
          UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[meta_path]					,     config=UpperCamelCase							)

    if len(UpperCamelCase							):
      _lowercase: List[str]						     =    renew_attention_paths(UpperCamelCase							)
      _lowercase: Union[str, Any]						     =    {
          'old': F'input_blocks.{i}.2',
          'new': F'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
      }
      assign_to_checkpoint(
          UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[meta_path]					,     config=UpperCamelCase							)

  _lowercase: Any						     =    middle_blocks[0]
  _lowercase: Tuple						     =    [key for key in resnet_a if 'temopral_conv' in key]
  _lowercase: str						     =    middle_blocks[1]
  _lowercase: Union[str, Any]						     =    middle_blocks[2]
  _lowercase: int						     =    middle_blocks[3]
  _lowercase: Optional[Any]						     =    [key for key in resnet_a if 'temopral_conv' in key]

  _lowercase: Optional[int]						     =    renew_resnet_paths(UpperCamelCase							)
  _lowercase: Dict						     =    {'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     config=UpperCamelCase					,     additional_replacements=[meta_path]							)

  _lowercase: Optional[int]						     =    renew_temp_conv_paths(UpperCamelCase							)
  _lowercase: Optional[Any]						     =    {'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     config=UpperCamelCase					,     additional_replacements=[meta_path]							)

  _lowercase: int						     =    renew_resnet_paths(UpperCamelCase							)
  _lowercase: Dict						     =    {'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     config=UpperCamelCase					,     additional_replacements=[meta_path]							)

  _lowercase: Dict						     =    renew_temp_conv_paths(UpperCamelCase							)
  _lowercase: Dict						     =    {'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     config=UpperCamelCase					,     additional_replacements=[meta_path]							)

  _lowercase: int						     =    renew_attention_paths(UpperCamelCase							)
  _lowercase: Dict						     =    {'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[meta_path]					,     config=UpperCamelCase							)

  _lowercase: Dict						     =    renew_attention_paths(UpperCamelCase							)
  _lowercase: Tuple						     =    {'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[meta_path]					,     config=UpperCamelCase							)

  for i in range(UpperCamelCase							):
    _lowercase: int						     =    i // (config['layers_per_block'] + 1)
    _lowercase: Dict						     =    i % (config['layers_per_block'] + 1)
    _lowercase: List[str]						     =    [shave_segments(UpperCamelCase					,     2							) for name in output_blocks[i]]
    _lowercase: Optional[int]						     =    {}

    for layer in output_block_layers:
      _lowercase: Union[str, Any]						     =    layer.split('.'							)[0], shave_segments(UpperCamelCase					,     1							)
      if layer_id in output_block_list:
        output_block_list[layer_id].append(UpperCamelCase							)
      else:
        _lowercase: Tuple						     =    [layer_name]

    if len(UpperCamelCase							) > 1:
      _lowercase: List[Any]						     =    [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
      _lowercase: Union[str, Any]						     =    [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]
      _lowercase: List[Any]						     =    [key for key in output_blocks[i] if F'output_blocks.{i}.2' in key]

      _lowercase: Optional[int]						     =    renew_resnet_paths(UpperCamelCase							)
      _lowercase: int						     =    renew_resnet_paths(UpperCamelCase							)

      _lowercase: List[Any]						     =    {'old': F'output_blocks.{i}.0', 'new': F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
      assign_to_checkpoint(
          UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[meta_path]					,     config=UpperCamelCase							)

      _lowercase: int						     =    [key for key in resnets if 'temopral_conv' in key]
      _lowercase: str						     =    renew_temp_conv_paths(UpperCamelCase							)
      _lowercase: Optional[int]						     =    {
          'old': F'output_blocks.{i}.0.temopral_conv',
          'new': F'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
      }
      assign_to_checkpoint(
          UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[meta_path]					,     config=UpperCamelCase							)

      _lowercase: str						     =    {k: sorted(UpperCamelCase							) for k, v in output_block_list.items()}
      if ["conv.bias", "conv.weight"] in output_block_list.values():
        _lowercase: Any						     =    list(output_block_list.values()							).index(['conv.bias', 'conv.weight']							)
        _lowercase: List[Any]						     =    unet_state_dict[
            F'output_blocks.{i}.{index}.conv.weight'
        ]
        _lowercase: Tuple						     =    unet_state_dict[
            F'output_blocks.{i}.{index}.conv.bias'
        ]

        # Clear attentions as they have been attributed above.
        if len(UpperCamelCase							) == 2:
          _lowercase: Optional[Any]						     =    []

      if len(UpperCamelCase							):
        _lowercase: Optional[int]						     =    renew_attention_paths(UpperCamelCase							)
        _lowercase: List[Any]						     =    {
            'old': F'output_blocks.{i}.1',
            'new': F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
        }
        assign_to_checkpoint(
            UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[meta_path]					,     config=UpperCamelCase							)

      if len(UpperCamelCase							):
        _lowercase: List[str]						     =    renew_attention_paths(UpperCamelCase							)
        _lowercase: Dict						     =    {
            'old': F'output_blocks.{i}.2',
            'new': F'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
        }
        assign_to_checkpoint(
            UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[meta_path]					,     config=UpperCamelCase							)
    else:
      _lowercase: Dict						     =    renew_resnet_paths(UpperCamelCase					,     n_shave_prefix_segments=1							)
      for path in resnet_0_paths:
        _lowercase: Optional[int]						     =    '.'.join(['output_blocks', str(UpperCamelCase							), path['old']]							)
        _lowercase: Optional[Any]						     =    '.'.join(['up_blocks', str(UpperCamelCase							), 'resnets', str(UpperCamelCase							), path['new']]							)
        _lowercase: List[Any]						     =    unet_state_dict[old_path]

      _lowercase: List[str]						     =    [l for l in output_block_layers if 'temopral_conv' in l]
      for path in temopral_conv_paths:
        _lowercase: Optional[int]						     =    path.split('temopral_conv.'							)[-1]
        _lowercase: Any						     =    '.'.join(['output_blocks', str(UpperCamelCase							), str(UpperCamelCase							), 'temopral_conv', pruned_path]							)
        _lowercase: Any						     =    '.'.join(['up_blocks', str(UpperCamelCase							), 'temp_convs', str(UpperCamelCase							), pruned_path]							)
        _lowercase: List[Any]						     =    unet_state_dict[old_path]

  return new_checkpoint


if __name__ == "__main__":
   UpperCAmelCase__						:       str							=    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.')
   UpperCAmelCase__						:       Tuple							=    parser.parse_args()

   UpperCAmelCase__						:       str							=    torch.load(args.checkpoint_path, map_location='cpu')
   UpperCAmelCase__						:       str							=    UNetaDConditionModel()

   UpperCAmelCase__						:       Any							=    convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

   UpperCAmelCase__						:       int							=    set(unet.state_dict().keys()) - set(converted_ckpt.keys())
   UpperCAmelCase__						:       Dict							=    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 --






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"AutoFeatureExtractor"
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"Speech2Text2Tokenizer"






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       List[Any]     )->			Dict:
     super().__init__(UpperCamelCase_						,					UpperCamelCase_     )
     _lowercase: Optional[int]						     =    self.feature_extractor
     _lowercase: int						     =    False






   def __call__(	self   :       List[Any]						,					*UpperCamelCase_   :       Dict						,					**UpperCamelCase_   :       Optional[int]     )->			str:
     # For backward compatibility
     if self._in_target_context_manager:
       return self.current_processor(*UpperCamelCase_						,					**UpperCamelCase_     )

     if "raw_speech" in kwargs:
       warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'     )
       _lowercase: Tuple						     =    kwargs.pop('raw_speech'     )
     else:
       _lowercase: Optional[Any]						     =    kwargs.pop('audio'						,					UpperCamelCase_     )
     _lowercase: int						     =    kwargs.pop('sampling_rate'						,					UpperCamelCase_     )
     _lowercase: Dict						     =    kwargs.pop('text'						,					UpperCamelCase_     )
     if len(UpperCamelCase_     ) > 0:
       _lowercase: List[Any]						     =    args[0]
       _lowercase: List[Any]						     =    args[1:]

     if audio is None and text is None:
       raise ValueError('You need to specify either an `audio` or `text` input to process.'     )

     if audio is not None:
       _lowercase: Optional[int]						     =    self.feature_extractor(UpperCamelCase_						,					*UpperCamelCase_						,					sampling_rate=UpperCamelCase_						,					**UpperCamelCase_     )
     if text is not None:
       _lowercase: str						     =    self.tokenizer(UpperCamelCase_						,					**UpperCamelCase_     )

     if text is None:
       return inputs
     elif audio is None:
       return encodings
     else:
       _lowercase: int						     =    encodings['input_ids']
       return inputs






   def  _a						(	self   :       Optional[int]						,					*UpperCamelCase_   :       Tuple						,					**UpperCamelCase_   :       Dict     )->			List[str]:
     return self.tokenizer.batch_decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   def  _a						(	self   :       Tuple						,					*UpperCamelCase_   :       List[Any]						,					**UpperCamelCase_   :       Union[str, Any]     )->			Optional[int]:
     return self.tokenizer.decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   @contextmanager
   def  _a						(	self   :       Tuple     )->			Union[str, Any]:
     warnings.warn(
         '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
         'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
         'your audio inputs, or in a separate call.'     )
     _lowercase: Any						     =    True
     _lowercase: Optional[Any]						     =    self.tokenizer
     yield
     _lowercase: Union[str, Any]						     =    self.feature_extractor
     _lowercase: str						     =    False
