

import argparse

import torch

from diffusers import UNetaDConditionModel







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     Optional[int]=None       ,   SCREAMING_SNAKE_CASE						:     str=None       ,   SCREAMING_SNAKE_CASE						:     Any=None						)       ->       Tuple:

 """simple docstring"""






 assert isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						), "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():
   __lowerCAmelCase:  Dict        = old_checkpoint[path]
   __lowerCAmelCase:  Tuple        = old_tensor.shape[0] // 3

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

   __lowerCAmelCase:  int        = old_tensor.shape[0] // config['num_head_channels'] // 3

   __lowerCAmelCase:  List[Any]        = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
   __lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase:  str        = old_tensor.split(channels // num_heads       ,   dim=1						)

   __lowerCAmelCase:  Optional[int]        = query.reshape(SCREAMING_SNAKE_CASE						)
   __lowerCAmelCase:  Tuple        = key.reshape(SCREAMING_SNAKE_CASE						)
   __lowerCAmelCase:  List[str]        = value.reshape(SCREAMING_SNAKE_CASE						)

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

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







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[str]=0						)       ->       Tuple:

 """simple docstring"""






 __lowerCAmelCase:  str        = []
 for old_item in old_list:
  __lowerCAmelCase:  Tuple        = 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 _a					(			SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     int=1						)       ->       List[str]:

 """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 _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Dict=0						)       ->       str:

 """simple docstring"""






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

 return mapping







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     int=0						)       ->       Dict:

 """simple docstring"""






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

  __lowerCAmelCase:  Optional[Any]        = new_item.replace('out_layers.0'       ,   'norm2'						)
  __lowerCAmelCase:  List[str]        = new_item.replace('out_layers.3'       ,   'conv2'						)

  __lowerCAmelCase:  List[Any]        = new_item.replace('emb_layers.1'       ,   'time_emb_proj'						)
  __lowerCAmelCase:  Tuple        = new_item.replace('skip_connection'       ,   'conv_shortcut'						)

  __lowerCAmelCase:  List[str]        = shave_segments(SCREAMING_SNAKE_CASE       ,   n_shave_prefix_segments=SCREAMING_SNAKE_CASE						)

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

 return mapping







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]=None       ,   SCREAMING_SNAKE_CASE						:     Optional[int]=False						)       ->       int:

 """simple docstring"""






 __lowerCAmelCase:  str        = {}
 __lowerCAmelCase:  Dict        = list(checkpoint.keys()						)

 __lowerCAmelCase:  int        = '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'						):
    __lowerCAmelCase:  str        = 'model_ema.' + ''.join(key.split('.'						)[1:]						)
    __lowerCAmelCase:  int        = checkpoint.pop(SCREAMING_SNAKE_CASE						)
 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:
   __lowerCAmelCase:  str        = checkpoint.pop(SCREAMING_SNAKE_CASE						)

 __lowerCAmelCase:  List[Any]        = {}

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

 __lowerCAmelCase:  str        = unet_state_dict['input_blocks.0.0.weight']
 __lowerCAmelCase:  List[str]        = unet_state_dict['input_blocks.0.0.bias']

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

 __lowerCAmelCase:  List[Any]        = unet_state_dict['out.0.weight']
 __lowerCAmelCase:  int        = unet_state_dict['out.0.bias']
 __lowerCAmelCase:  Any        = unet_state_dict['out.2.weight']
 __lowerCAmelCase:  Any        = unet_state_dict['out.2.bias']

 # Retrieves the keys for the input blocks only
 __lowerCAmelCase:  int        = len({'.'.join(layer.split('.'						)[:2]						) for layer in unet_state_dict if 'input_blocks' in layer}						)
 __lowerCAmelCase:  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(SCREAMING_SNAKE_CASE						)
 }

 # Retrieves the keys for the middle blocks only
 __lowerCAmelCase:  Union[str, Any]        = len({'.'.join(layer.split('.'						)[:2]						) for layer in unet_state_dict if 'middle_block' in layer}						)
 __lowerCAmelCase:  int        = {
     layer_id: [key for key in unet_state_dict if f'''middle_block.{layer_id}''' in key]
     for layer_id in range(SCREAMING_SNAKE_CASE						)
 }

 # Retrieves the keys for the output blocks only
 __lowerCAmelCase:  Any        = len({'.'.join(layer.split('.'						)[:2]						) for layer in unet_state_dict if 'output_blocks' in layer}						)
 __lowerCAmelCase:  Tuple        = {
     layer_id: [key for key in unet_state_dict if f'''output_blocks.{layer_id}''' in key]
     for layer_id in range(SCREAMING_SNAKE_CASE						)
 }

 for i in range(1       ,   SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Any        = (i - 1) // (config['layers_per_block'] + 1)
  __lowerCAmelCase:  Dict        = (i - 1) % (config['layers_per_block'] + 1)

  __lowerCAmelCase:  Optional[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
  ]
  __lowerCAmelCase:  str        = [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
  __lowerCAmelCase:  str        = [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:
   __lowerCAmelCase:  List[Any]        = unet_state_dict.pop(
       f'''input_blocks.{i}.op.weight'''						)
   __lowerCAmelCase:  Dict        = unet_state_dict.pop(
       f'''input_blocks.{i}.op.bias'''						)

  __lowerCAmelCase:  Dict        = renew_resnet_paths(SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Optional[Any]        = {'old': f'''input_blocks.{i}.0''', 'new': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
  assign_to_checkpoint(
      SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]       ,   config=SCREAMING_SNAKE_CASE						)

  __lowerCAmelCase:  Tuple        = [key for key in resnets if 'temopral_conv' in key]
  __lowerCAmelCase:  Optional[Any]        = renew_temp_conv_paths(SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Tuple        = {
      'old': f'''input_blocks.{i}.0.temopral_conv''',
      'new': f'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
  }
  assign_to_checkpoint(
      SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]       ,   config=SCREAMING_SNAKE_CASE						)

  if len(SCREAMING_SNAKE_CASE						):
   __lowerCAmelCase:  str        = renew_attention_paths(SCREAMING_SNAKE_CASE						)
   __lowerCAmelCase:  Optional[int]        = {'old': f'''input_blocks.{i}.1''', 'new': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
   assign_to_checkpoint(
       SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]       ,   config=SCREAMING_SNAKE_CASE						)

  if len(SCREAMING_SNAKE_CASE						):
   __lowerCAmelCase:  List[Any]        = renew_attention_paths(SCREAMING_SNAKE_CASE						)
   __lowerCAmelCase:  Any        = {
       'old': f'''input_blocks.{i}.2''',
       'new': f'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
   }
   assign_to_checkpoint(
       SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]       ,   config=SCREAMING_SNAKE_CASE						)

 __lowerCAmelCase:  Dict        = middle_blocks[0]
 __lowerCAmelCase:  int        = [key for key in resnet_a if 'temopral_conv' in key]
 __lowerCAmelCase:  Optional[Any]        = middle_blocks[1]
 __lowerCAmelCase:  Any        = middle_blocks[2]
 __lowerCAmelCase:  Optional[Any]        = middle_blocks[3]
 __lowerCAmelCase:  Any        = [key for key in resnet_a if 'temopral_conv' in key]

 __lowerCAmelCase:  str        = renew_resnet_paths(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  int        = {'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   config=SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]						)

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

 __lowerCAmelCase:  Union[str, Any]        = renew_resnet_paths(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Optional[int]        = {'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   config=SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]						)

 __lowerCAmelCase:  int        = renew_temp_conv_paths(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Tuple        = {'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   config=SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]						)

 __lowerCAmelCase:  Any        = renew_attention_paths(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  List[str]        = {'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]       ,   config=SCREAMING_SNAKE_CASE						)

 __lowerCAmelCase:  Optional[int]        = renew_attention_paths(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  str        = {'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]       ,   config=SCREAMING_SNAKE_CASE						)

 for i in range(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Union[str, Any]        = i // (config['layers_per_block'] + 1)
  __lowerCAmelCase:  Optional[Any]        = i % (config['layers_per_block'] + 1)
  __lowerCAmelCase:  List[Any]        = [shave_segments(SCREAMING_SNAKE_CASE       ,   2						) for name in output_blocks[i]]
  __lowerCAmelCase:  Optional[Any]        = {}

  for layer in output_block_layers:
   __lowerCAmelCase      ,				__lowerCAmelCase:  List[Any]        = layer.split('.'						)[0], shave_segments(SCREAMING_SNAKE_CASE       ,   1						)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(SCREAMING_SNAKE_CASE						)
   else:
    __lowerCAmelCase:  Dict        = [layer_name]

  if len(SCREAMING_SNAKE_CASE						) > 1:
   __lowerCAmelCase:  str        = [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
   __lowerCAmelCase:  int        = [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
   __lowerCAmelCase:  Optional[Any]        = [key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

   __lowerCAmelCase:  int        = renew_resnet_paths(SCREAMING_SNAKE_CASE						)
   __lowerCAmelCase:  int        = renew_resnet_paths(SCREAMING_SNAKE_CASE						)

   __lowerCAmelCase:  Optional[int]        = {'old': f'''output_blocks.{i}.0''', 'new': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
   assign_to_checkpoint(
       SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]       ,   config=SCREAMING_SNAKE_CASE						)

   __lowerCAmelCase:  str        = [key for key in resnets if 'temopral_conv' in key]
   __lowerCAmelCase:  Tuple        = renew_temp_conv_paths(SCREAMING_SNAKE_CASE						)
   __lowerCAmelCase:  Tuple        = {
       'old': f'''output_blocks.{i}.0.temopral_conv''',
       'new': f'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
   }
   assign_to_checkpoint(
       SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]       ,   config=SCREAMING_SNAKE_CASE						)

   __lowerCAmelCase:  List[str]        = {k: sorted(SCREAMING_SNAKE_CASE						) for k, v in output_block_list.items()}
   if ["conv.bias", "conv.weight"] in output_block_list.values():
    __lowerCAmelCase:  Optional[int]        = list(output_block_list.values()						).index(['conv.bias', 'conv.weight']						)
    __lowerCAmelCase:  List[Any]        = unet_state_dict[
        f'''output_blocks.{i}.{index}.conv.weight'''
    ]
    __lowerCAmelCase:  Union[str, Any]        = unet_state_dict[
        f'''output_blocks.{i}.{index}.conv.bias'''
    ]

    # Clear attentions as they have been attributed above.
    if len(SCREAMING_SNAKE_CASE						) == 2:
     __lowerCAmelCase:  Dict        = []

   if len(SCREAMING_SNAKE_CASE						):
    __lowerCAmelCase:  Any        = renew_attention_paths(SCREAMING_SNAKE_CASE						)
    __lowerCAmelCase:  Dict        = {
        'old': f'''output_blocks.{i}.1''',
        'new': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
    }
    assign_to_checkpoint(
        SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[meta_path]       ,   config=SCREAMING_SNAKE_CASE						)

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

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

 return new_checkpoint


if __name__ == "__main__":
  _a									=  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.''')
  _a									=  parser.parse_args()

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

  _a									=  convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

  _a									=  set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  _a									=  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 argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_a									=  logging.get_logger(__name__)

_a									=  {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}







def _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       str:

 """simple docstring"""






 for attribute in key.split('.'						):
  __lowerCAmelCase:  Optional[int]        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 if weight_type is not None:
  __lowerCAmelCase:  Dict        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						).shape
 else:
  __lowerCAmelCase:  str        = hf_pointer.shape

 assert hf_shape == value.shape, (
     f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
     f''' {value.shape} for {full_name}'''
 )

 if weight_type == "weight":
  __lowerCAmelCase:  str        = value
 elif weight_type == "weight_g":
  __lowerCAmelCase:  List[str]        = value
 elif weight_type == "weight_v":
  __lowerCAmelCase:  List[Any]        = value
 elif weight_type == "bias":
  __lowerCAmelCase:  Optional[int]        = value
 else:
  __lowerCAmelCase:  int        = value

 logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''						)







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

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = []
 __lowerCAmelCase:  Optional[int]        = fairseq_model.state_dict()

 __lowerCAmelCase:  int        = hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __lowerCAmelCase:  List[str]        = False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   hf_model.config.feat_extract_norm == 'group'       ,   )
   __lowerCAmelCase:  List[str]        = True
  else:
   for key, mapped_key in MAPPING.items():
    __lowerCAmelCase:  List[Any]        = mapped_key

    if key in name:
     __lowerCAmelCase:  List[Any]        = True
     if "*" in mapped_key:
      __lowerCAmelCase:  Any        = name.split(SCREAMING_SNAKE_CASE						)[0].split('.'						)[-2]
      __lowerCAmelCase:  Dict        = mapped_key.replace('*'       ,   SCREAMING_SNAKE_CASE						)
     if "weight_g" in name:
      __lowerCAmelCase:  str        = 'weight_g'
     elif "weight_v" in name:
      __lowerCAmelCase:  Tuple        = 'weight_v'
     elif "weight" in name:
      __lowerCAmelCase:  Tuple        = 'weight'
     elif "bias" in name:
      __lowerCAmelCase:  Optional[Any]        = 'bias'
     else:
      __lowerCAmelCase:  Optional[Any]        = None
     set_recursively(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
    continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE						)

 logger.warning(f'''Unused weights: {unused_weights}'''						)







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     List[str]						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  Dict        = full_name.split('conv_layers.'						)[-1]
 __lowerCAmelCase:  Optional[Any]        = name.split('.'						)
 __lowerCAmelCase:  List[str]        = int(items[0]						)
 __lowerCAmelCase:  str        = int(items[1]						)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       f'''{full_name} has size {value.shape}, but'''
       f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
   )
   __lowerCAmelCase:  Optional[int]        = value
   logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       f'''{full_name} has size {value.shape}, but'''
       f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
   )
   __lowerCAmelCase:  Tuple        = value
   logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
       " found."
   )
   __lowerCAmelCase:  List[Any]        = value
   logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       f'''{full_name} has size {value.shape}, but'''
       f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
   )
   __lowerCAmelCase:  Any        = value
   logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
 else:
  unused_weights.append(SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  str        = HubertConfig()
 __lowerCAmelCase:  Any        = model.config

 __lowerCAmelCase:  str        = fs_config.activation_dropout
 __lowerCAmelCase:  Optional[Any]        = False
 __lowerCAmelCase:  Tuple        = fs_config.attention_dropout
 __lowerCAmelCase:  Tuple        = False
 __lowerCAmelCase:  Tuple        = eval(fs_config.extractor_conv_feature_layers						)
 __lowerCAmelCase:  Optional[int]        = [x[0] for x in conv_layers]
 __lowerCAmelCase:  Dict        = [x[1] for x in conv_layers]
 __lowerCAmelCase:  List[str]        = [x[2] for x in conv_layers]
 __lowerCAmelCase:  Dict        = 'gelu'
 __lowerCAmelCase:  List[str]        = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
 __lowerCAmelCase:  int        = False
 __lowerCAmelCase:  Dict        = 0.0
 __lowerCAmelCase:  str        = 0.0
 __lowerCAmelCase:  List[Any]        = fs_config.activation_fn
 __lowerCAmelCase:  str        = fs_config.dropout
 __lowerCAmelCase:  List[str]        = fs_config.encoder_embed_dim
 __lowerCAmelCase:  str        = 0.0_2
 __lowerCAmelCase:  List[Any]        = fs_config.encoder_ffn_embed_dim
 __lowerCAmelCase:  List[str]        = 1E-5
 __lowerCAmelCase:  Optional[Any]        = 0.0
 __lowerCAmelCase:  int        = fs_config.encoder_attention_heads
 __lowerCAmelCase:  Any        = fs_config.conv_pos_groups
 __lowerCAmelCase:  str        = fs_config.conv_pos
 __lowerCAmelCase:  Optional[int]        = len(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Dict        = fs_config.encoder_layers

 return config







@torch.no_grad()
def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]=None						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = distilhubert().model.model

 if config_path is not None:
  __lowerCAmelCase:  Dict        = HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
 else:
  __lowerCAmelCase:  Union[str, Any]        = convert_config(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Any        = model.eval()

 __lowerCAmelCase:  Tuple        = WavaVecaFeatureExtractor(
     feature_size=1       ,   sampling_rate=1_60_00       ,   padding_value=0       ,   do_normalize=SCREAMING_SNAKE_CASE       ,   return_attention_mask=SCREAMING_SNAKE_CASE       ,   )
 __lowerCAmelCase:  Tuple        = HubertModel(SCREAMING_SNAKE_CASE						)

 recursively_load_weights(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE						)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
  _a									=  argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  _a									=  parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





