





"""simple docstring"""




import argparse

import torch

from diffusers import UNetaDConditionModel





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase=None			, _UpperCAmelCase=None			, _UpperCAmelCase=None ):






  """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():
      A_  :	Optional[Any]    						=  old_checkpoint[path]
      A_  :	Any    						=  old_tensor.shape[0] // 3

      A_  :	Union[str, Any]    						=  (-1, channels) if len(old_tensor.shape ) == 3 else (-1)

      A_  :	Optional[Any]    						=  old_tensor.shape[0] // config['num_head_channels'] // 3

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

      A_  :	List[str]    						=  query.reshape(_UpperCAmelCase )
      A_  :	Any    						=  key.reshape(_UpperCAmelCase )
      A_  :	int    						=  value.reshape(_UpperCAmelCase )

  for path in paths:
    A_  :	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:
        A_  :	int    						=  new_path.replace(replacement['old']			, replacement['new'] )

        # proj_attn.weight has to be converted from conv 1D to linear
    A_  :	Tuple    						=  old_checkpoint[path['old']]
    A_  :	Any    						=  ['proj_attn.weight']
    A_  :	Union[str, Any]    						=  ['proj_out.weight', 'proj_in.weight']
    if any(k in new_path for k in names ):
      A_  :	Tuple    						=  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_  :	Tuple    						=  weight[:, :, 0]
    else:
      A_  :	str    						=  weight





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=0 ):






  """simple docstring"""





  A_  :	Any    						=  []
  for old_item in old_list:
    A_  :	List[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 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=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__		(					_UpperCAmelCase			, _UpperCAmelCase=0 ):






  """simple docstring"""





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

  return mapping





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=0 ):






  """simple docstring"""





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

    A_  :	List[Any]    						=  new_item.replace('out_layers.0'			, 'norm2' )
    A_  :	Tuple    						=  new_item.replace('out_layers.3'			, 'conv2' )

    A_  :	List[str]    						=  new_item.replace('emb_layers.1'			, 'time_emb_proj' )
    A_  :	int    						=  new_item.replace('skip_connection'			, 'conv_shortcut' )

    A_  :	Optional[Any]    						=  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 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase=None			, _UpperCAmelCase=False ):






  """simple docstring"""





  A_  :	Union[str, Any]    						=  {}
  A_  :	int    						=  list(checkpoint.keys() )

  A_  :	List[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' ):
        A_  :	str    						=  'model_ema.' + ''.join(key.split('.' )[1:] )
        A_  :	List[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:
      A_  :	Union[str, Any]    						=  checkpoint.pop(_UpperCAmelCase )

  A_  :	Union[str, Any]    						=  {}

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

  A_  :	Any    						=  unet_state_dict['input_blocks.0.0.weight']
  A_  :	Optional[int]    						=  unet_state_dict['input_blocks.0.0.bias']

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

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

  # Retrieves the keys for the input blocks only
  A_  :	List[Any]    						=  len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'input_blocks' in layer} )
  A_  :	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
  A_  :	Optional[Any]    						=  len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'middle_block' in layer} )
  A_  :	Any    						=  {
      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
  A_  :	Union[str, Any]    						=  len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'output_blocks' in layer} )
  A_  :	Tuple    						=  {
      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 ):
    A_  :	Any    						=  (i - 1) // (config['layers_per_block'] + 1)
    A_  :	Tuple    						=  (i - 1) % (config['layers_per_block'] + 1)

    A_  :	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
    ]
    A_  :	Optional[int]    						=  [key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]
    A_  :	List[Any]    						=  [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_  :	List[Any]    						=  unet_state_dict.pop(
          f"""input_blocks.{i}.op.weight""" )
      A_  :	Dict    						=  unet_state_dict.pop(
          f"""input_blocks.{i}.op.bias""" )

    A_  :	str    						=  renew_resnet_paths(_UpperCAmelCase )
    A_  :	List[str]    						=  {'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 )

    A_  :	Tuple    						=  [key for key in resnets if 'temopral_conv' in key]
    A_  :	Any    						=  renew_temp_conv_paths(_UpperCAmelCase )
    A_  :	Optional[int]    						=  {
        '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 ):
      A_  :	List[str]    						=  renew_attention_paths(_UpperCAmelCase )
      A_  :	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 ):
      A_  :	List[str]    						=  renew_attention_paths(_UpperCAmelCase )
      A_  :	Tuple    						=  {
          '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 )

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

  A_  :	Dict    						=  renew_resnet_paths(_UpperCAmelCase )
  A_  :	Any    						=  {'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  assign_to_checkpoint(
      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, config=_UpperCAmelCase			, additional_replacements=[meta_path] )

  A_  :	Dict    						=  renew_temp_conv_paths(_UpperCAmelCase )
  A_  :	List[str]    						=  {'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  assign_to_checkpoint(
      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, config=_UpperCAmelCase			, additional_replacements=[meta_path] )

  A_  :	str    						=  renew_resnet_paths(_UpperCAmelCase )
  A_  :	List[str]    						=  {'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  assign_to_checkpoint(
      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, config=_UpperCAmelCase			, additional_replacements=[meta_path] )

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

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

  A_  :	str    						=  renew_attention_paths(_UpperCAmelCase )
  A_  :	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 ):
    A_  :	int    						=  i // (config['layers_per_block'] + 1)
    A_  :	Optional[Any]    						=  i % (config['layers_per_block'] + 1)
    A_  :	Optional[int]    						=  [shave_segments(_UpperCAmelCase			, 2 ) for name in output_blocks[i]]
    A_  :	Tuple    						=  {}

    for layer in output_block_layers:
      A_  :	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:
        A_  :	Any    						=  [layer_name]

    if len(_UpperCAmelCase ) > 1:
      A_  :	Optional[int]    						=  [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
      A_  :	int    						=  [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]
      A_  :	Optional[int]    						=  [key for key in output_blocks[i] if f"""output_blocks.{i}.2""" in key]

      A_  :	str    						=  renew_resnet_paths(_UpperCAmelCase )
      A_  :	str    						=  renew_resnet_paths(_UpperCAmelCase )

      A_  :	str    						=  {'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 )

      A_  :	str    						=  [key for key in resnets if 'temopral_conv' in key]
      A_  :	List[Any]    						=  renew_temp_conv_paths(_UpperCAmelCase )
      A_  :	List[Any]    						=  {
          '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 )

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

        # Clear attentions as they have been attributed above.
        if len(_UpperCAmelCase ) == 2:
          A_  :	Optional[int]    						=  []

      if len(_UpperCAmelCase ):
        A_  :	Any    						=  renew_attention_paths(_UpperCAmelCase )
        A_  :	Optional[int]    						=  {
            '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 ):
        A_  :	Tuple    						=  renew_attention_paths(_UpperCAmelCase )
        A_  :	List[Any]    						=  {
            '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:
      A_  :	str    						=  renew_resnet_paths(_UpperCAmelCase			, n_shave_prefix_segments=1 )
      for path in resnet_0_paths:
        A_  :	str    						=  '.'.join(['output_blocks', str(_UpperCAmelCase ), path['old']] )
        A_  :	Any    						=  '.'.join(['up_blocks', str(_UpperCAmelCase ), 'resnets', str(_UpperCAmelCase ), path['new']] )
        A_  :	Optional[Any]    						=  unet_state_dict[old_path]

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

  return new_checkpoint


if __name__ == "__main__":
   lowerCamelCase_					:						Dict      = 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_					:						Optional[int]      = parser.parse_args()

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

   lowerCamelCase_					:						List[Any]      = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

   lowerCamelCase_					:						Union[str, Any]      = set(unet.state_dict().keys()) - set(converted_ckpt.keys())
   lowerCamelCase_					:						Tuple      = 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 --






"""simple docstring"""




def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Any    						=  len(_UpperCAmelCase )
  # Create a stack and push index of fist element to it
  A_  :	List[Any]    						=  []
  st.append(0 )

  # Span value of first element is always 1
  A_  :	List[str]    						=  1

  # Calculate span values for rest of the elements
  for i in range(1			, _UpperCAmelCase ):
    # Pop elements from stack while stack is not
    # empty and top of stack is smaller than price[i]
    while len(_UpperCAmelCase ) > 0 and price[st[0]] <= price[i]:
      st.pop()

    # If stack becomes empty, then price[i] is greater
    # than all elements on left of it, i.e. price[0],
    # price[1], ..price[i-1]. Else the price[i]  is
    # greater than elements after top of stack
    A_  :	Union[str, Any]    						=  i + 1 if len(_UpperCAmelCase ) <= 0 else (i - st[0])

    # Push this element to stack
    st.append(_UpperCAmelCase )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  for i in range(0			, _UpperCAmelCase ):
    print(arr[i]			, end=' ' )


# Driver program to test above function
lowerCamelCase_					:						List[Any]      = [10, 4, 5, 90, 1_20, 80]
lowerCamelCase_					:						Optional[Any]      = [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)





print_array(S, len(price))