
import argparse

import torch

from diffusers import UNetaDConditionModel
def lowerCamelCase_       (			A			:      Optional[int]		,					A			:      str		,					A			:      str		,					A			:      Optional[int]=None		,					A			:      str=None		,					A			:      Any=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():
                  lowerCAmelCase_						=      old_checkpoint[path]
                  lowerCAmelCase_						=      old_tensor.shape[0] // 3

                  lowerCAmelCase_						=      (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

                  lowerCAmelCase_						=      old_tensor.shape[0] // config['num_head_channels'] // 3

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

                  lowerCAmelCase_						=      query.reshape(A						)
                  lowerCAmelCase_						=      key.reshape(A						)
                  lowerCAmelCase_						=      value.reshape(A						)

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

                            # proj_attn.weight has to be converted from conv 1D to linear
            lowerCAmelCase_						=      old_checkpoint[path['old']]
            lowerCAmelCase_						=      ['proj_attn.weight']
            lowerCAmelCase_						=      ['proj_out.weight', 'proj_in.weight']
            if any(k in new_path for k in names						):
                  lowerCAmelCase_						=      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_						=      weight[:, :, 0]
            else:
                  lowerCAmelCase_						=      weight
def lowerCamelCase_       (			A			:      Optional[int]		,					A			:      List[str]=0						):




      """simple docstring"""
      lowerCAmelCase_						=      []
      for old_item in old_list:
            lowerCAmelCase_						=      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_       (			A			:      Tuple		,					A			:      int=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 lowerCamelCase_       (			A			:      List[str]		,					A			:      Dict=0						):




      """simple docstring"""
      lowerCAmelCase_						=      []
      for old_item in old_list:
            mapping.append({'''old''': old_item, '''new''': old_item}						)

      return mapping
def lowerCamelCase_       (			A			:      List[Any]		,					A			:      int=0						):




      """simple docstring"""
      lowerCAmelCase_						=      []
      for old_item in old_list:
            lowerCAmelCase_						=      old_item.replace('''in_layers.0'''		,					'''norm1'''						)
            lowerCAmelCase_						=      new_item.replace('''in_layers.2'''		,					'''conv1'''						)

            lowerCAmelCase_						=      new_item.replace('''out_layers.0'''		,					'''norm2'''						)
            lowerCAmelCase_						=      new_item.replace('''out_layers.3'''		,					'''conv2'''						)

            lowerCAmelCase_						=      new_item.replace('''emb_layers.1'''		,					'''time_emb_proj'''						)
            lowerCAmelCase_						=      new_item.replace('''skip_connection'''		,					'''conv_shortcut'''						)

            lowerCAmelCase_						=      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 lowerCamelCase_       (			A			:      Union[str, Any]		,					A			:      List[Any]		,					A			:      Optional[int]=None		,					A			:      Optional[int]=False						):




      """simple docstring"""
      lowerCAmelCase_						=      {}
      lowerCAmelCase_						=      list(checkpoint.keys()						)

      lowerCAmelCase_						=      '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_						=      'model_ema.' + ''.join(key.split('''.'''						)[1:]						)
                        lowerCAmelCase_						=      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:
                  lowerCAmelCase_						=      checkpoint.pop(A						)

      lowerCAmelCase_						=      {}

      lowerCAmelCase_						=      unet_state_dict['time_embed.0.weight']
      lowerCAmelCase_						=      unet_state_dict['time_embed.0.bias']
      lowerCAmelCase_						=      unet_state_dict['time_embed.2.weight']
      lowerCAmelCase_						=      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_						=      unet_state_dict['label_emb.0.0.weight']
            lowerCAmelCase_						=      unet_state_dict['label_emb.0.0.bias']
            lowerCAmelCase_						=      unet_state_dict['label_emb.0.2.weight']
            lowerCAmelCase_						=      unet_state_dict['label_emb.0.2.bias']
      else:
            raise NotImplementedError(F'Not implemented `class_embed_type`: {config["class_embed_type"]}'						)

      lowerCAmelCase_						=      unet_state_dict['input_blocks.0.0.weight']
      lowerCAmelCase_						=      unet_state_dict['input_blocks.0.0.bias']

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

      lowerCAmelCase_						=      unet_state_dict['out.0.weight']
      lowerCAmelCase_						=      unet_state_dict['out.0.bias']
      lowerCAmelCase_						=      unet_state_dict['out.2.weight']
      lowerCAmelCase_						=      unet_state_dict['out.2.bias']

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

            lowerCAmelCase_						=      [
                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_						=      [key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]
            lowerCAmelCase_						=      [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_						=      unet_state_dict.pop(
                      F'input_blocks.{i}.op.weight'						)
                  lowerCAmelCase_						=      unet_state_dict.pop(
                      F'input_blocks.{i}.op.bias'						)

            lowerCAmelCase_						=      renew_resnet_paths(A						)
            lowerCAmelCase_						=      {'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						)

            lowerCAmelCase_						=      [key for key in resnets if 'temopral_conv' in key]
            lowerCAmelCase_						=      renew_temp_conv_paths(A						)
            lowerCAmelCase_						=      {
                '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						):
                  lowerCAmelCase_						=      renew_attention_paths(A						)
                  lowerCAmelCase_						=      {'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						):
                  lowerCAmelCase_						=      renew_attention_paths(A						)
                  lowerCAmelCase_						=      {
                      '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						)

      lowerCAmelCase_						=      middle_blocks[0]
      lowerCAmelCase_						=      [key for key in resnet_a if 'temopral_conv' in key]
      lowerCAmelCase_						=      middle_blocks[1]
      lowerCAmelCase_						=      middle_blocks[2]
      lowerCAmelCase_						=      middle_blocks[3]
      lowerCAmelCase_						=      [key for key in resnet_a if 'temopral_conv' in key]

      lowerCAmelCase_						=      renew_resnet_paths(A						)
      lowerCAmelCase_						=      {'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
      assign_to_checkpoint(
          A		,					A		,					A		,					config=A		,					additional_replacements=[meta_path]						)

      lowerCAmelCase_						=      renew_temp_conv_paths(A						)
      lowerCAmelCase_						=      {'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
      assign_to_checkpoint(
          A		,					A		,					A		,					config=A		,					additional_replacements=[meta_path]						)

      lowerCAmelCase_						=      renew_resnet_paths(A						)
      lowerCAmelCase_						=      {'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
      assign_to_checkpoint(
          A		,					A		,					A		,					config=A		,					additional_replacements=[meta_path]						)

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

      lowerCAmelCase_						=      renew_attention_paths(A						)
      lowerCAmelCase_						=      {'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
      assign_to_checkpoint(
          A		,					A		,					A		,					additional_replacements=[meta_path]		,					config=A						)

      lowerCAmelCase_						=      renew_attention_paths(A						)
      lowerCAmelCase_						=      {'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						):
            lowerCAmelCase_						=      i // (config['layers_per_block'] + 1)
            lowerCAmelCase_						=      i % (config['layers_per_block'] + 1)
            lowerCAmelCase_						=      [shave_segments(A		,					2						) for name in output_blocks[i]]
            lowerCAmelCase_						=      {}

            for layer in output_block_layers:
                  lowerCAmelCase_						=      layer.split('''.'''						)[0], shave_segments(A		,					1						)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(A						)
                  else:
                        lowerCAmelCase_						=      [layer_name]

            if len(A						) > 1:
                  lowerCAmelCase_						=      [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
                  lowerCAmelCase_						=      [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]
                  lowerCAmelCase_						=      [key for key in output_blocks[i] if F'output_blocks.{i}.2' in key]

                  lowerCAmelCase_						=      renew_resnet_paths(A						)
                  lowerCAmelCase_						=      renew_resnet_paths(A						)

                  lowerCAmelCase_						=      {'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						)

                  lowerCAmelCase_						=      [key for key in resnets if 'temopral_conv' in key]
                  lowerCAmelCase_						=      renew_temp_conv_paths(A						)
                  lowerCAmelCase_						=      {
                      '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						)

                  lowerCAmelCase_						=      {k: sorted(A						) for k, v in output_block_list.items()}
                  if ["conv.bias", "conv.weight"] in output_block_list.values():
                        lowerCAmelCase_						=      list(output_block_list.values()						).index(['''conv.bias''', '''conv.weight''']						)
                        lowerCAmelCase_						=      unet_state_dict[
                            F'output_blocks.{i}.{index}.conv.weight'
                        ]
                        lowerCAmelCase_						=      unet_state_dict[
                            F'output_blocks.{i}.{index}.conv.bias'
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(A						) == 2:
                              lowerCAmelCase_						=      []

                  if len(A						):
                        lowerCAmelCase_						=      renew_attention_paths(A						)
                        lowerCAmelCase_						=      {
                            '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						):
                        lowerCAmelCase_						=      renew_attention_paths(A						)
                        lowerCAmelCase_						=      {
                            '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:
                  lowerCAmelCase_						=      renew_resnet_paths(A		,					n_shave_prefix_segments=1						)
                  for path in resnet_0_paths:
                        lowerCAmelCase_						=      '.'.join(['''output_blocks''', str(A						), path['''old''']]						)
                        lowerCAmelCase_						=      '.'.join(['''up_blocks''', str(A						), '''resnets''', str(A						), path['''new''']]						)
                        lowerCAmelCase_						=      unet_state_dict[old_path]

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

      return new_checkpoint


if __name__ == "__main__":
 _snake_case		  =   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.")
 _snake_case		  =   parser.parse_args()

 _snake_case		  =   torch.load(args.checkpoint_path, map_location="cpu")
 _snake_case		  =   UNetaDConditionModel()

 _snake_case		  =   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

 _snake_case		  =   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
 _snake_case		  =   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 --






def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 return "".join(chr(ord(SCREAMING_SNAKE_CASE						) + 32						) if 'A' <= char <= 'Z' else char for char in word						)


if __name__ == "__main__":
  from doctest import testmod

  testmod()





