






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase=1) ->			Optional[Any]:
      """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 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase=0) ->			Dict:
      """simple docstring"""

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

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

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

            UpperCamelCase					       =  shave_segments(_UpperCamelCase       ,							n_shave_prefix_segments=_UpperCamelCase)

            mapping.append({'old': old_item, 'new': new_item})

      return mapping
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase=0) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  []
      for old_item in old_list:
            UpperCamelCase					       =  old_item

            UpperCamelCase					       =  new_item.replace('norm.weight'       ,							'group_norm.weight')
            UpperCamelCase					       =  new_item.replace('norm.bias'       ,							'group_norm.bias')

            UpperCamelCase					       =  new_item.replace('proj_out.weight'       ,							'proj_attn.weight')
            UpperCamelCase					       =  new_item.replace('proj_out.bias'       ,							'proj_attn.bias')

            UpperCamelCase					       =  shave_segments(_UpperCamelCase       ,							n_shave_prefix_segments=_UpperCamelCase)

            mapping.append({'old': old_item, 'new': new_item})

      return mapping
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None       ,							_UpperCamelCase=None       ,							_UpperCamelCase=None) ->			int:
      """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():
                  UpperCamelCase					       =  old_checkpoint[path]
                  UpperCamelCase					       =  old_tensor.shape[0] // 3

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

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

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

                  UpperCamelCase					       =  query.reshape(_UpperCamelCase)
                  UpperCamelCase					       =  key.reshape(_UpperCamelCase)
                  UpperCamelCase					       =  value.reshape(_UpperCamelCase)

      for path in paths:
            UpperCamelCase					       =  path['new']

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            # Global renaming happens here
            UpperCamelCase					       =  new_path.replace('middle_block.0'       ,							'mid_block.resnets.0')
            UpperCamelCase					       =  new_path.replace('middle_block.1'       ,							'mid_block.attentions.0')
            UpperCamelCase					       =  new_path.replace('middle_block.2'       ,							'mid_block.resnets.1')

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        UpperCamelCase					       =  new_path.replace(replacement['old']       ,							replacement['new'])

        # proj_attn.weight has to be converted from conv 1D to linear
            if "proj_attn.weight" in new_path:
                  UpperCamelCase					       =  old_checkpoint[path['old']][:, :, 0]
            else:
                  UpperCamelCase					       =  old_checkpoint[path['old']]
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			Tuple:
      """simple docstring"""

      UpperCamelCase					       =  {}

      UpperCamelCase					       =  checkpoint['time_embed.0.weight']
      UpperCamelCase					       =  checkpoint['time_embed.0.bias']
      UpperCamelCase					       =  checkpoint['time_embed.2.weight']
      UpperCamelCase					       =  checkpoint['time_embed.2.bias']

      UpperCamelCase					       =  checkpoint['input_blocks.0.0.weight']
      UpperCamelCase					       =  checkpoint['input_blocks.0.0.bias']

      UpperCamelCase					       =  checkpoint['out.0.weight']
      UpperCamelCase					       =  checkpoint['out.0.bias']
      UpperCamelCase					       =  checkpoint['out.2.weight']
      UpperCamelCase					       =  checkpoint['out.2.bias']

      # Retrieves the keys for the input blocks only
      UpperCamelCase					       =  len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'input_blocks' in layer})
      UpperCamelCase					       =  {
          layer_id: [key for key in checkpoint if F'input_blocks.{layer_id}' in key]
          for layer_id in range(_UpperCamelCase)
      }

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

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

      for i in range(1       ,							_UpperCamelCase):
            UpperCamelCase					       =  (i - 1) // (config['num_res_blocks'] + 1)
            UpperCamelCase					       =  (i - 1) % (config['num_res_blocks'] + 1)

            UpperCamelCase					       =  [key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
            UpperCamelCase					       =  [key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]

            if F'input_blocks.{i}.0.op.weight' in checkpoint:
                  UpperCamelCase					       =  checkpoint[
                      F'input_blocks.{i}.0.op.weight'
                  ]
                  UpperCamelCase					       =  checkpoint[
                      F'input_blocks.{i}.0.op.bias'
                  ]
                  continue

            UpperCamelCase					       =  renew_resnet_paths(_UpperCamelCase)
            UpperCamelCase					       =  {'old': F'input_blocks.{i}.0', 'new': F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
            UpperCamelCase					       =  {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
            assign_to_checkpoint(
                _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							additional_replacements=[meta_path, resnet_op]       ,							config=_UpperCamelCase)

            if len(_UpperCamelCase):
                  UpperCamelCase					       =  renew_attention_paths(_UpperCamelCase)
                  UpperCamelCase					       =  {
                      'old': F'input_blocks.{i}.1',
                      'new': F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
                  }
                  UpperCamelCase					       =  {
                      F'input_blocks.{i}.1.qkv.bias': {
                          'key': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                          'query': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                          'value': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                      },
                      F'input_blocks.{i}.1.qkv.weight': {
                          'key': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                          'query': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                          'value': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                      },
                  }
                  assign_to_checkpoint(
                      _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							additional_replacements=[meta_path]       ,							attention_paths_to_split=_UpperCamelCase       ,							config=_UpperCamelCase       ,							)

      UpperCamelCase					       =  middle_blocks[0]
      UpperCamelCase					       =  middle_blocks[1]
      UpperCamelCase					       =  middle_blocks[2]

      UpperCamelCase					       =  renew_resnet_paths(_UpperCamelCase)
      assign_to_checkpoint(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							config=_UpperCamelCase)

      UpperCamelCase					       =  renew_resnet_paths(_UpperCamelCase)
      assign_to_checkpoint(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							config=_UpperCamelCase)

      UpperCamelCase					       =  renew_attention_paths(_UpperCamelCase)
      UpperCamelCase					       =  {
          'middle_block.1.qkv.bias': {
              'key': 'mid_block.attentions.0.key.bias',
              'query': 'mid_block.attentions.0.query.bias',
              'value': 'mid_block.attentions.0.value.bias',
          },
          'middle_block.1.qkv.weight': {
              'key': 'mid_block.attentions.0.key.weight',
              'query': 'mid_block.attentions.0.query.weight',
              'value': 'mid_block.attentions.0.value.weight',
          },
      }
      assign_to_checkpoint(
          _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							attention_paths_to_split=_UpperCamelCase       ,							config=_UpperCamelCase)

      for i in range(_UpperCamelCase):
            UpperCamelCase					       =  i // (config['num_res_blocks'] + 1)
            UpperCamelCase					       =  i % (config['num_res_blocks'] + 1)
            UpperCamelCase					       =  [shave_segments(_UpperCamelCase       ,							2) for name in output_blocks[i]]
            UpperCamelCase					       =  {}

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

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

                  UpperCamelCase					       =  renew_resnet_paths(_UpperCamelCase)
                  UpperCamelCase					       =  renew_resnet_paths(_UpperCamelCase)

                  UpperCamelCase					       =  {'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)

                  if ["conv.weight", "conv.bias"] in output_block_list.values():
                        UpperCamelCase					       =  list(output_block_list.values()).index(['conv.weight', 'conv.bias'])
                        UpperCamelCase					       =  checkpoint[
                            F'output_blocks.{i}.{index}.conv.weight'
                        ]
                        UpperCamelCase					       =  checkpoint[
                            F'output_blocks.{i}.{index}.conv.bias'
                        ]

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

                  if len(_UpperCamelCase):
                        UpperCamelCase					       =  renew_attention_paths(_UpperCamelCase)
                        UpperCamelCase					       =  {
                            'old': F'output_blocks.{i}.1',
                            'new': F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                        }
                        UpperCamelCase					       =  {
                            F'output_blocks.{i}.1.qkv.bias': {
                                'key': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                                'query': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                                'value': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                            },
                            F'output_blocks.{i}.1.qkv.weight': {
                                'key': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                                'query': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                                'value': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                            },
                        }
                        assign_to_checkpoint(
                            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							additional_replacements=[meta_path]       ,							attention_paths_to_split=to_split if any('qkv' in key for key in attentions) else None       ,							config=_UpperCamelCase       ,							)
            else:
                  UpperCamelCase					       =  renew_resnet_paths(_UpperCamelCase       ,							n_shave_prefix_segments=1)
                  for path in resnet_0_paths:
                        UpperCamelCase					       =  '.'.join(['output_blocks', str(_UpperCamelCase), path['old']])
                        UpperCamelCase					       =  '.'.join(['up_blocks', str(_UpperCamelCase), 'resnets', str(_UpperCamelCase), path['new']])

                        UpperCamelCase					       =  checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
       __magic_name__   :  List[str]        =							argparse.ArgumentParser()

       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
       )

       parser.add_argument(
           '''--config_file''',
           default=None,
           type=str,
           required=True,
           help='''The config json file corresponding to the architecture.''',
       )

       parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

       __magic_name__   :  Dict        =							parser.parse_args()

       __magic_name__   :  Union[str, Any]        =							torch.load(args.checkpoint_path)

       with open(args.config_file) as f:
              __magic_name__   :  Any        =							json.loads(f.read())

       __magic_name__   :  Union[str, Any]        =							convert_ldm_checkpoint(checkpoint, config)

       if "ldm" in config:
              del config["ldm"]

       __magic_name__   :  Optional[int]        =							UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)

       try:
              __magic_name__   :  List[str]        =							DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
              __magic_name__   :  Optional[int]        =							VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

              __magic_name__   :  int        =							LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
              pipe.save_pretrained(args.dump_path)
       except:  # noqa: E722
              model.save_pretrained(args.dump_path)











from pathlib import Path

import fire
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  Path(_UpperCamelCase)
      UpperCamelCase					       =  Path(_UpperCamelCase)
      dest_dir.mkdir(exist_ok=_UpperCamelCase)
      for path in src_dir.iterdir():
            UpperCamelCase					       =  [x.rstrip() for x in list(path.open().readlines())][:n]
            UpperCamelCase					       =  dest_dir.joinpath(path.name)
            print(_UpperCamelCase)
            dest_path.open('w').write('\n'.join(_UpperCamelCase))


if __name__ == "__main__":
       fire.Fire(minify)



