


"""simple docstring"""





import argparse

import torch

from diffusers import UNetaDConditionModel


def   _snake_case    (    _snake_case					:      Any			, _snake_case					:      List[str]			, _snake_case					:      int			, _snake_case					:      str=None			, _snake_case					:      Dict=None			, _snake_case					:      Union[str, Any]=None      ):
      assert isinstance(_snake_case			, _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       :						Any							= old_checkpoint[path]
                  lowerCAmelCase       :						Union[str, Any]							= old_tensor.shape[0] // 3

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

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

                  lowerCAmelCase       :						Optional[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       :						List[Any]							= query.reshape(_snake_case      )
                  lowerCAmelCase       :						Union[str, Any]							= key.reshape(_snake_case      )
                  lowerCAmelCase       :						int							= value.reshape(_snake_case      )

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

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


def   _snake_case    (    _snake_case					:      Optional[int]			, _snake_case					:      Union[str, Any]=0      ):
      lowerCAmelCase       :						Optional[int]							= []
      for old_item in old_list:
            lowerCAmelCase       :						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   _snake_case    (    _snake_case					:      int			, _snake_case					:      Any=1      ):
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split('''.'''      )[n_shave_prefix_segments:]      )
      else:
            return ".".join(path.split('''.'''      )[:n_shave_prefix_segments]      )


def   _snake_case    (    _snake_case					:      int			, _snake_case					:      Optional[Any]=0      ):
      lowerCAmelCase       :						Tuple							= []
      for old_item in old_list:
            mapping.append({'''old''': old_item, '''new''': old_item}      )

      return mapping


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

            lowerCAmelCase       :						int							= new_item.replace('''out_layers.0'''			, '''norm2'''      )
            lowerCAmelCase       :						str							= new_item.replace('''out_layers.3'''			, '''conv2'''      )

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

            lowerCAmelCase       :						str							= shave_segments(_snake_case			, n_shave_prefix_segments=_snake_case      )

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

      return mapping


def   _snake_case    (    _snake_case					:      Optional[int]			, _snake_case					:      List[str]			, _snake_case					:      Optional[int]=None			, _snake_case					:      List[Any]=False      ):
      lowerCAmelCase       :						Union[str, Any]							= {}
      lowerCAmelCase       :						List[Any]							= list(checkpoint.keys()      )

      lowerCAmelCase       :						str							= '''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'''      ):
                        lowerCAmelCase       :						Any							= '''model_ema.''' + ''''''.join(key.split('''.'''      )[1:]      )
                        lowerCAmelCase       :						str							= checkpoint.pop(_snake_case      )
      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:
                  lowerCAmelCase       :						int							= checkpoint.pop(_snake_case      )

      lowerCAmelCase       :						str							= {}

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

      lowerCAmelCase       :						Dict							= unet_state_dict['''input_blocks.0.0.weight''']
      lowerCAmelCase       :						Tuple							= unet_state_dict['''input_blocks.0.0.bias''']

      lowerCAmelCase       :						Dict							= [v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''      )]
      lowerCAmelCase       :						List[Any]							= renew_attention_paths(_snake_case      )
      lowerCAmelCase       :						Dict							= {'''old''': '''input_blocks.0.1''', '''new''': '''transformer_in'''}
      assign_to_checkpoint(_snake_case			, _snake_case			, _snake_case			, additional_replacements=[meta_path]			, config=_snake_case      )

      lowerCAmelCase       :						str							= unet_state_dict['''out.0.weight''']
      lowerCAmelCase       :						List[str]							= unet_state_dict['''out.0.bias''']
      lowerCAmelCase       :						List[str]							= unet_state_dict['''out.2.weight''']
      lowerCAmelCase       :						int							= unet_state_dict['''out.2.bias''']

      # Retrieves the keys for the input blocks only
      lowerCAmelCase       :						Dict							= len({'''.'''.join(layer.split('''.'''      )[:2]      ) for layer in unet_state_dict if '''input_blocks''' in layer}      )
      lowerCAmelCase       :						int							= {
          layer_id: [key for key in unet_state_dict if f'''input_blocks.{layer_id}''' in key]
          for layer_id in range(_snake_case      )
      }

      # Retrieves the keys for the middle blocks only
      lowerCAmelCase       :						List[Any]							= len({'''.'''.join(layer.split('''.'''      )[:2]      ) for layer in unet_state_dict if '''middle_block''' in layer}      )
      lowerCAmelCase       :						Dict							= {
          layer_id: [key for key in unet_state_dict if f'''middle_block.{layer_id}''' in key]
          for layer_id in range(_snake_case      )
      }

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

      for i in range(1			, _snake_case      ):
            lowerCAmelCase       :						List[str]							= (i - 1) // (config['''layers_per_block'''] + 1)
            lowerCAmelCase       :						Any							= (i - 1) % (config['''layers_per_block'''] + 1)

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

            lowerCAmelCase       :						List[Any]							= renew_resnet_paths(_snake_case      )
            lowerCAmelCase       :						List[str]							= {'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
            assign_to_checkpoint(
                _snake_case			, _snake_case			, _snake_case			, additional_replacements=[meta_path]			, config=_snake_case      )

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

            if len(_snake_case      ):
                  lowerCAmelCase       :						Dict							= renew_attention_paths(_snake_case      )
                  lowerCAmelCase       :						Tuple							= {'''old''': f'''input_blocks.{i}.1''', '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
                  assign_to_checkpoint(
                      _snake_case			, _snake_case			, _snake_case			, additional_replacements=[meta_path]			, config=_snake_case      )

            if len(_snake_case      ):
                  lowerCAmelCase       :						Union[str, Any]							= renew_attention_paths(_snake_case      )
                  lowerCAmelCase       :						Dict							= {
                      '''old''': f'''input_blocks.{i}.2''',
                      '''new''': f'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                  }
                  assign_to_checkpoint(
                      _snake_case			, _snake_case			, _snake_case			, additional_replacements=[meta_path]			, config=_snake_case      )

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

      lowerCAmelCase       :						Union[str, Any]							= renew_resnet_paths(_snake_case      )
      lowerCAmelCase       :						List[Any]							= {'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
      assign_to_checkpoint(
          _snake_case			, _snake_case			, _snake_case			, config=_snake_case			, additional_replacements=[meta_path]      )

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

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

      lowerCAmelCase       :						Union[str, Any]							= renew_temp_conv_paths(_snake_case      )
      lowerCAmelCase       :						Tuple							= {'''old''': '''middle_block.3.temopral_conv''', '''new''': '''mid_block.temp_convs.1'''}
      assign_to_checkpoint(
          _snake_case			, _snake_case			, _snake_case			, config=_snake_case			, additional_replacements=[meta_path]      )

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

      lowerCAmelCase       :						Dict							= renew_attention_paths(_snake_case      )
      lowerCAmelCase       :						List[Any]							= {'''old''': '''middle_block.2''', '''new''': '''mid_block.temp_attentions.0'''}
      assign_to_checkpoint(
          _snake_case			, _snake_case			, _snake_case			, additional_replacements=[meta_path]			, config=_snake_case      )

      for i in range(_snake_case      ):
            lowerCAmelCase       :						Any							= i // (config['''layers_per_block'''] + 1)
            lowerCAmelCase       :						List[str]							= i % (config['''layers_per_block'''] + 1)
            lowerCAmelCase       :						List[Any]							= [shave_segments(_snake_case			, 2      ) for name in output_blocks[i]]
            lowerCAmelCase       :						Tuple							= {}

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

            if len(_snake_case      ) > 1:
                  lowerCAmelCase       :						Optional[Any]							= [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       :						List[str]							= [key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

                  lowerCAmelCase       :						Optional[Any]							= renew_resnet_paths(_snake_case      )
                  lowerCAmelCase       :						List[str]							= renew_resnet_paths(_snake_case      )

                  lowerCAmelCase       :						Tuple							= {'''old''': f'''output_blocks.{i}.0''', '''new''': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
                  assign_to_checkpoint(
                      _snake_case			, _snake_case			, _snake_case			, additional_replacements=[meta_path]			, config=_snake_case      )

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

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

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

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

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

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

      return new_checkpoint


if __name__ == "__main__":
 snake_case__		:  int     					=					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__		:  int     					=					parser.parse_args()

 snake_case__		:  Tuple     					=					torch.load(args.checkpoint_path, map_location='''cpu''')
 snake_case__		:  List[Any]     					=					UNetaDConditionModel()

 snake_case__		:  Optional[int]     					=					convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

 snake_case__		:  Tuple     					=					set(unet.state_dict().keys()) - set(converted_ckpt.keys())
 snake_case__		:  Optional[int]     					=					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"""





from collections.abc import Callable

snake_case__		:  Optional[int]     					=					Callable[[float], float]  # type alias for a real -> real function


def   _snake_case    (    _snake_case					:      RealFunc			, _snake_case					:      RealFunc			, _snake_case					:      int			, ):
      lowerCAmelCase       :						Dict							= float(_snake_case      )
      while True:
            try:
                  lowerCAmelCase       :						Optional[Any]							= prev_guess - function(_snake_case      ) / derivative(_snake_case      )
            except ZeroDivisionError:
                  raise ZeroDivisionError('''Could not find root'''      ) from None
            if abs(prev_guess - next_guess      ) < 10**-5:
                  return next_guess
            lowerCAmelCase       :						Optional[int]							= next_guess


def   _snake_case    (    _snake_case					:      float      ):
      return (x**3) - (2 * x) - 5


def   _snake_case    (    _snake_case					:      float      ):
      return 3 * (x**2) - 2


if __name__ == "__main__":
 print(newton(f, fa, 3))


