




import argparse

import torch

from diffusers import UNetaDConditionModel


def   A		(__A  :       Tuple  ,							__A  :       Tuple  ,							__A  :       Optional[Any]  ,							__A  :       List[str]=None  ,							__A  :       Union[str, Any]=None  ,							__A  :       List[str]=None			)  -> str:







     """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():
               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(__A			)
               UpperCAmelCase_		=				key.reshape(__A			)
               UpperCAmelCase_		=				value.reshape(__A			)

     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

          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
          UpperCAmelCase_		=				old_checkpoint[path['''old''']]
          UpperCAmelCase_		=				['''proj_attn.weight''']
          UpperCAmelCase_		=				['''proj_out.weight''', '''proj_in.weight''']
          if any(k in new_path for k in names			):
               UpperCAmelCase_		=				weight[:, :, 0]
          elif any(k in new_path for k in names_a			) and len(weight.shape			) > 2 and ".attentions." not in new_path:
               UpperCAmelCase_		=				weight[:, :, 0]
          else:
               UpperCAmelCase_		=				weight


def   A		(__A  :       List[Any]  ,							__A  :       List[Any]=0			)  -> Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_		=				[]
     for old_item in old_list:
          UpperCAmelCase_		=				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		(__A  :       Union[str, Any]  ,							__A  :       List[Any]=1			)  -> Tuple:







     """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		(__A  :       Dict  ,							__A  :       Union[str, Any]=0			)  -> str:







     """simple docstring"""


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

     return mapping


def   A		(__A  :       Dict  ,							__A  :       Optional[int]=0			)  -> str:







     """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(__A  ,							n_shave_prefix_segments=__A			)

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

     return mapping


def   A		(__A  :       Tuple  ,							__A  :       Tuple  ,							__A  :       Dict=None  ,							__A  :       str=False			)  -> str:







     """simple docstring"""


     UpperCAmelCase_		=				{}
     UpperCAmelCase_		=				list(checkpoint.keys()			)

     UpperCAmelCase_		=				'''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'''			):
                    UpperCAmelCase_		=				'''model_ema.''' + ''''''.join(key.split('''.'''			)[1:]			)
                    UpperCAmelCase_		=				checkpoint.pop(__A			)
     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:
               UpperCAmelCase_		=				checkpoint.pop(__A			)

     UpperCAmelCase_		=				{}

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

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

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

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

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

          UpperCAmelCase_		=				[
              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
          ]
          UpperCAmelCase_		=				[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
          UpperCAmelCase_		=				[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:
               UpperCAmelCase_		=				unet_state_dict.pop(
                   F"""input_blocks.{i}.op.weight"""			)
               UpperCAmelCase_		=				unet_state_dict.pop(
                   F"""input_blocks.{i}.op.bias"""			)

          UpperCAmelCase_		=				renew_resnet_paths(__A			)
          UpperCAmelCase_		=				{'''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			)

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

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

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

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

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

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

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

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

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

          if len(__A			) > 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_		=				[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

               UpperCAmelCase_		=				renew_resnet_paths(__A			)
               UpperCAmelCase_		=				renew_resnet_paths(__A			)

               UpperCAmelCase_		=				{'''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			)

               UpperCAmelCase_		=				[key for key in resnets if '''temopral_conv''' in key]
               UpperCAmelCase_		=				renew_temp_conv_paths(__A			)
               UpperCAmelCase_		=				{
                   '''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			)

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

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

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

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

     return new_checkpoint


if __name__ == "__main__":
    snake_case_					:					Optional[Any]       						=			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_					:					List[Any]       						=			parser.parse_args()

    snake_case_					:					Optional[int]       						=			torch.load(args.checkpoint_path, map_location="cpu")
    snake_case_					:					Optional[Any]       						=			UNetaDConditionModel()

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

    snake_case_					:					Tuple       						=			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
    snake_case_					:					Dict       						=			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 math


def   A		(__A  :       int			)  -> float:







     """simple docstring"""


     UpperCAmelCase_		=				math.log(2  ,							10			) * number
     UpperCAmelCase_		=				round((log_number - int(__A			))  ,							15			)

     return difference


def   A		(__A  :       int = 678910			)  -> int:







     """simple docstring"""


     UpperCAmelCase_		=				90
     UpperCAmelCase_		=				0

     UpperCAmelCase_		=				math.log(1.23  ,							10			)
     UpperCAmelCase_		=				math.log(1.24  ,							10			)
     UpperCAmelCase_		=				0

     while position < number:
          UpperCAmelCase_		=				log_difference(__A			)

          if difference >= upper_limit:
               power_iterator += 93

          elif difference < lower_limit:
               power_iterator += 196

          else:
               UpperCAmelCase_		=				power_iterator
               power_iterator += 196
               position += 1

     return previous_power


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(f"{solution() = }")






