'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 	lowerCAmelCase_				(					snake_case_       : Tuple					,    snake_case_       : List[Any]=1			)    ->      Optional[int]:


     '''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_				(					snake_case_       : Dict					,    snake_case_       : int=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(snake_case_					,    n_shave_prefix_segments=snake_case_			)

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

     return mapping





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]					,    snake_case_       : Optional[int]=0			)    ->      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(snake_case_					,    n_shave_prefix_segments=snake_case_			)

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

     return mapping





def 	lowerCAmelCase_				(					snake_case_       : Any					,    snake_case_       : Any					,    snake_case_       : str					,    snake_case_       : List[Any]=None					,    snake_case_       : Optional[int]=None					,    snake_case_       : str=None			)    ->      List[Any]:


     '''simple docstring'''




     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():
               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(snake_case_			)
               UpperCAmelCase_           =   key.reshape(snake_case_			)
               UpperCAmelCase_           =   value.reshape(snake_case_			)

     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 	lowerCAmelCase_				(					snake_case_       : List[str]					,    snake_case_       : Optional[int]			)    ->      Dict:


     '''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(snake_case_			)
     }

     # 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(snake_case_			)
     }

     # 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(snake_case_			)
     }

     for i in range(1					,    snake_case_			):
          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(snake_case_			)
          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(
              snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path, resnet_op]					,    config=snake_case_			)

          if len(snake_case_			):
               UpperCAmelCase_           =   renew_attention_paths(snake_case_			)
               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(
                   snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    attention_paths_to_split=snake_case_					,    config=snake_case_					,    )

     UpperCAmelCase_           =   middle_blocks[0]
     UpperCAmelCase_           =   middle_blocks[1]
     UpperCAmelCase_           =   middle_blocks[2]

     UpperCAmelCase_           =   renew_resnet_paths(snake_case_			)
     assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    config=snake_case_			)

     UpperCAmelCase_           =   renew_resnet_paths(snake_case_			)
     assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    config=snake_case_			)

     UpperCAmelCase_           =   renew_attention_paths(snake_case_			)
     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(
         snake_case_					,    snake_case_					,    snake_case_					,    attention_paths_to_split=snake_case_					,    config=snake_case_			)

     for i in range(snake_case_			):
          UpperCAmelCase_           =   i // (config["num_res_blocks"] + 1)
          UpperCAmelCase_           =   i % (config["num_res_blocks"] + 1)
          UpperCAmelCase_           =   [shave_segments(snake_case_					,    2			) for name in output_blocks[i]]
          UpperCAmelCase_           =   {}

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

          if len(snake_case_			) > 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(snake_case_			)
               UpperCAmelCase_           =   renew_resnet_paths(snake_case_			)

               UpperCAmelCase_           =   {"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_			)

               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(snake_case_			) == 2:
                         UpperCAmelCase_           =   []

               if len(snake_case_			):
                    UpperCAmelCase_           =   renew_attention_paths(snake_case_			)
                    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(
                        snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    attention_paths_to_split=to_split if any("qkv" in key for key in attentions			) else None					,    config=snake_case_					,    )
          else:
               UpperCAmelCase_           =   renew_resnet_paths(snake_case_					,    n_shave_prefix_segments=1			)
               for path in resnet_0_paths:
                    UpperCAmelCase_           =   ".".join(["output_blocks", str(snake_case_			), path["old"]]			)
                    UpperCAmelCase_           =   ".".join(["up_blocks", str(snake_case_			), "resnets", str(snake_case_			), path["new"]]			)

                    UpperCAmelCase_           =   checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Tuple  =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.')

    SCREAMING_SNAKE_CASE_:     int  =parser.parse_args()

    SCREAMING_SNAKE_CASE_:     List[str]  =torch.load(args.checkpoint_path)

    with open(args.config_file) as f:
        SCREAMING_SNAKE_CASE_:     Dict  =json.loads(f.read())

    SCREAMING_SNAKE_CASE_:     int  =convert_ldm_checkpoint(checkpoint, config)

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

    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    try:
        SCREAMING_SNAKE_CASE_:     Optional[Any]  =DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
        SCREAMING_SNAKE_CASE_:     Optional[Any]  =VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

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





'''simple docstring'''





import os
from math import logaa





def 	lowerCAmelCase_				(					snake_case_       : str = "base_exp.txt"			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   0
     for i, line in enumerate(open(os.path.join(os.path.dirname(snake_case_			)					,    snake_case_			)			)			):
          UpperCAmelCase_    , UpperCAmelCase_           =   list(map(snake_case_					,    line.split(","			)			)			)
          if x * logaa(snake_case_			) > largest:
               UpperCAmelCase_           =   x * logaa(snake_case_			)
               UpperCAmelCase_           =   i + 1
     return result


if __name__ == "__main__":
    print(solution())




