"""simple docstring"""


import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       str=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   __UpperCAmelCase     (	_snake_case		:       Tuple,				_snake_case		:       Optional[Any]=0       ):
   _lowercase							     =       []
   for old_item in old_list:
      _lowercase							     =       old_item
      _lowercase							     =       new_item.replace("block.",				"resnets."       )
      _lowercase							     =       new_item.replace("conv_shorcut",				"conv1"       )
      _lowercase							     =       new_item.replace("in_shortcut",				"conv_shortcut"       )
      _lowercase							     =       new_item.replace("temb_proj",				"time_emb_proj"       )

      _lowercase							     =       shave_segments(_snake_case,				n_shave_prefix_segments=_snake_case       )

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

   return mapping


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       Any=0,				_snake_case		:       Tuple=False       ):
   _lowercase							     =       []
   for old_item in old_list:
      _lowercase							     =       old_item

      # In `model.mid`, the layer is called `attn`.
      if not in_mid:
         _lowercase							     =       new_item.replace("attn",				"attentions"       )
      _lowercase							     =       new_item.replace(".k.",				".key."       )
      _lowercase							     =       new_item.replace(".v.",				".value."       )
      _lowercase							     =       new_item.replace(".q.",				".query."       )

      _lowercase							     =       new_item.replace("proj_out",				"proj_attn"       )
      _lowercase							     =       new_item.replace("norm",				"group_norm"       )

      _lowercase							     =       shave_segments(_snake_case,				n_shave_prefix_segments=_snake_case       )
      mapping.append({"old": old_item, "new": new_item}       )

   return mapping


def   __UpperCAmelCase     (	_snake_case		:       Tuple,				_snake_case		:       int,				_snake_case		:       List[Any],				_snake_case		:       Tuple=None,				_snake_case		:       Optional[int]=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."

   if attention_paths_to_split is not None:
      if config is None:
         raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."       )

      for path, path_map in attention_paths_to_split.items():
         _lowercase							     =       old_checkpoint[path]
         _lowercase							     =       old_tensor.shape[0] // 3

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

         _lowercase							     =       old_tensor.shape[0] // config.get("num_head_channels",				1       ) // 3

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

         _lowercase							     =       query.reshape(_snake_case       ).squeeze()
         _lowercase							     =       key.reshape(_snake_case       ).squeeze()
         _lowercase							     =       value.reshape(_snake_case       ).squeeze()

   for path in paths:
      _lowercase							     =       path["new"]

      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      _lowercase							     =       new_path.replace("down.",				"down_blocks."       )
      _lowercase							     =       new_path.replace("up.",				"up_blocks."       )

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

      if "attentions" in new_path:
         _lowercase							     =       old_checkpoint[path["old"]].squeeze()
      else:
         _lowercase							     =       old_checkpoint[path["old"]]


def   __UpperCAmelCase     (	_snake_case		:       Union[str, Any],				_snake_case		:       str       ):
   _lowercase							     =       {}

   _lowercase							     =       checkpoint["temb.dense.0.weight"]
   _lowercase							     =       checkpoint["temb.dense.0.bias"]
   _lowercase							     =       checkpoint["temb.dense.1.weight"]
   _lowercase							     =       checkpoint["temb.dense.1.bias"]

   _lowercase							     =       checkpoint["norm_out.weight"]
   _lowercase							     =       checkpoint["norm_out.bias"]

   _lowercase							     =       checkpoint["conv_in.weight"]
   _lowercase							     =       checkpoint["conv_in.bias"]
   _lowercase							     =       checkpoint["conv_out.weight"]
   _lowercase							     =       checkpoint["conv_out.bias"]

   _lowercase							     =       len({".".join(layer.split("."       )[:2]       ) for layer in checkpoint if "down" in layer}       )
   _lowercase							     =       {
       layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(_snake_case       )
   }

   _lowercase							     =       len({".".join(layer.split("."       )[:2]       ) for layer in checkpoint if "up" in layer}       )
   _lowercase							     =       {layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(_snake_case       )}

   for i in range(_snake_case       ):
      _lowercase							     =       (i - 1) // (config["layers_per_block"] + 1)

      if any("downsample" in layer for layer in down_blocks[i]       ):
         _lowercase							     =       checkpoint[
             f"""down.{i}.downsample.op.weight"""
         ]
         _lowercase							     =       checkpoint[f"""down.{i}.downsample.op.bias"""]
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

      if any("block" in layer for layer in down_blocks[i]       ):
         _lowercase							     =       len(
             {".".join(shave_segments(_snake_case,				2       ).split("."       )[:2]       ) for layer in down_blocks[i] if "block" in layer}       )
         _lowercase							     =       {
             layer_id: [key for key in down_blocks[i] if f"""block.{layer_id}""" in key]
             for layer_id in range(_snake_case       )
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"]       ):
               _lowercase							     =       renew_resnet_paths(blocks[j]       )
               assign_to_checkpoint(_snake_case,				_snake_case,				_snake_case       )

      if any("attn" in layer for layer in down_blocks[i]       ):
         _lowercase							     =       len(
             {".".join(shave_segments(_snake_case,				2       ).split("."       )[:2]       ) for layer in down_blocks[i] if "attn" in layer}       )
         _lowercase							     =       {
             layer_id: [key for key in down_blocks[i] if f"""attn.{layer_id}""" in key]
             for layer_id in range(_snake_case       )
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"]       ):
               _lowercase							     =       renew_attention_paths(attns[j]       )
               assign_to_checkpoint(_snake_case,				_snake_case,				_snake_case,				config=_snake_case       )

   _lowercase							     =       [key for key in checkpoint if "mid.block_1" in key]
   _lowercase							     =       [key for key in checkpoint if "mid.block_2" in key]
   _lowercase							     =       [key for key in checkpoint if "mid.attn_1" in key]

   # Mid new 2
   _lowercase							     =       renew_resnet_paths(_snake_case       )
   assign_to_checkpoint(
       _snake_case,				_snake_case,				_snake_case,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}],				)

   _lowercase							     =       renew_resnet_paths(_snake_case       )
   assign_to_checkpoint(
       _snake_case,				_snake_case,				_snake_case,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}],				)

   _lowercase							     =       renew_attention_paths(_snake_case,				in_mid=_snake_case       )
   assign_to_checkpoint(
       _snake_case,				_snake_case,				_snake_case,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}],				)

   for i in range(_snake_case       ):
      _lowercase							     =       num_up_blocks - 1 - i

      if any("upsample" in layer for layer in up_blocks[i]       ):
         _lowercase							     =       checkpoint[
             f"""up.{i}.upsample.conv.weight"""
         ]
         _lowercase							     =       checkpoint[f"""up.{i}.upsample.conv.bias"""]

      if any("block" in layer for layer in up_blocks[i]       ):
         _lowercase							     =       len(
             {".".join(shave_segments(_snake_case,				2       ).split("."       )[:2]       ) for layer in up_blocks[i] if "block" in layer}       )
         _lowercase							     =       {
             layer_id: [key for key in up_blocks[i] if f"""block.{layer_id}""" in key] for layer_id in range(_snake_case       )
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"] + 1       ):
               _lowercase							     =       {"old": f"""up_blocks.{i}""", "new": f"""up_blocks.{block_id}"""}
               _lowercase							     =       renew_resnet_paths(blocks[j]       )
               assign_to_checkpoint(_snake_case,				_snake_case,				_snake_case,				additional_replacements=[replace_indices]       )

      if any("attn" in layer for layer in up_blocks[i]       ):
         _lowercase							     =       len(
             {".".join(shave_segments(_snake_case,				2       ).split("."       )[:2]       ) for layer in up_blocks[i] if "attn" in layer}       )
         _lowercase							     =       {
             layer_id: [key for key in up_blocks[i] if f"""attn.{layer_id}""" in key] for layer_id in range(_snake_case       )
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"] + 1       ):
               _lowercase							     =       {"old": f"""up_blocks.{i}""", "new": f"""up_blocks.{block_id}"""}
               _lowercase							     =       renew_attention_paths(attns[j]       )
               assign_to_checkpoint(_snake_case,				_snake_case,				_snake_case,				additional_replacements=[replace_indices]       )

   _lowercase							     =       {k.replace("mid_new_2",				"mid_block"       ): v for k, v in new_checkpoint.items()}
   return new_checkpoint


def   __UpperCAmelCase     (	_snake_case		:       Dict,				_snake_case		:       Optional[int]       ):
   _lowercase							     =       {}

   _lowercase							     =       checkpoint["encoder.norm_out.weight"]
   _lowercase							     =       checkpoint["encoder.norm_out.bias"]

   _lowercase							     =       checkpoint["encoder.conv_in.weight"]
   _lowercase							     =       checkpoint["encoder.conv_in.bias"]
   _lowercase							     =       checkpoint["encoder.conv_out.weight"]
   _lowercase							     =       checkpoint["encoder.conv_out.bias"]

   _lowercase							     =       checkpoint["decoder.norm_out.weight"]
   _lowercase							     =       checkpoint["decoder.norm_out.bias"]

   _lowercase							     =       checkpoint["decoder.conv_in.weight"]
   _lowercase							     =       checkpoint["decoder.conv_in.bias"]
   _lowercase							     =       checkpoint["decoder.conv_out.weight"]
   _lowercase							     =       checkpoint["decoder.conv_out.bias"]

   _lowercase							     =       len({".".join(layer.split("."       )[:3]       ) for layer in checkpoint if "down" in layer}       )
   _lowercase							     =       {
       layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(_snake_case       )
   }

   _lowercase							     =       len({".".join(layer.split("."       )[:3]       ) for layer in checkpoint if "up" in layer}       )
   _lowercase							     =       {layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(_snake_case       )}

   for i in range(_snake_case       ):
      _lowercase							     =       (i - 1) // (config["layers_per_block"] + 1)

      if any("downsample" in layer for layer in down_blocks[i]       ):
         _lowercase							     =       checkpoint[
             f"""encoder.down.{i}.downsample.conv.weight"""
         ]
         _lowercase							     =       checkpoint[
             f"""encoder.down.{i}.downsample.conv.bias"""
         ]

      if any("block" in layer for layer in down_blocks[i]       ):
         _lowercase							     =       len(
             {".".join(shave_segments(_snake_case,				3       ).split("."       )[:3]       ) for layer in down_blocks[i] if "block" in layer}       )
         _lowercase							     =       {
             layer_id: [key for key in down_blocks[i] if f"""block.{layer_id}""" in key]
             for layer_id in range(_snake_case       )
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"]       ):
               _lowercase							     =       renew_resnet_paths(blocks[j]       )
               assign_to_checkpoint(_snake_case,				_snake_case,				_snake_case       )

      if any("attn" in layer for layer in down_blocks[i]       ):
         _lowercase							     =       len(
             {".".join(shave_segments(_snake_case,				3       ).split("."       )[:3]       ) for layer in down_blocks[i] if "attn" in layer}       )
         _lowercase							     =       {
             layer_id: [key for key in down_blocks[i] if f"""attn.{layer_id}""" in key]
             for layer_id in range(_snake_case       )
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"]       ):
               _lowercase							     =       renew_attention_paths(attns[j]       )
               assign_to_checkpoint(_snake_case,				_snake_case,				_snake_case,				config=_snake_case       )

   _lowercase							     =       [key for key in checkpoint if "mid.block_1" in key]
   _lowercase							     =       [key for key in checkpoint if "mid.block_2" in key]
   _lowercase							     =       [key for key in checkpoint if "mid.attn_1" in key]

   # Mid new 2
   _lowercase							     =       renew_resnet_paths(_snake_case       )
   assign_to_checkpoint(
       _snake_case,				_snake_case,				_snake_case,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}],				)

   _lowercase							     =       renew_resnet_paths(_snake_case       )
   assign_to_checkpoint(
       _snake_case,				_snake_case,				_snake_case,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}],				)

   _lowercase							     =       renew_attention_paths(_snake_case,				in_mid=_snake_case       )
   assign_to_checkpoint(
       _snake_case,				_snake_case,				_snake_case,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}],				)

   for i in range(_snake_case       ):
      _lowercase							     =       num_up_blocks - 1 - i

      if any("upsample" in layer for layer in up_blocks[i]       ):
         _lowercase							     =       checkpoint[
             f"""decoder.up.{i}.upsample.conv.weight"""
         ]
         _lowercase							     =       checkpoint[
             f"""decoder.up.{i}.upsample.conv.bias"""
         ]

      if any("block" in layer for layer in up_blocks[i]       ):
         _lowercase							     =       len(
             {".".join(shave_segments(_snake_case,				3       ).split("."       )[:3]       ) for layer in up_blocks[i] if "block" in layer}       )
         _lowercase							     =       {
             layer_id: [key for key in up_blocks[i] if f"""block.{layer_id}""" in key] for layer_id in range(_snake_case       )
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"] + 1       ):
               _lowercase							     =       {"old": f"""up_blocks.{i}""", "new": f"""up_blocks.{block_id}"""}
               _lowercase							     =       renew_resnet_paths(blocks[j]       )
               assign_to_checkpoint(_snake_case,				_snake_case,				_snake_case,				additional_replacements=[replace_indices]       )

      if any("attn" in layer for layer in up_blocks[i]       ):
         _lowercase							     =       len(
             {".".join(shave_segments(_snake_case,				3       ).split("."       )[:3]       ) for layer in up_blocks[i] if "attn" in layer}       )
         _lowercase							     =       {
             layer_id: [key for key in up_blocks[i] if f"""attn.{layer_id}""" in key] for layer_id in range(_snake_case       )
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"] + 1       ):
               _lowercase							     =       {"old": f"""up_blocks.{i}""", "new": f"""up_blocks.{block_id}"""}
               _lowercase							     =       renew_attention_paths(attns[j]       )
               assign_to_checkpoint(_snake_case,				_snake_case,				_snake_case,				additional_replacements=[replace_indices]       )

   _lowercase							     =       {k.replace("mid_new_2",				"mid_block"       ): v for k, v in new_checkpoint.items()}
   _lowercase							     =       checkpoint["quant_conv.weight"]
   _lowercase							     =       checkpoint["quant_conv.bias"]
   if "quantize.embedding.weight" in checkpoint:
      _lowercase							     =       checkpoint["quantize.embedding.weight"]
   _lowercase							     =       checkpoint["post_quant_conv.weight"]
   _lowercase							     =       checkpoint["post_quant_conv.bias"]

   return new_checkpoint


if __name__ == "__main__":
  __UpperCamelCase       :    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(
      "--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.")

  __UpperCamelCase       :    Any         =		parser.parse_args()
  __UpperCamelCase       :    List[Any]         =		torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    __UpperCamelCase       :    List[Any]         =		json.loads(f.read())

  # unet case
  __UpperCamelCase       :    Union[str, Any]         =		{key.split(".")[0] for key in checkpoint.keys()}
  if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    __UpperCamelCase       :    Tuple         =		convert_vq_autoenc_checkpoint(checkpoint, config)
  else:
    __UpperCamelCase       :    Tuple         =		convert_ddpm_checkpoint(checkpoint, config)

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

  if config["_class_name"] == "VQModel":
    __UpperCamelCase       :    List[Any]         =		VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  elif config["_class_name"] == "AutoencoderKL":
    __UpperCamelCase       :    List[str]         =		AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  else:
    __UpperCamelCase       :    List[str]         =		UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    __UpperCamelCase       :    Dict         =		DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

    __UpperCamelCase       :    str         =		DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
"""simple docstring"""


from typing import Literal

__UpperCamelCase       :    int         =		"ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str,				_snake_case		:       Literal["encrypt", "decrypt"]       ):
   _lowercase							     =       LETTERS if mode == "decrypt" else key
   _lowercase							     =       key if mode == "decrypt" else LETTERS
   _lowercase							     =       ""
   # loop through each symbol in the message
   for symbol in message:
      if symbol.upper() in chars_a:
         # encrypt/decrypt the symbol
         _lowercase							     =       chars_a.find(symbol.upper()       )
         if symbol.isupper():
            translated += chars_b[sym_index].upper()
         else:
            translated += chars_b[sym_index].lower()
      else:
         # symbol is not in LETTERS, just add it
         translated += symbol
   return translated


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"encrypt"       )


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"decrypt"       )


def   __UpperCAmelCase     (	):
   _lowercase							     =       "Hello World"
   _lowercase							     =       "QWERTYUIOPASDFGHJKLZXCVBNM"
   _lowercase							     =       "decrypt"  # set to 'encrypt' or 'decrypt'

   if mode == "encrypt":
      _lowercase							     =       encrypt_message(_snake_case,				_snake_case       )
   elif mode == "decrypt":
      _lowercase							     =       decrypt_message(_snake_case,				_snake_case       )
   print(f"""Using the key {key}, the {mode}ed message is: {translated}"""       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()