code: 
  
  
  
  
  
  '''simple docstring'''
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      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 		UpperCamelCase       (							_lowerCamelCase      :      Tuple		, _lowerCamelCase      :      List[Any]=0 ):
      A__       =    []
      for old_item in old_list:
          A__       =    old_item
          A__       =    new_item.replace("block."		, "resnets." )
          A__       =    new_item.replace("conv_shorcut"		, "conv1" )
          A__       =    new_item.replace("in_shortcut"		, "conv_shortcut" )
          A__       =    new_item.replace("temb_proj"		, "time_emb_proj" )
  
          A__       =    shave_segments(_UpperCAmelCase		, n_shave_prefix_segments=_UpperCAmelCase )
  
          mapping.append({"old": old_item, "new": new_item} )
  
      return mapping
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      int=0		, _lowerCamelCase      :      Any=False ):
      A__       =    []
      for old_item in old_list:
          A__       =    old_item
  
          # In `model.mid`, the layer is called `attn`.
          if not in_mid:
              A__       =    new_item.replace("attn"		, "attentions" )
          A__       =    new_item.replace(".k."		, ".key." )
          A__       =    new_item.replace(".v."		, ".value." )
          A__       =    new_item.replace(".q."		, ".query." )
  
          A__       =    new_item.replace("proj_out"		, "proj_attn" )
          A__       =    new_item.replace("norm"		, "group_norm" )
  
          A__       =    shave_segments(_UpperCAmelCase		, n_shave_prefix_segments=_UpperCAmelCase )
          mapping.append({"old": old_item, "new": new_item} )
  
      return mapping
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      Any		, _lowerCamelCase      :      Dict		, _lowerCamelCase      :      Dict=None		, _lowerCamelCase      :      Any=None		, _lowerCamelCase      :      Optional[Any]=None ):
      assert isinstance(_UpperCAmelCase		, _UpperCAmelCase ), "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():
              A__       =    old_checkpoint[path]
              A__       =    old_tensor.shape[0] // 3
  
              A__       =    (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
  
              A__       =    old_tensor.shape[0] // config.get("num_head_channels"		, 1 ) // 3
  
              A__       =    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
              A__,			A__,			A__       =    old_tensor.split(channels // num_heads		, dim=1 )
  
              A__       =    query.reshape(_UpperCAmelCase ).squeeze()
              A__       =    key.reshape(_UpperCAmelCase ).squeeze()
              A__       =    value.reshape(_UpperCAmelCase ).squeeze()
  
      for path in paths:
          A__       =    path["new"]
  
          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
              continue
  
          A__       =    new_path.replace("down."		, "down_blocks." )
          A__       =    new_path.replace("up."		, "up_blocks." )
  
          if additional_replacements is not None:
              for replacement in additional_replacements:
                  A__       =    new_path.replace(replacement["old"]		, replacement["new"] )
  
          if "attentions" in new_path:
              A__       =    old_checkpoint[path["old"]].squeeze()
          else:
              A__       =    old_checkpoint[path["old"]]
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      List[Any] ):
      A__       =    {}
  
      A__       =    checkpoint["temb.dense.0.weight"]
      A__       =    checkpoint["temb.dense.0.bias"]
      A__       =    checkpoint["temb.dense.1.weight"]
      A__       =    checkpoint["temb.dense.1.bias"]
  
      A__       =    checkpoint["norm_out.weight"]
      A__       =    checkpoint["norm_out.bias"]
  
      A__       =    checkpoint["conv_in.weight"]
      A__       =    checkpoint["conv_in.bias"]
      A__       =    checkpoint["conv_out.weight"]
      A__       =    checkpoint["conv_out.bias"]
  
      A__       =    len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "down" in layer} )
      A__       =    {
          layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(_UpperCAmelCase )
      }
  
      A__       =    len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "up" in layer} )
      A__       =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(_UpperCAmelCase )}
  
      for i in range(_UpperCAmelCase ):
          A__       =    (i - 1) // (config["layers_per_block"] + 1)
  
          if any("downsample" in layer for layer in down_blocks[i] ):
              A__       =    checkpoint[
                  F"down.{i}.downsample.op.weight"
              ]
              A__       =    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] ):
              A__       =    len(
                  {".".join(shave_segments(_UpperCAmelCase		, 2 ).split("." )[:2] ) for layer in down_blocks[i] if "block" in layer} )
              A__       =    {
                  layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
                  for layer_id in range(_UpperCAmelCase )
              }
  
              if num_blocks > 0:
                  for j in range(config["layers_per_block"] ):
                      A__       =    renew_resnet_paths(blocks[j] )
                      assign_to_checkpoint(_UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase )
  
          if any("attn" in layer for layer in down_blocks[i] ):
              A__       =    len(
                  {".".join(shave_segments(_UpperCAmelCase		, 2 ).split("." )[:2] ) for layer in down_blocks[i] if "attn" in layer} )
              A__       =    {
                  layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
                  for layer_id in range(_UpperCAmelCase )
              }
  
              if num_attn > 0:
                  for j in range(config["layers_per_block"] ):
                      A__       =    renew_attention_paths(attns[j] )
                      assign_to_checkpoint(_UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, config=_UpperCAmelCase )
  
      A__       =    [key for key in checkpoint if "mid.block_1" in key]
      A__       =    [key for key in checkpoint if "mid.block_2" in key]
      A__       =    [key for key in checkpoint if "mid.attn_1" in key]
  
      # Mid new 2
      A__       =    renew_resnet_paths(_UpperCAmelCase )
      assign_to_checkpoint(
          _UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]		, )
  
      A__       =    renew_resnet_paths(_UpperCAmelCase )
      assign_to_checkpoint(
          _UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]		, )
  
      A__       =    renew_attention_paths(_UpperCAmelCase		, in_mid=_UpperCAmelCase )
      assign_to_checkpoint(
          _UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]		, )
  
      for i in range(_UpperCAmelCase ):
          A__       =    num_up_blocks - 1 - i
  
          if any("upsample" in layer for layer in up_blocks[i] ):
              A__       =    checkpoint[
                  F"up.{i}.upsample.conv.weight"
              ]
              A__       =    checkpoint[F"up.{i}.upsample.conv.bias"]
  
          if any("block" in layer for layer in up_blocks[i] ):
              A__       =    len(
                  {".".join(shave_segments(_UpperCAmelCase		, 2 ).split("." )[:2] ) for layer in up_blocks[i] if "block" in layer} )
              A__       =    {
                  layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(_UpperCAmelCase )
              }
  
              if num_blocks > 0:
                  for j in range(config["layers_per_block"] + 1 ):
                      A__       =    {"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
                      A__       =    renew_resnet_paths(blocks[j] )
                      assign_to_checkpoint(_UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, additional_replacements=[replace_indices] )
  
          if any("attn" in layer for layer in up_blocks[i] ):
              A__       =    len(
                  {".".join(shave_segments(_UpperCAmelCase		, 2 ).split("." )[:2] ) for layer in up_blocks[i] if "attn" in layer} )
              A__       =    {
                  layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(_UpperCAmelCase )
              }
  
              if num_attn > 0:
                  for j in range(config["layers_per_block"] + 1 ):
                      A__       =    {"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
                      A__       =    renew_attention_paths(attns[j] )
                      assign_to_checkpoint(_UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, additional_replacements=[replace_indices] )
  
      A__       =    {k.replace("mid_new_2"		, "mid_block" ): v for k, v in new_checkpoint.items()}
      return new_checkpoint
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      str ):
      A__       =    {}
  
      A__       =    checkpoint["encoder.norm_out.weight"]
      A__       =    checkpoint["encoder.norm_out.bias"]
  
      A__       =    checkpoint["encoder.conv_in.weight"]
      A__       =    checkpoint["encoder.conv_in.bias"]
      A__       =    checkpoint["encoder.conv_out.weight"]
      A__       =    checkpoint["encoder.conv_out.bias"]
  
      A__       =    checkpoint["decoder.norm_out.weight"]
      A__       =    checkpoint["decoder.norm_out.bias"]
  
      A__       =    checkpoint["decoder.conv_in.weight"]
      A__       =    checkpoint["decoder.conv_in.bias"]
      A__       =    checkpoint["decoder.conv_out.weight"]
      A__       =    checkpoint["decoder.conv_out.bias"]
  
      A__       =    len({".".join(layer.split("." )[:3] ) for layer in checkpoint if "down" in layer} )
      A__       =    {
          layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(_UpperCAmelCase )
      }
  
      A__       =    len({".".join(layer.split("." )[:3] ) for layer in checkpoint if "up" in layer} )
      A__       =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(_UpperCAmelCase )}
  
      for i in range(_UpperCAmelCase ):
          A__       =    (i - 1) // (config["layers_per_block"] + 1)
  
          if any("downsample" in layer for layer in down_blocks[i] ):
              A__       =    checkpoint[
                  F"encoder.down.{i}.downsample.conv.weight"
              ]
              A__       =    checkpoint[
                  F"encoder.down.{i}.downsample.conv.bias"
              ]
  
          if any("block" in layer for layer in down_blocks[i] ):
              A__       =    len(
                  {".".join(shave_segments(_UpperCAmelCase		, 3 ).split("." )[:3] ) for layer in down_blocks[i] if "block" in layer} )
              A__       =    {
                  layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
                  for layer_id in range(_UpperCAmelCase )
              }
  
              if num_blocks > 0:
                  for j in range(config["layers_per_block"] ):
                      A__       =    renew_resnet_paths(blocks[j] )
                      assign_to_checkpoint(_UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase )
  
          if any("attn" in layer for layer in down_blocks[i] ):
              A__       =    len(
                  {".".join(shave_segments(_UpperCAmelCase		, 3 ).split("." )[:3] ) for layer in down_blocks[i] if "attn" in layer} )
              A__       =    {
                  layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
                  for layer_id in range(_UpperCAmelCase )
              }
  
              if num_attn > 0:
                  for j in range(config["layers_per_block"] ):
                      A__       =    renew_attention_paths(attns[j] )
                      assign_to_checkpoint(_UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, config=_UpperCAmelCase )
  
      A__       =    [key for key in checkpoint if "mid.block_1" in key]
      A__       =    [key for key in checkpoint if "mid.block_2" in key]
      A__       =    [key for key in checkpoint if "mid.attn_1" in key]
  
      # Mid new 2
      A__       =    renew_resnet_paths(_UpperCAmelCase )
      assign_to_checkpoint(
          _UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]		, )
  
      A__       =    renew_resnet_paths(_UpperCAmelCase )
      assign_to_checkpoint(
          _UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]		, )
  
      A__       =    renew_attention_paths(_UpperCAmelCase		, in_mid=_UpperCAmelCase )
      assign_to_checkpoint(
          _UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]		, )
  
      for i in range(_UpperCAmelCase ):
          A__       =    num_up_blocks - 1 - i
  
          if any("upsample" in layer for layer in up_blocks[i] ):
              A__       =    checkpoint[
                  F"decoder.up.{i}.upsample.conv.weight"
              ]
              A__       =    checkpoint[
                  F"decoder.up.{i}.upsample.conv.bias"
              ]
  
          if any("block" in layer for layer in up_blocks[i] ):
              A__       =    len(
                  {".".join(shave_segments(_UpperCAmelCase		, 3 ).split("." )[:3] ) for layer in up_blocks[i] if "block" in layer} )
              A__       =    {
                  layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(_UpperCAmelCase )
              }
  
              if num_blocks > 0:
                  for j in range(config["layers_per_block"] + 1 ):
                      A__       =    {"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
                      A__       =    renew_resnet_paths(blocks[j] )
                      assign_to_checkpoint(_UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, additional_replacements=[replace_indices] )
  
          if any("attn" in layer for layer in up_blocks[i] ):
              A__       =    len(
                  {".".join(shave_segments(_UpperCAmelCase		, 3 ).split("." )[:3] ) for layer in up_blocks[i] if "attn" in layer} )
              A__       =    {
                  layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(_UpperCAmelCase )
              }
  
              if num_attn > 0:
                  for j in range(config["layers_per_block"] + 1 ):
                      A__       =    {"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
                      A__       =    renew_attention_paths(attns[j] )
                      assign_to_checkpoint(_UpperCAmelCase		, _UpperCAmelCase		, _UpperCAmelCase		, additional_replacements=[replace_indices] )
  
      A__       =    {k.replace("mid_new_2"		, "mid_block" ): v for k, v in new_checkpoint.items()}
      A__       =    checkpoint["quant_conv.weight"]
      A__       =    checkpoint["quant_conv.bias"]
      if "quantize.embedding.weight" in checkpoint:
          A__       =    checkpoint["quantize.embedding.weight"]
      A__       =    checkpoint["post_quant_conv.weight"]
      A__       =    checkpoint["post_quant_conv.bias"]
  
      return new_checkpoint
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			int				=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.")
  
    __lowerCAmelCase							:			Union[str, Any]				=parser.parse_args()
    __lowerCAmelCase							:			List[Any]				=torch.load(args.checkpoint_path)
  
    with open(args.config_file) as f:
      __lowerCAmelCase							:			str				=json.loads(f.read())
  
    # unet case
    __lowerCAmelCase							:			Optional[Any]				={key.split(".")[0] for key in checkpoint.keys()}
    if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
      __lowerCAmelCase							:			List[str]				=convert_vq_autoenc_checkpoint(checkpoint, config)
    else:
      __lowerCAmelCase							:			List[str]				=convert_ddpm_checkpoint(checkpoint, config)
  
    if "ddpm" in config:
      del config["ddpm"]
  
    if config["_class_name"] == "VQModel":
      __lowerCAmelCase							:			Any				=VQModel(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
    elif config["_class_name"] == "AutoencoderKL":
      __lowerCAmelCase							:			Tuple				=AutoencoderKL(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
    else:
      __lowerCAmelCase							:			List[Any]				=UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)
  
      __lowerCAmelCase							:			Any				=DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
  
      __lowerCAmelCase							:			str				=DDPMPipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      Optional[Any] ):
      A__       =    copy.deepcopy(_lowerCamelCase )
      A__       =    sorted(_lowerCamelCase )
      A__       =    [bisect.bisect_right(_lowerCamelCase		, _lowerCamelCase ) for y in x]
      return quantized
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      Tuple=0 ):
      A__       =    np.arange(start=3		, stop=_lowerCamelCase		, step=4 ).tolist() if k > 0 else [10]
      A__       =    _quantize(_lowerCamelCase		, _lowerCamelCase )
      # count number of elements per group
      A__       =    np.unique(_lowerCamelCase		, return_counts=_lowerCamelCase )[1]
      A__       =    [0] + bins + [np.inf]
      logger.info("Using {} as bins for aspect lengths quantization".format(_lowerCamelCase ) )
      logger.info("Count of instances per bin: {}".format(_lowerCamelCase ) )
      return groups
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Optional[Any]		, lowercase_   :List[Any]		, lowercase_   :Optional[int]		, lowercase_   :str							)->							Dict:
       if not isinstance(lowercase_		, lowercase_							):
           raise ValueError(
               "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(lowercase_							)							)
       A__       =    sampler
       A__       =    group_ids
       A__       =    batch_size
   def __iter__(       self   :int							)->							Tuple:
       A__       =    defaultdict(lowercase_							)
       A__       =    defaultdict(lowercase_							)
  
       A__       =    0
       for idx in self.sampler:
           A__       =    self.group_ids[idx]
           buffer_per_group[group_id].append(lowercase_							)
           samples_per_group[group_id].append(lowercase_							)
           if len(buffer_per_group[group_id]							) == self.batch_size:
               yield buffer_per_group[group_id]  # TODO
               num_batches += 1
               del buffer_per_group[group_id]
           assert len(buffer_per_group[group_id]							) < self.batch_size
  
       # now we have run out of elements that satisfy
       # the group criteria, let's return the remaining
       # elements so that the size of the sampler is
       # deterministic
       A__       =    len(self							)
       A__       =    expected_num_batches - num_batches
       if num_remaining > 0:
           # for the remaining batches, group the batches by similar lengths
           A__       =    []
           for group_id, idxs in sorted(buffer_per_group.items()		, key=lambda lowercase_							: x[0]							):
               batch_idx.extend(lowercase_							)
               if len(lowercase_							) >= self.batch_size:
                   yield batch_idx[: self.batch_size]
                   A__       =    batch_idx[self.batch_size :]
                   num_remaining -= 1
           if len(lowercase_							) > 0:
               yield batch_idx
               num_remaining -= 1
       assert num_remaining == 0
  
  
  
   def __len__(       self   :int							)->							Tuple:
       return (len(self.sampler							) + self.batch_size - 1) // self.batch_size
  
  
  
  