code: import argparse
  import logging
  import os
  import sys
  from collections import namedtuple
  
  import torch
  from modeling_bertabs import BertAbs, build_predictor
  from torch.utils.data import DataLoader, SequentialSampler
  from tqdm import tqdm
  
  from transformers import BertTokenizer
  
  from .utils_summarization import (
      CNNDMDataset,
      build_mask,
      compute_token_type_ids,
      encode_for_summarization,
      truncate_or_pad,
  )
  
  
  a     :			List[Any]					    =						logging.getLogger(__name__)
  logging.basicConfig(stream=sys.stdout, level=logging.INFO)
  
  
  a     :			int					    =						namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])
  def 		snake_case__  (		lowercase				):
     lowerCAmelCase_:       Any         =  BertTokenizer.from_pretrained("bert-base-uncased"       ,							do_lower_case=lowercase_				)
     lowerCAmelCase_:       Optional[int]         =  BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"				)
     model.to(args.device				)
     model.eval()
  
     lowerCAmelCase_:       int         =  {
         'BOS': tokenizer.vocab['[unused0]'],
         'EOS': tokenizer.vocab['[unused1]'],
         'PAD': tokenizer.vocab['[PAD]'],
     }
  
     if args.compute_rouge:
        lowerCAmelCase_:       int         =  []
        lowerCAmelCase_:       int         =  []
  
        import nltk
        import rouge
  
        nltk.download("punkt"				)
        lowerCAmelCase_:       Tuple         =  rouge.Rouge(
            metrics=["rouge-n", "rouge-l"]       ,							max_n=2       ,							limit_length=lowercase_       ,							length_limit=args.beam_size       ,							length_limit_type="words"       ,							apply_avg=lowercase_       ,							apply_best=lowercase_       ,							alpha=0.5       ,							weight_factor=1.2       ,							stemming=lowercase_       ,							)
  
     # these (unused) arguments are defined to keep the compatibility
     # with the legacy code and will be deleted in a next iteration.
     lowerCAmelCase_:       List[Any]         =  ''
     lowerCAmelCase_:       Tuple         =  ''
  
     lowerCAmelCase_:       int         =  build_data_iterator(lowercase_       ,							lowercase_				)
     lowerCAmelCase_:       Union[str, Any]         =  build_predictor(lowercase_       ,							lowercase_       ,							lowercase_       ,							lowercase_				)
  
     logger.info("***** Running evaluation *****"				)
     logger.info("  Number examples = %d"       ,							len(data_iterator.dataset				)				)
     logger.info("  Batch size = %d"       ,							args.batch_size				)
     logger.info(""				)
     logger.info("***** Beam Search parameters *****"				)
     logger.info("  Beam size = %d"       ,							args.beam_size				)
     logger.info("  Minimum length = %d"       ,							args.min_length				)
     logger.info("  Maximum length = %d"       ,							args.max_length				)
     logger.info("  Alpha (length penalty) = %.2f"       ,							args.alpha				)
     logger.info("  Trigrams %s be blocked"       ,							("will" if args.block_trigram else "will NOT")				)
  
     for batch in tqdm(lowercase_				):
        lowerCAmelCase_:       Optional[int]         =  predictor.translate_batch(lowercase_				)
        lowerCAmelCase_:       Any         =  predictor.from_batch(lowercase_				)
        lowerCAmelCase_:       Union[str, Any]         =  [format_summary(lowercase_				) for t in translations]
        save_summaries(lowercase_       ,							args.summaries_output_dir       ,							batch.document_names				)
  
        if args.compute_rouge:
           reference_summaries += batch.tgt_str
           generated_summaries += summaries
  
     if args.compute_rouge:
        lowerCAmelCase_:       List[str]         =  rouge_evaluator.get_scores(lowercase_       ,							lowercase_				)
        lowerCAmelCase_:       int         =  format_rouge_scores(lowercase_				)
        save_rouge_scores(lowercase_				)
        print(lowercase_				)
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase				):
     for summary, document_name in zip(lowercase_       ,							lowercase_				):
        # Prepare the summary file's name
        if "." in document_name:
           lowerCAmelCase_:       int         =  '.'.join(document_name.split("."				)[:-1]				)
           lowerCAmelCase_:       List[Any]         =  document_name.split("."				)[-1]
           lowerCAmelCase_:       Optional[int]         =  bare_document_name + '_summary.' + extension
        else:
           lowerCAmelCase_:       List[str]         =  document_name + '_summary'
  
        lowerCAmelCase_:       Union[str, Any]         =  os.path.join(lowercase_       ,							lowercase_				)
        with open(lowercase_       ,							"w"				) as output:
           output.write(lowercase_				)
  def 		snake_case__  (		lowercase				):
     lowerCAmelCase_:       List[str]         =  translation
     lowerCAmelCase_:       Dict         =  (
         raw_summary.replace("[unused0]"       ,							""				)
         .replace("[unused3]"       ,							""				)
         .replace("[PAD]"       ,							""				)
         .replace("[unused1]"       ,							""				)
         .replace(r" +"       ,							" "				)
         .replace(" [unused2] "       ,							". "				)
         .replace("[unused2]"       ,							""				)
         .strip()
     )
  
     return summary
  def 		snake_case__  (		lowercase				):
     return """\n
  ****** ROUGE SCORES ******
  
  ** ROUGE 1
  F1        >> {:.3f}
  Precision >> {:.3f}
  Recall    >> {:.3f}
  
  ** ROUGE 2
  F1        >> {:.3f}
  Precision >> {:.3f}
  Recall    >> {:.3f}
  
  ** ROUGE L
  F1        >> {:.3f}
  Precision >> {:.3f}
  Recall    >> {:.3f}""".format(
         scores["rouge-1"]["f"]       ,							scores["rouge-1"]["p"]       ,							scores["rouge-1"]["r"]       ,							scores["rouge-2"]["f"]       ,							scores["rouge-2"]["p"]       ,							scores["rouge-2"]["r"]       ,							scores["rouge-l"]["f"]       ,							scores["rouge-l"]["p"]       ,							scores["rouge-l"]["r"]       ,							)
  def 		snake_case__  (		lowercase				):
     with open("rouge_scores.txt"       ,							"w"				) as output:
        output.write(lowercase_				)
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Optional[Any]         =  load_and_cache_examples(lowercase_       ,							lowercase_				)
     lowerCAmelCase_:       Dict         =  SequentialSampler(lowercase_				)
  
     def collate_fn(lowercase				):
        return collate(lowercase_       ,							lowercase_       ,							block_size=512       ,							device=args.device				)
  
     lowerCAmelCase_:       Optional[int]         =  DataLoader(
         lowercase_       ,							sampler=lowercase_       ,							batch_size=args.batch_size       ,							collate_fn=lowercase_       ,							)
  
     return iterator
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Tuple         =  CNNDMDataset(args.documents_dir				)
     return dataset
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase       ,							lowercase				):
     lowerCAmelCase_:       Dict         =  [x for x in data if not len(x[1]				) == 0]  # remove empty_files
     lowerCAmelCase_:       str         =  [name for name, _, _ in data]
     lowerCAmelCase_:       Optional[Any]         =  [' '.join(lowercase_				) for _, _, summary_list in data]
  
     lowerCAmelCase_:       Any         =  [encode_for_summarization(lowercase_       ,							lowercase_       ,							lowercase_				) for _, story, summary in data]
     lowerCAmelCase_:       int         =  torch.tensor(
         [truncate_or_pad(lowercase_       ,							lowercase_       ,							tokenizer.pad_token_id				) for story, _ in encoded_text]				)
     lowerCAmelCase_:       Tuple         =  compute_token_type_ids(lowercase_       ,							tokenizer.cls_token_id				)
     lowerCAmelCase_:       List[str]         =  build_mask(lowercase_       ,							tokenizer.pad_token_id				)
  
     lowerCAmelCase_:       List[Any]         =  Batch(
         document_names=lowercase_       ,							batch_size=len(lowercase_				)       ,							src=encoded_stories.to(lowercase_				)       ,							segs=encoder_token_type_ids.to(lowercase_				)       ,							mask_src=encoder_mask.to(lowercase_				)       ,							tgt_str=lowercase_       ,							)
  
     return batch
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Dict         =  summary_tokens.to("cpu"				).numpy()
     lowerCAmelCase_:       int         =  tokenizer.decode(lowercase_				)
     lowerCAmelCase_:       str         =  summary.split("."				)
     lowerCAmelCase_:       List[str]         =  [s + '.' for s in sentences]
     return sentences
  def 		snake_case__  (		):
     lowerCAmelCase_:       Any         =  argparse.ArgumentParser()
     parser.add_argument(
         "--documents_dir"       ,							default=lowercase_       ,							type=lowercase_       ,							required=lowercase_       ,							help="The folder where the documents to summarize are located."       ,							)
     parser.add_argument(
         "--summaries_output_dir"       ,							default=lowercase_       ,							type=lowercase_       ,							required=lowercase_       ,							help="The folder in wich the summaries should be written. Defaults to the folder where the documents are"       ,							)
     parser.add_argument(
         "--compute_rouge"       ,							default=lowercase_       ,							type=lowercase_       ,							required=lowercase_       ,							help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."       ,							)
     # EVALUATION options
     parser.add_argument(
         "--no_cuda"       ,							default=lowercase_       ,							type=lowercase_       ,							help="Whether to force the execution on CPU."       ,							)
     parser.add_argument(
         "--batch_size"       ,							default=4       ,							type=lowercase_       ,							help="Batch size per GPU/CPU for training."       ,							)
     # BEAM SEARCH arguments
     parser.add_argument(
         "--min_length"       ,							default=50       ,							type=lowercase_       ,							help="Minimum number of tokens for the summaries."       ,							)
     parser.add_argument(
         "--max_length"       ,							default=200       ,							type=lowercase_       ,							help="Maixmum number of tokens for the summaries."       ,							)
     parser.add_argument(
         "--beam_size"       ,							default=5       ,							type=lowercase_       ,							help="The number of beams to start with for each example."       ,							)
     parser.add_argument(
         "--alpha"       ,							default=0.95       ,							type=lowercase_       ,							help="The value of alpha for the length penalty in the beam search."       ,							)
     parser.add_argument(
         "--block_trigram"       ,							default=lowercase_       ,							type=lowercase_       ,							help="Whether to block the existence of repeating trigrams in the text generated by beam search."       ,							)
     lowerCAmelCase_:       Dict         =  parser.parse_args()
  
     # Select device (distibuted not available)
     lowerCAmelCase_:       Optional[Any]         =  torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"				)
  
     # Check the existence of directories
     if not args.summaries_output_dir:
        lowerCAmelCase_:       Dict         =  args.documents_dir
  
     if not documents_dir_is_valid(args.documents_dir				):
        raise FileNotFoundError(
            "We could not find the directory you specified for the documents to summarize, or it was empty. Please"
            " specify a valid path."				)
     os.makedirs(args.summaries_output_dir       ,							exist_ok=lowercase_				)
  
     evaluate(lowercase_				)
  def 		snake_case__  (		lowercase				):
     if not os.path.exists(lowercase_				):
        return False
  
     lowerCAmelCase_:       str         =  os.listdir(lowercase_				)
     if len(lowercase_				) == 0:
        return False
  
     return True
  
  
  if __name__ == "__main__":
   main()
style_context: import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  def 		snake_case__  (		lowercase       ,							lowercase=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__  (		lowercase       ,							lowercase=0				):
     lowerCAmelCase_:       List[Any]         =  []
     for old_item in old_list:
        lowerCAmelCase_:       Any         =  old_item
        lowerCAmelCase_:       Dict         =  new_item.replace("block."       ,							"resnets."				)
        lowerCAmelCase_:       Tuple         =  new_item.replace("conv_shorcut"       ,							"conv1"				)
        lowerCAmelCase_:       Optional[int]         =  new_item.replace("in_shortcut"       ,							"conv_shortcut"				)
        lowerCAmelCase_:       Any         =  new_item.replace("temb_proj"       ,							"time_emb_proj"				)
  
        lowerCAmelCase_:       Tuple         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)
  
        mapping.append({"old": old_item, "new": new_item}				)
  
     return mapping
  def 		snake_case__  (		lowercase       ,							lowercase=0       ,							lowercase=False				):
     lowerCAmelCase_:       Any         =  []
     for old_item in old_list:
        lowerCAmelCase_:       List[Any]         =  old_item
  
        # In `model.mid`, the layer is called `attn`.
        if not in_mid:
           lowerCAmelCase_:       str         =  new_item.replace("attn"       ,							"attentions"				)
        lowerCAmelCase_:       str         =  new_item.replace(".k."       ,							".key."				)
        lowerCAmelCase_:       Tuple         =  new_item.replace(".v."       ,							".value."				)
        lowerCAmelCase_:       Any         =  new_item.replace(".q."       ,							".query."				)
  
        lowerCAmelCase_:       List[Any]         =  new_item.replace("proj_out"       ,							"proj_attn"				)
        lowerCAmelCase_:       List[str]         =  new_item.replace("norm"       ,							"group_norm"				)
  
        lowerCAmelCase_:       Dict         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)
        mapping.append({"old": old_item, "new": new_item}				)
  
     return mapping
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase       ,							lowercase=None       ,							lowercase=None       ,							lowercase=None				):
     assert isinstance(lowercase       ,							lowercase				), "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():
           lowerCAmelCase_:       List[Any]         =  old_checkpoint[path]
           lowerCAmelCase_:       Optional[Any]         =  old_tensor.shape[0] // 3
  
           lowerCAmelCase_:       Any         =  (-1, channels) if len(old_tensor.shape				) == 3 else (-1)
  
           lowerCAmelCase_:       Optional[int]         =  old_tensor.shape[0] // config.get("num_head_channels"       ,							1				) // 3
  
           lowerCAmelCase_:       Optional[int]         =  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
           lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Tuple         =  old_tensor.split(channels // num_heads       ,							dim=1				)
  
           lowerCAmelCase_:       List[str]         =  query.reshape(lowercase				).squeeze()
           lowerCAmelCase_:       str         =  key.reshape(lowercase				).squeeze()
           lowerCAmelCase_:       Tuple         =  value.reshape(lowercase				).squeeze()
  
     for path in paths:
        lowerCAmelCase_:       str         =  path["new"]
  
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
           continue
  
        lowerCAmelCase_:       str         =  new_path.replace("down."       ,							"down_blocks."				)
        lowerCAmelCase_:       Tuple         =  new_path.replace("up."       ,							"up_blocks."				)
  
        if additional_replacements is not None:
           for replacement in additional_replacements:
              lowerCAmelCase_:       Union[str, Any]         =  new_path.replace(replacement["old"]       ,							replacement["new"]				)
  
        if "attentions" in new_path:
           lowerCAmelCase_:       List[Any]         =  old_checkpoint[path["old"]].squeeze()
        else:
           lowerCAmelCase_:       Union[str, Any]         =  old_checkpoint[path["old"]]
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Any         =  {}
  
     lowerCAmelCase_:       List[str]         =  checkpoint["temb.dense.0.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["temb.dense.0.bias"]
     lowerCAmelCase_:       Any         =  checkpoint["temb.dense.1.weight"]
     lowerCAmelCase_:       Optional[Any]         =  checkpoint["temb.dense.1.bias"]
  
     lowerCAmelCase_:       Optional[Any]         =  checkpoint["norm_out.weight"]
     lowerCAmelCase_:       List[str]         =  checkpoint["norm_out.bias"]
  
     lowerCAmelCase_:       int         =  checkpoint["conv_in.weight"]
     lowerCAmelCase_:       int         =  checkpoint["conv_in.bias"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["conv_out.weight"]
     lowerCAmelCase_:       Dict         =  checkpoint["conv_out.bias"]
  
     lowerCAmelCase_:       Dict         =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "down" in layer}				)
     lowerCAmelCase_:       Tuple         =  {
         layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase				)
     }
  
     lowerCAmelCase_:       str         =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "up" in layer}				)
     lowerCAmelCase_:       Optional[int]         =  {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowercase				)}
  
     for i in range(lowercase				):
        lowerCAmelCase_:       List[str]         =  (i - 1) // (config["layers_per_block"] + 1)
  
        if any("downsample" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Any         =  checkpoint[
               F'''down.{i}.downsample.op.weight'''
           ]
           lowerCAmelCase_:       List[Any]         =  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]				):
           lowerCAmelCase_:       Optional[Any]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in down_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       str         =  {
               layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
               for layer_id in range(lowercase				)
           }
  
           if num_blocks > 0:
              for j in range(config["layers_per_block"]				):
                 lowerCAmelCase_:       str         =  renew_resnet_paths(blocks[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase				)
  
        if any("attn" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       List[Any]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in down_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       Optional[Any]         =  {
               layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
               for layer_id in range(lowercase				)
           }
  
           if num_attn > 0:
              for j in range(config["layers_per_block"]				):
                 lowerCAmelCase_:       List[Any]         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase				)
  
     lowerCAmelCase_:       List[str]         =  [key for key in checkpoint if "mid.block_1" in key]
     lowerCAmelCase_:       List[Any]         =  [key for key in checkpoint if "mid.block_2" in key]
     lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.attn_1" in key]
  
     # Mid new 2
     lowerCAmelCase_:       Optional[int]         =  renew_resnet_paths(lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]       ,							)
  
     lowerCAmelCase_:       Dict         =  renew_resnet_paths(lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,							)
  
     lowerCAmelCase_:       int         =  renew_attention_paths(lowercase       ,							in_mid=lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]       ,							)
  
     for i in range(lowercase				):
        lowerCAmelCase_:       Any         =  num_up_blocks - 1 - i
  
        if any("upsample" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       str         =  checkpoint[
               F'''up.{i}.upsample.conv.weight'''
           ]
           lowerCAmelCase_:       Union[str, Any]         =  checkpoint[F'''up.{i}.upsample.conv.bias''']
  
        if any("block" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       List[str]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in up_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       List[str]         =  {
               layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowercase				)
           }
  
           if num_blocks > 0:
              for j in range(config["layers_per_block"] + 1				):
                 lowerCAmelCase_:       List[Any]         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       List[Any]         =  renew_resnet_paths(blocks[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)
  
        if any("attn" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       Optional[int]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in up_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       List[str]         =  {
               layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowercase				)
           }
  
           if num_attn > 0:
              for j in range(config["layers_per_block"] + 1				):
                 lowerCAmelCase_:       Optional[int]         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       Dict         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)
  
     lowerCAmelCase_:       Tuple         =  {k.replace("mid_new_2"       ,							"mid_block"				): v for k, v in new_checkpoint.items()}
     return new_checkpoint
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Optional[int]         =  {}
  
     lowerCAmelCase_:       Any         =  checkpoint["encoder.norm_out.weight"]
     lowerCAmelCase_:       Tuple         =  checkpoint["encoder.norm_out.bias"]
  
     lowerCAmelCase_:       Optional[int]         =  checkpoint["encoder.conv_in.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["encoder.conv_in.bias"]
     lowerCAmelCase_:       List[str]         =  checkpoint["encoder.conv_out.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["encoder.conv_out.bias"]
  
     lowerCAmelCase_:       Dict         =  checkpoint["decoder.norm_out.weight"]
     lowerCAmelCase_:       List[str]         =  checkpoint["decoder.norm_out.bias"]
  
     lowerCAmelCase_:       Dict         =  checkpoint["decoder.conv_in.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["decoder.conv_in.bias"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["decoder.conv_out.weight"]
     lowerCAmelCase_:       List[str]         =  checkpoint["decoder.conv_out.bias"]
  
     lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "down" in layer}				)
     lowerCAmelCase_:       Any         =  {
         layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase				)
     }
  
     lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "up" in layer}				)
     lowerCAmelCase_:       Union[str, Any]         =  {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowercase				)}
  
     for i in range(lowercase				):
        lowerCAmelCase_:       Any         =  (i - 1) // (config["layers_per_block"] + 1)
  
        if any("downsample" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Optional[Any]         =  checkpoint[
               F'''encoder.down.{i}.downsample.conv.weight'''
           ]
           lowerCAmelCase_:       Any         =  checkpoint[
               F'''encoder.down.{i}.downsample.conv.bias'''
           ]
  
        if any("block" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Union[str, Any]         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in down_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       Optional[Any]         =  {
               layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
               for layer_id in range(lowercase				)
           }
  
           if num_blocks > 0:
              for j in range(config["layers_per_block"]				):
                 lowerCAmelCase_:       Dict         =  renew_resnet_paths(blocks[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase				)
  
        if any("attn" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Dict         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in down_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       List[str]         =  {
               layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
               for layer_id in range(lowercase				)
           }
  
           if num_attn > 0:
              for j in range(config["layers_per_block"]				):
                 lowerCAmelCase_:       List[str]         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase				)
  
     lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.block_1" in key]
     lowerCAmelCase_:       List[Any]         =  [key for key in checkpoint if "mid.block_2" in key]
     lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.attn_1" in key]
  
     # Mid new 2
     lowerCAmelCase_:       Dict         =  renew_resnet_paths(lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]       ,							)
  
     lowerCAmelCase_:       List[str]         =  renew_resnet_paths(lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,							)
  
     lowerCAmelCase_:       List[Any]         =  renew_attention_paths(lowercase       ,							in_mid=lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]       ,							)
  
     for i in range(lowercase				):
        lowerCAmelCase_:       str         =  num_up_blocks - 1 - i
  
        if any("upsample" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       Optional[Any]         =  checkpoint[
               F'''decoder.up.{i}.upsample.conv.weight'''
           ]
           lowerCAmelCase_:       str         =  checkpoint[
               F'''decoder.up.{i}.upsample.conv.bias'''
           ]
  
        if any("block" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       str         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in up_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       Optional[Any]         =  {
               layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowercase				)
           }
  
           if num_blocks > 0:
              for j in range(config["layers_per_block"] + 1				):
                 lowerCAmelCase_:       Dict         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       str         =  renew_resnet_paths(blocks[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)
  
        if any("attn" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       Dict         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in up_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       Optional[int]         =  {
               layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowercase				)
           }
  
           if num_attn > 0:
              for j in range(config["layers_per_block"] + 1				):
                 lowerCAmelCase_:       Any         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       List[Any]         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)
  
     lowerCAmelCase_:       Optional[Any]         =  {k.replace("mid_new_2"       ,							"mid_block"				): v for k, v in new_checkpoint.items()}
     lowerCAmelCase_:       Optional[int]         =  checkpoint["quant_conv.weight"]
     lowerCAmelCase_:       Optional[Any]         =  checkpoint["quant_conv.bias"]
     if "quantize.embedding.weight" in checkpoint:
        lowerCAmelCase_:       List[Any]         =  checkpoint["quantize.embedding.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["post_quant_conv.weight"]
     lowerCAmelCase_:       Any         =  checkpoint["post_quant_conv.bias"]
  
     return new_checkpoint
  
  
  if __name__ == "__main__":
   a     :			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.""")
  
   a     :			Union[str, Any]					    =						parser.parse_args()
   a     :			List[Any]					    =						torch.load(args.checkpoint_path)
  
   with open(args.config_file) as f:
    a     :			Any					    =						json.loads(f.read())
  
   # unet case
   a     :			List[str]					    =						{key.split(""".""")[0] for key in checkpoint.keys()}
   if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    a     :			Optional[Any]					    =						convert_vq_autoenc_checkpoint(checkpoint, config)
   else:
    a     :			Optional[Any]					    =						convert_ddpm_checkpoint(checkpoint, config)
  
   if "ddpm" in config:
    del config["ddpm"]
  
   if config["_class_name"] == "VQModel":
    a     :			Any					    =						VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
    a     :			Tuple					    =						AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   else:
    a     :			Optional[Any]					    =						UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)
  
    a     :			Dict					    =						DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
  
    a     :			int					    =						DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)