code: import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	Optional[int]				):
         _UpperCamelCase     :       int  						=  UNetaDModel(**lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  checkpoint['all_modules.0.W'].data
         _UpperCamelCase     :       List[Any]  						=  checkpoint['all_modules.0.W'].data
         _UpperCamelCase     :       str  						=  checkpoint['all_modules.1.weight'].data
         _UpperCamelCase     :       List[Any]  						=  checkpoint['all_modules.1.bias'].data
  
         _UpperCamelCase     :       str  						=  checkpoint['all_modules.2.weight'].data
         _UpperCamelCase     :       Optional[Any]  						=  checkpoint['all_modules.2.bias'].data
  
         _UpperCamelCase     :       Any  						=  checkpoint['all_modules.3.weight'].data
         _UpperCamelCase     :       Optional[Any]  						=  checkpoint['all_modules.3.bias'].data
  
         _UpperCamelCase     :       Optional[int]  						=  checkpoint[list(checkpoint.keys()				)[-4]].data
         _UpperCamelCase     :       Any  						=  checkpoint[list(checkpoint.keys()				)[-3]].data
         _UpperCamelCase     :       Optional[Any]  						=  checkpoint[list(checkpoint.keys()				)[-2]].data
         _UpperCamelCase     :       List[str]  						=  checkpoint[list(checkpoint.keys()				)[-1]].data
  
         _UpperCamelCase     :       Any  						=  4
  
         def set_attention_weights(lowercase_:	Dict		,						lowercase_:	List[Any]		,						lowercase_:	Optional[Any]				):
                _UpperCamelCase     :       Optional[Any]  						=  old_checkpoint[F"all_modules.{index}.NIN_0.W"].data.T
                _UpperCamelCase     :       Tuple  						=  old_checkpoint[F"all_modules.{index}.NIN_1.W"].data.T
                _UpperCamelCase     :       Union[str, Any]  						=  old_checkpoint[F"all_modules.{index}.NIN_2.W"].data.T
  
                _UpperCamelCase     :       Any  						=  old_checkpoint[F"all_modules.{index}.NIN_0.b"].data
                _UpperCamelCase     :       List[str]  						=  old_checkpoint[F"all_modules.{index}.NIN_1.b"].data
                _UpperCamelCase     :       Any  						=  old_checkpoint[F"all_modules.{index}.NIN_2.b"].data
  
                _UpperCamelCase     :       Optional[Any]  						=  old_checkpoint[F"all_modules.{index}.NIN_3.W"].data.T
                _UpperCamelCase     :       int  						=  old_checkpoint[F"all_modules.{index}.NIN_3.b"].data
  
                _UpperCamelCase     :       int  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
                _UpperCamelCase     :       Tuple  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data
  
         def set_resnet_weights(lowercase_:	Optional[Any]		,						lowercase_:	int		,						lowercase_:	Tuple				):
                _UpperCamelCase     :       List[Any]  						=  old_checkpoint[F"all_modules.{index}.Conv_0.weight"].data
                _UpperCamelCase     :       List[str]  						=  old_checkpoint[F"all_modules.{index}.Conv_0.bias"].data
                _UpperCamelCase     :       Any  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
                _UpperCamelCase     :       Optional[int]  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data
  
                _UpperCamelCase     :       Optional[int]  						=  old_checkpoint[F"all_modules.{index}.Conv_1.weight"].data
                _UpperCamelCase     :       Any  						=  old_checkpoint[F"all_modules.{index}.Conv_1.bias"].data
                _UpperCamelCase     :       Union[str, Any]  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_1.weight"].data
                _UpperCamelCase     :       Union[str, Any]  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_1.bias"].data
  
                _UpperCamelCase     :       Any  						=  old_checkpoint[F"all_modules.{index}.Dense_0.weight"].data
                _UpperCamelCase     :       Optional[Any]  						=  old_checkpoint[F"all_modules.{index}.Dense_0.bias"].data
  
                if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                       _UpperCamelCase     :       int  						=  old_checkpoint[F"all_modules.{index}.Conv_2.weight"].data
                       _UpperCamelCase     :       Dict  						=  old_checkpoint[F"all_modules.{index}.Conv_2.bias"].data
  
         for i, block in enumerate(new_model_architecture.downsample_blocks				):
                _UpperCamelCase     :       List[str]  						=  hasattr(lowercase_		,						'attentions'				)
                for j in range(len(block.resnets				)				):
                       set_resnet_weights(block.resnets[j]		,						lowercase_		,						lowercase_				)
                       module_index += 1
                       if has_attentions:
                              set_attention_weights(block.attentions[j]		,						lowercase_		,						lowercase_				)
                              module_index += 1
  
                if hasattr(lowercase_		,						'downsamplers'				) and block.downsamplers is not None:
                       set_resnet_weights(block.resnet_down		,						lowercase_		,						lowercase_				)
                       module_index += 1
                       _UpperCamelCase     :       str  						=  checkpoint[F"all_modules.{module_index}.Conv_0.weight"].data
                       _UpperCamelCase     :       Optional[int]  						=  checkpoint[F"all_modules.{module_index}.Conv_0.bias"].data
                       module_index += 1
  
         set_resnet_weights(new_model_architecture.mid_block.resnets[0]		,						lowercase_		,						lowercase_				)
         module_index += 1
         set_attention_weights(new_model_architecture.mid_block.attentions[0]		,						lowercase_		,						lowercase_				)
         module_index += 1
         set_resnet_weights(new_model_architecture.mid_block.resnets[1]		,						lowercase_		,						lowercase_				)
         module_index += 1
  
         for i, block in enumerate(new_model_architecture.up_blocks				):
                _UpperCamelCase     :       Optional[Any]  						=  hasattr(lowercase_		,						'attentions'				)
                for j in range(len(block.resnets				)				):
                       set_resnet_weights(block.resnets[j]		,						lowercase_		,						lowercase_				)
                       module_index += 1
                if has_attentions:
                       set_attention_weights(
                           block.attentions[0]		,						lowercase_		,						lowercase_				)  # why can there only be a single attention layer for up?
                       module_index += 1
  
                if hasattr(lowercase_		,						'resnet_up'				) and block.resnet_up is not None:
                       _UpperCamelCase     :       int  						=  checkpoint[F"all_modules.{module_index}.weight"].data
                       _UpperCamelCase     :       Dict  						=  checkpoint[F"all_modules.{module_index}.bias"].data
                       module_index += 1
                       _UpperCamelCase     :       Tuple  						=  checkpoint[F"all_modules.{module_index}.weight"].data
                       _UpperCamelCase     :       str  						=  checkpoint[F"all_modules.{module_index}.bias"].data
                       module_index += 1
                       set_resnet_weights(block.resnet_up		,						lowercase_		,						lowercase_				)
                       module_index += 1
  
         _UpperCamelCase     :       int  						=  checkpoint[F"all_modules.{module_index}.weight"].data
         _UpperCamelCase     :       int  						=  checkpoint[F"all_modules.{module_index}.bias"].data
         module_index += 1
         _UpperCamelCase     :       Tuple  						=  checkpoint[F"all_modules.{module_index}.weight"].data
         _UpperCamelCase     :       List[str]  						=  checkpoint[F"all_modules.{module_index}.bias"].data
  
         return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
   UpperCAmelCase     :   int					=				argparse.ArgumentParser()
  
   parser.add_argument(
       '''--checkpoint_path''',
       default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
       type=str,
       required=False,
       help='''Path to the checkpoint to convert.''',
   )
  
   parser.add_argument(
       '''--config_file''',
       default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
       type=str,
       required=False,
       help='''The config json file corresponding to the architecture.''',
   )
  
   parser.add_argument(
       '''--dump_path''',
       default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
       type=str,
       required=False,
       help='''Path to the output model.''',
   )
  
   UpperCAmelCase     :   Optional[int]					=				parser.parse_args()
  
   UpperCAmelCase     :   Union[str, Any]					=				torch.load(args.checkpoint_path, map_location='''cpu''')
  
   with open(args.config_file) as f:
    UpperCAmelCase     :   Optional[int]					=				json.loads(f.read())
  
   UpperCAmelCase     :   List[Any]					=				convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )
  
   if "sde" in config:
    del config["sde"]
  
   UpperCAmelCase     :   List[str]					=				UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)
  
   try:
    UpperCAmelCase     :   Optional[int]					=				ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  
    UpperCAmelCase     :   Union[str, Any]					=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
    model.save_pretrained(args.dump_path)
  
  
  
  
style_context: import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
  
         def remove_articles(lowercase_:	Dict				):
                return re.sub(R'\b(a|an|the)\b'		,						' '		,						lowercase_				)
  
         def white_space_fix(lowercase_:	Optional[Any]				):
                return " ".join(text.split()				)
  
         def remove_punc(lowercase_:	List[str]				):
                _UpperCamelCase     :       str  						=  set(string.punctuation				)
                return "".join(ch for ch in text if ch not in exclude				)
  
         def lower(lowercase_:	List[str]				):
                return text.lower()
  
         return white_space_fix(remove_articles(remove_punc(lower(lowercase_				)				)				)				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]		,						lowercase_:	Tuple				):
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       Union[str, Any]  						=  Counter(lowercase_				) & Counter(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  sum(common.values()				)
         if num_same == 0:
                return 0
         _UpperCamelCase     :       Optional[Any]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       List[str]  						=  (2 * precision * recall) / (precision + recall)
         return fa
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	Union[str, Any]				):
         return normalize_answer(lowercase_				) == normalize_answer(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	int		,						lowercase_:	Union[str, Any]				):
         _UpperCamelCase     :       Any  						=  []
         for ground_truth in ground_truths:
                _UpperCamelCase     :       Optional[Any]  						=  metric_fn(lowercase_		,						lowercase_				)
                scores_for_ground_truths.append(lowercase_				)
         return max(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]		,						lowercase_:	List[Any]				):
         _UpperCamelCase     :       List[Any]  						=  0
         for article in dataset:
                for paragraph in article["paragraphs"]:
                       for qa in paragraph["qas"]:
                              total += 1
                              if qa["id"] not in predictions:
                                     _UpperCamelCase     :       Union[str, Any]  						=  'Unanswered question ' + qa['id'] + ' will receive score 0.'
                                     print(lowercase_		,						file=sys.stderr				)
                                     continue
                              _UpperCamelCase     :       Dict  						=  [x['text'] for x in qa['answers']]
                              _UpperCamelCase     :       List[str]  						=  predictions[qa['id']]
                              exact_match += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
                              fa += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
  
         _UpperCamelCase     :       Dict  						=  100.0 * exact_match / total
         _UpperCamelCase     :       Optional[Any]  						=  100.0 * fa / total
  
         return {"exact_match": exact_match, "f1": fa}
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				'''1.1'''
   UpperCAmelCase					=				argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
   parser.add_argument('''dataset_file''', help='''Dataset file''')
   parser.add_argument('''prediction_file''', help='''Prediction File''')
   UpperCAmelCase					=				parser.parse_args()
   with open(args.dataset_file) as dataset_file:
    UpperCAmelCase					=				json.load(dataset_file)
    if dataset_json["version"] != expected_version:
     print(
         '''Evaluation expects v-''' + expected_version + ''', but got dataset with v-''' + dataset_json['''version'''],
         file=sys.stderr,
     )
    UpperCAmelCase					=				dataset_json['''data''']
   with open(args.prediction_file) as prediction_file:
    UpperCAmelCase					=				json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))
  
  
  
  