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)




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)))



