




'''simple docstring'''





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def 			__a			(     _UpperCamelCase:					List[Any]      ,		_UpperCamelCase:					Optional[Any]					)		->				Dict:

     """simple docstring"""




     _snake_case							=   UNetaDModel(**_UpperCamelCase					)
     _snake_case							=   checkpoint["all_modules.0.W"].data
     _snake_case							=   checkpoint["all_modules.0.W"].data
     _snake_case							=   checkpoint["all_modules.1.weight"].data
     _snake_case							=   checkpoint["all_modules.1.bias"].data

     _snake_case							=   checkpoint["all_modules.2.weight"].data
     _snake_case							=   checkpoint["all_modules.2.bias"].data

     _snake_case							=   checkpoint["all_modules.3.weight"].data
     _snake_case							=   checkpoint["all_modules.3.bias"].data

     _snake_case							=   checkpoint[list(checkpoint.keys()					)[-4]].data
     _snake_case							=   checkpoint[list(checkpoint.keys()					)[-3]].data
     _snake_case							=   checkpoint[list(checkpoint.keys()					)[-2]].data
     _snake_case							=   checkpoint[list(checkpoint.keys()					)[-1]].data

     _snake_case							=   4

     def set_attention_weights(_UpperCamelCase:					Dict      ,		_UpperCamelCase:					List[Any]      ,		_UpperCamelCase:					Any					):
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

          _snake_case							=   old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

          _snake_case							=   old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

          _snake_case							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

     def set_resnet_weights(_UpperCamelCase:					int      ,		_UpperCamelCase:					int      ,		_UpperCamelCase:					str					):
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

          _snake_case							=   old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
          _snake_case							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

          _snake_case							=   old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
          _snake_case							=   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:
               _snake_case							=   old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
               _snake_case							=   old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

     for i, block in enumerate(new_model_architecture.downsample_blocks					):
          _snake_case							=   hasattr(_UpperCamelCase      ,		"attentions"					)
          for j in range(len(block.resnets					)					):
               set_resnet_weights(block.resnets[j]      ,		_UpperCamelCase      ,		_UpperCamelCase					)
               module_index += 1
               if has_attentions:
                    set_attention_weights(block.attentions[j]      ,		_UpperCamelCase      ,		_UpperCamelCase					)
                    module_index += 1

          if hasattr(_UpperCamelCase      ,		"downsamplers"					) and block.downsamplers is not None:
               set_resnet_weights(block.resnet_down      ,		_UpperCamelCase      ,		_UpperCamelCase					)
               module_index += 1
               _snake_case							=   checkpoint[F"""all_modules.{module_index}.Conv_0.weight"""].data
               _snake_case							=   checkpoint[F"""all_modules.{module_index}.Conv_0.bias"""].data
               module_index += 1

     set_resnet_weights(new_model_architecture.mid_block.resnets[0]      ,		_UpperCamelCase      ,		_UpperCamelCase					)
     module_index += 1
     set_attention_weights(new_model_architecture.mid_block.attentions[0]      ,		_UpperCamelCase      ,		_UpperCamelCase					)
     module_index += 1
     set_resnet_weights(new_model_architecture.mid_block.resnets[1]      ,		_UpperCamelCase      ,		_UpperCamelCase					)
     module_index += 1

     for i, block in enumerate(new_model_architecture.up_blocks					):
          _snake_case							=   hasattr(_UpperCamelCase      ,		"attentions"					)
          for j in range(len(block.resnets					)					):
               set_resnet_weights(block.resnets[j]      ,		_UpperCamelCase      ,		_UpperCamelCase					)
               module_index += 1
          if has_attentions:
               set_attention_weights(
                   block.attentions[0]      ,		_UpperCamelCase      ,		_UpperCamelCase					)  # why can there only be a single attention layer for up?
               module_index += 1

          if hasattr(_UpperCamelCase      ,		"resnet_up"					) and block.resnet_up is not None:
               _snake_case							=   checkpoint[F"""all_modules.{module_index}.weight"""].data
               _snake_case							=   checkpoint[F"""all_modules.{module_index}.bias"""].data
               module_index += 1
               _snake_case							=   checkpoint[F"""all_modules.{module_index}.weight"""].data
               _snake_case							=   checkpoint[F"""all_modules.{module_index}.bias"""].data
               module_index += 1
               set_resnet_weights(block.resnet_up      ,		_UpperCamelCase      ,		_UpperCamelCase					)
               module_index += 1

     _snake_case							=   checkpoint[F"""all_modules.{module_index}.weight"""].data
     _snake_case							=   checkpoint[F"""all_modules.{module_index}.bias"""].data
     module_index += 1
     _snake_case							=   checkpoint[F"""all_modules.{module_index}.weight"""].data
     _snake_case							=   checkpoint[F"""all_modules.{module_index}.bias"""].data

     return new_model_architecture.state_dict()


if __name__ == "__main__":
   UpperCamelCase_		:    Dict									=		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_		:    Tuple									=		parser.parse_args()

   UpperCamelCase_		:    Optional[Any]									=		torch.load(args.checkpoint_path, map_location='''cpu''')

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

   UpperCamelCase_		:    Optional[int]									=		convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )

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

   UpperCamelCase_		:    List[Any]									=		UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

   try:
      UpperCamelCase_		:    int									=		ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

      UpperCamelCase_		:    Tuple									=		ScoreSdeVePipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
      model.save_pretrained(args.dump_path)








'''simple docstring'''





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase_		:    List[str]									=		500000

UpperCamelCase_			,			UpperCamelCase_		:    Dict									=		os.path.split(__file__)
UpperCamelCase_		:    int									=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.select(range(0      ,		len(_UpperCamelCase					)      ,		2					)					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.sort("numbers"					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.shuffle()





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				Any:

     """simple docstring"""




     _snake_case							=   dataset.train_test_split(0.1					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset      ,		_UpperCamelCase:					int=10					)		->				List[str]:

     """simple docstring"""




     for shard_id in range(_UpperCamelCase					):
          _snake_case							=   dataset.shard(_UpperCamelCase      ,		_UpperCamelCase					)





def 			__a			(     )		->				int:

     """simple docstring"""




     _snake_case							=   {"num examples": SPEED_TEST_N_EXAMPLES}
     _snake_case							=   (select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print("generating dataset"					)
          _snake_case							=   datasets.Features({"text": datasets.Value("string"					), "numbers": datasets.Value("float32"					)}					)
          _snake_case							=   generate_example_dataset(
              os.path.join(_UpperCamelCase      ,		"dataset.arrow"					)      ,		_UpperCamelCase      ,		num_examples=_UpperCamelCase					)
          print("Functions"					)
          for func in functions:
               print(func.__name__					)
               _snake_case							=   func(_UpperCamelCase					)

     with open(_UpperCamelCase      ,		"wb"					) as f:
          f.write(json.dumps(_UpperCamelCase					).encode("utf-8"					)					)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()


