
'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[int]:




    """simple docstring"""
    _SCREAMING_SNAKE_CASE       	=			UNetaDModel(**SCREAMING_SNAKE_CASE_  )
    _SCREAMING_SNAKE_CASE       	=			checkpoint["""all_modules.0.W"""].data
    _SCREAMING_SNAKE_CASE       	=			checkpoint["""all_modules.0.W"""].data
    _SCREAMING_SNAKE_CASE       	=			checkpoint["""all_modules.1.weight"""].data
    _SCREAMING_SNAKE_CASE       	=			checkpoint["""all_modules.1.bias"""].data

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

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

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

    _SCREAMING_SNAKE_CASE       	=			4

    def set_attention_weights(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  ):
        _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.NIN_0.W"].data.T
        _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.NIN_1.W"].data.T
        _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.NIN_2.W"].data.T

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

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

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

    def set_resnet_weights(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  ):
        _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.Conv_0.weight"].data
        _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.Conv_0.bias"].data
        _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
        _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

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

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

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

        if hasattr(SCREAMING_SNAKE_CASE_      , """downsamplers"""  ) and block.downsamplers is not None:
            set_resnet_weights(block.resnet_down      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
            module_index += 1
            _SCREAMING_SNAKE_CASE       	=			checkpoint[F"all_modules.{module_index}.Conv_0.weight"].data
            _SCREAMING_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]      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
    module_index += 1
    set_attention_weights(new_model_architecture.mid_block.attentions[0]      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
    module_index += 1
    set_resnet_weights(new_model_architecture.mid_block.resnets[1]      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
    module_index += 1

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

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

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

    return new_model_architecture.state_dict()


if __name__ == "__main__":
     UpperCamelCase__ :						List[str]    							=					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__ :						Any    							=					parser.parse_args()

     UpperCamelCase__ :						Tuple    							=					torch.load(args.checkpoint_path, map_location="cpu")

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

     UpperCamelCase__ :						Union[str, Any]    							=					convert_ncsnpp_checkpoint(
         checkpoint,
         config,
     )

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

     UpperCamelCase__ :						Dict    							=					UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          UpperCamelCase__ :						Any    							=					ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

          UpperCamelCase__ :						List[Any]    							=					ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)








'''simple docstring'''



#
# Sort large text files in a minimum amount of memory
#
import argparse
import os

class 				_a			:





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					'block_{0}.dat'






       def __init__(							self    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			filename
          _SCREAMING_SNAKE_CASE       	=			[]






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.BLOCK_FILENAME_FORMAT.format(A__ )
          with open(A__    ,							"""w""" ) as file:
             file.write(A__ )
          self.block_filenames.append(A__ )






       def     UpperCamelCase			(							self )      ->     Tuple:
          return self.block_filenames






       def     UpperCamelCase			(							self    ,							A__    ,							A__=None )      ->     Any:
          _SCREAMING_SNAKE_CASE       	=			0
          with open(self.filename ) as file:
             while True:
                _SCREAMING_SNAKE_CASE       	=			file.readlines(A__ )

                if lines == []:
                   break

                if sort_key is None:
                   lines.sort()
                else:
                   lines.sort(key=A__ )

                self.write_block("""""".join(A__ )    ,							A__ )
                i += 1






       def     UpperCamelCase			(							self )      ->     List[Any]:
          map(os.remove    ,							self.block_filenames )






class 				_a			:





       """simple docstring"""




       def     UpperCamelCase			(							self    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			-1
          _SCREAMING_SNAKE_CASE       	=			None

          for i in range(len(A__ ) ):
             if min_str is None or choices[i] < min_str:
                _SCREAMING_SNAKE_CASE       	=			i

          return min_index






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			files
          _SCREAMING_SNAKE_CASE       	=			set()
          _SCREAMING_SNAKE_CASE       	=			len(A__ )
          _SCREAMING_SNAKE_CASE       	=			{i: None for i in range(self.num_buffers )}






       def     UpperCamelCase			(							self )      ->     Dict:
          return {
              i: self.buffers[i] for i in range(self.num_buffers ) if i not in self.empty
          }






       def     UpperCamelCase			(							self )      ->     Tuple:
          for i in range(self.num_buffers ):
             if self.buffers[i] is None and i not in self.empty:
                _SCREAMING_SNAKE_CASE       	=			self.files[i].readline()

                if self.buffers[i] == "":
                   self.empty.add(A__ )
                   self.files[i].close()

          if len(self.empty ) == self.num_buffers:
             return False

          return True






       def     UpperCamelCase			(							self    ,							A__ )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.buffers[index]
          _SCREAMING_SNAKE_CASE       	=			None

          return value






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			merge_strategy






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			FilesArray(self.get_file_handles(A__    ,							A__ ) )
          with open(A__    ,							"""w"""    ,							A__ ) as outfile:
             while buffers.refresh():
                _SCREAMING_SNAKE_CASE       	=			self.merge_strategy.select(buffers.get_dict() )
                outfile.write(buffers.unshift(A__ ) )






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			{}

          for i in range(len(A__ ) ):
             _SCREAMING_SNAKE_CASE       	=			open(filenames[i]    ,							"""r"""    ,							A__ )  # noqa: UP015

          return files






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			block_size






       def     UpperCamelCase			(							self    ,							A__    ,							A__=None )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.get_number_blocks(A__    ,							self.block_size )
          _SCREAMING_SNAKE_CASE       	=			FileSplitter(A__ )
          splitter.split(self.block_size    ,							A__ )

          _SCREAMING_SNAKE_CASE       	=			FileMerger(NWayMerge() )
          _SCREAMING_SNAKE_CASE       	=			self.block_size / (num_blocks + 1)
          merger.merge(splitter.get_block_filenames()    ,							filename + """.out"""    ,							A__ )

          splitter.cleanup()






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     Any:
          return (os.stat(A__ ).st_size / block_size) + 1






def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       str:




   """simple docstring"""
   if string[-1].lower() == "k":
      return int(string[:-1]  ) * 10_24
   elif string[-1].lower() == "m":
      return int(string[:-1]  ) * 10_24 * 10_24
   elif string[-1].lower() == "g":
      return int(string[:-1]  ) * 10_24 * 10_24 * 10_24
   else:
      return int(SCREAMING_SNAKE_CASE_  )
def 				lowerCAmelCase_		(			)       ->       List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			argparse.ArgumentParser()
   parser.add_argument(
       """-m"""      , """--mem"""      , help="""amount of memory to use for sorting"""      , default="""100M"""  )
   parser.add_argument(
       """filename"""      , metavar="""<filename>"""      , nargs=1      , help="""name of file to sort"""  )
   _SCREAMING_SNAKE_CASE       	=			parser.parse_args()

   _SCREAMING_SNAKE_CASE       	=			ExternalSort(parse_memory(args.mem  )  )
   sorter.sort(args.filename[0]  )


if __name__ == "__main__":
     main()






