code: 
  
  
  import subprocess
  from argparse import ArgumentParser
  from typing import List, Union
  
  from huggingface_hub.hf_api import HfFolder, create_repo, whoami
  from requests.exceptions import HTTPError
  
  from . import BaseTransformersCLICommand
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       lowerCAmelCase__):
  
  
  
  
       '''simple docstring'''
  
  
       @staticmethod
       def 				_lowerCamelCase				(		__lowerCamelCase)    ->       int:
              _A   : List[str]       					=				parser.add_parser("login"				,		help="Log in using the same credentials as on huggingface.co")
              login_parser.set_defaults(func=lambda __lowerCamelCase: LoginCommand(lowerCAmelCase_))
              _A   : Union[str, Any]       					=				parser.add_parser("whoami"				,		help="Find out which huggingface.co account you are logged in as.")
              whoami_parser.set_defaults(func=lambda __lowerCamelCase: WhoamiCommand(lowerCAmelCase_))
              _A   : Optional[int]       					=				parser.add_parser("logout"				,		help="Log out")
              logout_parser.set_defaults(func=lambda __lowerCamelCase: LogoutCommand(lowerCAmelCase_))
  
              # new system: git-based repo system
              _A   : Optional[int]       					=				parser.add_parser(
                  "repo"				,		help="Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos."				,		)
              _A   : List[Any]       					=				repo_parser.add_subparsers(
                  help="Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands")
              _A   : Any       					=				repo_subparsers.add_parser(
                  "create"				,		help="Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co")
              repo_create_parser.add_argument(
                  "name"				,		type=lowerCAmelCase_				,		help="Name for your model\'s repo. Will be namespaced under your username to build the model id."				,		)
              repo_create_parser.add_argument("--organization"				,		type=lowerCAmelCase_				,		help="Optional: organization namespace.")
              repo_create_parser.add_argument("-y"				,		"--yes"				,		action="store_true"				,		help="Optional: answer Yes to the prompt")
              repo_create_parser.set_defaults(func=lambda __lowerCamelCase: RepoCreateCommand(lowerCAmelCase_))
  
  
  
  
  
  
  
  class   lowerCAmelCase__ :
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				"""\u001b[1m"""
       __SCREAMING_SNAKE_CASE						    =				"""\u001b[31m"""
       __SCREAMING_SNAKE_CASE						    =				"""\u001b[90m"""
       __SCREAMING_SNAKE_CASE						    =				"""\u001b[0m"""
  
  
       @classmethod
       def 				_lowerCamelCase				(		cls				,		__lowerCamelCase)    ->       Tuple:
              return F"{cls._bold}{s}{cls._reset}"
  
  
       @classmethod
       def 				_lowerCamelCase				(		cls				,		__lowerCamelCase)    ->       Optional[Any]:
              return F"{cls._bold}{cls._red}{s}{cls._reset}"
  
  
       @classmethod
       def 				_lowerCamelCase				(		cls				,		__lowerCamelCase)    ->       Union[str, Any]:
              return F"{cls._gray}{s}{cls._reset}"
  
  
  
  
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						str		):
         _A   : Union[str, Any]       					=				[max(len(str(UpperCamelCase__		)		) for x in col		) for col in zip(*UpperCamelCase__       ,							UpperCamelCase__		)]
         _A   : Dict       					=				("{{:{}}} " * len(UpperCamelCase__		)).format(*UpperCamelCase__		)
         _A   : Dict       					=				[]
         lines.append(row_format.format(*UpperCamelCase__		)		)
         lines.append(row_format.format(*["-" * w for w in col_widths]		)		)
         for row in rows:
                lines.append(row_format.format(*UpperCamelCase__		)		)
         return "\n".join(UpperCamelCase__		)
  
  
  
  
  
  
  
  class   lowerCAmelCase__ :
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase)    ->       Tuple:
              _A   : Union[str, Any]       					=				args
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       lowerCAmelCase__):
  
  
  
  
       '''simple docstring'''
  
  
       def 				_lowerCamelCase				(		self)    ->       int:
              print(
                  ANSI.red(
                      "ERROR! `huggingface-cli login` uses an outdated login mechanism "
                      "that is not compatible with the Hugging Face Hub backend anymore. "
                      "Please use `huggingface-cli login instead."))
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       lowerCAmelCase__):
  
  
  
  
       '''simple docstring'''
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[Any]:
              print(
                  ANSI.red(
                      "WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use "
                      "`huggingface-cli whoami` instead."))
              _A   : Union[str, Any]       					=				HfFolder.get_token()
              if token is None:
                     print("Not logged in")
                     exit()
              try:
                     _A					,    _A   : List[Any]       					=				whoami(lowerCAmelCase_)
                     print(lowerCAmelCase_)
                     if orgs:
                            print(ANSI.bold("orgs: ")				,		",".join(lowerCAmelCase_))
              except HTTPError as e:
                     print(lowerCAmelCase_)
                     print(ANSI.red(e.response.text))
                     exit(1)
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       lowerCAmelCase__):
  
  
  
  
       '''simple docstring'''
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              print(
                  ANSI.red(
                      "ERROR! `transformers-cli logout` uses an outdated logout mechanism "
                      "that is not compatible with the Hugging Face Hub backend anymore. "
                      "Please use `huggingface-cli logout instead."))
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       lowerCAmelCase__):
  
  
  
  
       '''simple docstring'''
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              print(
                  ANSI.red(
                      "WARNING! Managing repositories through transformers-cli is deprecated. "
                      "Please use `huggingface-cli` instead."))
              _A   : str       					=				HfFolder.get_token()
              if token is None:
                     print("Not logged in")
                     exit(1)
              try:
                     _A   : Optional[Any]       					=				subprocess.check_output(["git", "--version"]).decode("utf-8")
                     print(ANSI.gray(stdout.strip()))
              except FileNotFoundError:
                     print("Looks like you do not have git installed, please install.")
  
              try:
                     _A   : List[Any]       					=				subprocess.check_output(["git-lfs", "--version"]).decode("utf-8")
                     print(ANSI.gray(stdout.strip()))
              except FileNotFoundError:
                     print(
                         ANSI.red(
                             "Looks like you do not have git-lfs installed, please install."
                             " You can install from https://git-lfs.github.com/."
                             " Then run `git lfs install` (you only have to do this once)."))
              print("")
  
              _A					,    _A   : Optional[Any]       					=				whoami(lowerCAmelCase_)
              _A   : int       					=				self.args.organization if self.args.organization is not None else user
              _A   : int       					=				F"{namespace}/{self.args.name}"
              print(F"You are about to create {ANSI.bold(lowerCAmelCase_)}")
  
              if not self.args.yes:
                     _A   : Tuple       					=				input("Proceed? [Y/n] ").lower()
                     if not (choice == "" or choice == "y" or choice == "yes"):
                            print("Abort")
                            exit()
              try:
                     _A   : List[Any]       					=				create_repo(lowerCAmelCase_				,		name=self.args.name				,		organization=self.args.organization)
              except HTTPError as e:
                     print(lowerCAmelCase_)
                     print(ANSI.red(e.response.text))
                     exit(1)
              print("\nYour repo now lives at:")
              print(F"  {ANSI.bold(lowerCAmelCase_)}")
              print("\nYou can clone it locally with the command below, and commit/push as usual.")
              print(F"\n  git clone {url}")
              print("")
  
style_context: 
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Dict		):
         _A   : Dict       					=				UNetaDModel(**UpperCamelCase__		)
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.1.weight"].data
         _A   : Optional[Any]       					=				checkpoint["all_modules.1.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.2.weight"].data
         _A   : List[str]       					=				checkpoint["all_modules.2.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.3.weight"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.3.bias"].data
  
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-4]].data
         _A   : Any       					=				checkpoint[list(checkpoint.keys()		)[-3]].data
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-2]].data
         _A   : Optional[int]       					=				checkpoint[list(checkpoint.keys()		)[-1]].data
  
         _A   : str       					=				4
  
         def set_attention_weights(UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple		):
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
                _A   : List[str]       					=				old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
                _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
                _A   : Dict       					=				old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
         def set_resnet_weights(UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict		):
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
                _A   : str       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
                _A   : Union[str, 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:
                       _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                       _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
         for i, block in enumerate(new_model_architecture.downsample_blocks		):
                _A   : Any       					=				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
                       _A   : List[str]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
                       _A   : 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]       ,							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		):
                _A   : Dict       					=				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:
                       _A   : Dict       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       _A   : int       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       set_resnet_weights(block.resnet_up       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
  
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
         module_index += 1
         _A   : Tuple       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
  
         return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
      lowerCAmelCase__   	=				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.',
      )
  
      lowerCAmelCase__   	=				parser.parse_args()
  
      lowerCAmelCase__   	=				torch.load(args.checkpoint_path, map_location='cpu')
  
      with open(args.config_file) as f:
          lowerCAmelCase__   	=				json.loads(f.read())
  
      lowerCAmelCase__   	=				convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )
  
      if "sde" in config:
          del config["sde"]
  
      lowerCAmelCase__   	=				UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)
  
      try:
          lowerCAmelCase__   	=				ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
          lowerCAmelCase__   	=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
          model.save_pretrained(args.dump_path)
  