code: 
  
  
  """simple docstring"""
  import tempfile
  
  import torch
  
  from diffusers import (
      DEISMultistepScheduler,
      DPMSolverMultistepScheduler,
      DPMSolverSinglestepScheduler,
      UniPCMultistepScheduler,
  )
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  class   lowercase__							(				A_							):
     __UpperCAmelCase       					=				(DPMSolverMultistepScheduler,)
     __UpperCAmelCase       					=				(('''num_inference_steps''', 25),)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      **SCREAMING_SNAKE_CASE)     ->     Tuple:
        _lowerCamelCase  :				Tuple      =		{
            """num_train_timesteps""": 1000,
            """beta_start""": 0.00_01,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """solver_order""": 2,
            """prediction_type""": """epsilon""",
            """thresholding""": False,
            """sample_max_value""": 1.0,
            """algorithm_type""": """dpmsolver++""",
            """solver_type""": """midpoint""",
            """lower_order_final""": False,
            """lambda_min_clipped""": -float("""inf"""),
            """variance_type""": None,
        }
  
        config.update(**SCREAMING_SNAKE_CASE)
        return config
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE=0			,      **SCREAMING_SNAKE_CASE)     ->     List[str]:
        _lowerCamelCase  :				List[str]      =		dict(self.forward_default_kwargs)
        _lowerCamelCase  :				Optional[Any]      =		kwargs.pop("""num_inference_steps"""			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[str]      =		self.dummy_sample
        _lowerCamelCase  :				str      =		0.1 * sample
        _lowerCamelCase  :				Dict      =		[residual + 0.2, residual + 0.15, residual + 0.10]
  
        for scheduler_class in self.scheduler_classes:
           _lowerCamelCase  :				Union[str, Any]      =		self.get_scheduler_config(**SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				List[str]      =		scheduler_class(**SCREAMING_SNAKE_CASE)
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
           # copy over dummy past residuals
           _lowerCamelCase  :				Optional[int]      =		dummy_past_residuals[: scheduler.config.solver_order]
  
           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(SCREAMING_SNAKE_CASE)
              _lowerCamelCase  :				List[Any]      =		scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE)
              new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
              # copy over dummy past residuals
              _lowerCamelCase  :				Union[str, Any]      =		dummy_past_residuals[: new_scheduler.config.solver_order]
  
           _lowerCamelCase      ,						_lowerCamelCase  :				Dict      =		sample, sample
           for t in range(SCREAMING_SNAKE_CASE			,      time_step + scheduler.config.solver_order + 1):
              _lowerCamelCase  :				Optional[int]      =		scheduler.step(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE).prev_sample
              _lowerCamelCase  :				str      =		new_scheduler.step(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE).prev_sample
  
              assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        pass
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE=0			,      **SCREAMING_SNAKE_CASE)     ->     Dict:
        _lowerCamelCase  :				Union[str, Any]      =		dict(self.forward_default_kwargs)
        _lowerCamelCase  :				Tuple      =		kwargs.pop("""num_inference_steps"""			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Tuple      =		self.dummy_sample
        _lowerCamelCase  :				Optional[int]      =		0.1 * sample
        _lowerCamelCase  :				List[str]      =		[residual + 0.2, residual + 0.15, residual + 0.10]
  
        for scheduler_class in self.scheduler_classes:
           _lowerCamelCase  :				int      =		self.get_scheduler_config()
           _lowerCamelCase  :				List[Any]      =		scheduler_class(**SCREAMING_SNAKE_CASE)
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
  
           # copy over dummy past residuals (must be after setting timesteps)
           _lowerCamelCase  :				Dict      =		dummy_past_residuals[: scheduler.config.solver_order]
  
           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(SCREAMING_SNAKE_CASE)
              _lowerCamelCase  :				Any      =		scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE)
              # copy over dummy past residuals
              new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
  
              # copy over dummy past residual (must be after setting timesteps)
              _lowerCamelCase  :				Dict      =		dummy_past_residuals[: new_scheduler.config.solver_order]
  
           _lowerCamelCase  :				Optional[int]      =		scheduler.step(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE).prev_sample
           _lowerCamelCase  :				Tuple      =		new_scheduler.step(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE).prev_sample
  
           assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE=None			,      **SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        if scheduler is None:
           _lowerCamelCase  :				Any      =		self.scheduler_classes[0]
           _lowerCamelCase  :				Union[str, Any]      =		self.get_scheduler_config(**SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				List[Any]      =		scheduler_class(**SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				int      =		10
        _lowerCamelCase  :				Optional[Any]      =		self.dummy_model()
        _lowerCamelCase  :				Optional[Any]      =		self.dummy_sample_deter
        scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
  
        for i, t in enumerate(scheduler.timesteps):
           _lowerCamelCase  :				Optional[int]      =		model(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Tuple      =		scheduler.step(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE).prev_sample
  
        return sample
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        _lowerCamelCase  :				Dict      =		dict(self.forward_default_kwargs)
  
        _lowerCamelCase  :				Optional[int]      =		kwargs.pop("""num_inference_steps"""			,      SCREAMING_SNAKE_CASE)
  
        for scheduler_class in self.scheduler_classes:
           _lowerCamelCase  :				Union[str, Any]      =		self.get_scheduler_config()
           _lowerCamelCase  :				Optional[int]      =		scheduler_class(**SCREAMING_SNAKE_CASE)
  
           _lowerCamelCase  :				Optional[Any]      =		self.dummy_sample
           _lowerCamelCase  :				Optional[int]      =		0.1 * sample
  
           if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE			,      """set_timesteps"""):
              scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
           elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE			,      """set_timesteps"""):
              _lowerCamelCase  :				Optional[int]      =		num_inference_steps
  
           # copy over dummy past residuals (must be done after set_timesteps)
           _lowerCamelCase  :				List[Any]      =		[residual + 0.2, residual + 0.15, residual + 0.10]
           _lowerCamelCase  :				Any      =		dummy_past_residuals[: scheduler.config.solver_order]
  
           _lowerCamelCase  :				List[Any]      =		scheduler.timesteps[5]
           _lowerCamelCase  :				Dict      =		scheduler.timesteps[6]
  
           _lowerCamelCase  :				List[Any]      =		scheduler.step(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE).prev_sample
           _lowerCamelCase  :				Tuple      =		scheduler.step(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE).prev_sample
  
           self.assertEqual(output_a.shape			,      sample.shape)
           self.assertEqual(output_a.shape			,      output_a.shape)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        for timesteps in [25, 50, 100, 999, 1000]:
           self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE)
        for order in [1, 2, 3]:
           for solver_type in ["midpoint", "heun"]:
              for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(
                        thresholding=SCREAMING_SNAKE_CASE			,      prediction_type=SCREAMING_SNAKE_CASE			,      sample_max_value=SCREAMING_SNAKE_CASE			,      algorithm_type="""dpmsolver++"""			,      solver_order=SCREAMING_SNAKE_CASE			,      solver_type=SCREAMING_SNAKE_CASE			,      )
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
           for solver_type in ["midpoint", "heun"]:
              for order in [1, 2, 3]:
                 for prediction_type in ["epsilon", "sample"]:
                    if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                       if order == 3:
                          continue
                    else:
                       self.check_over_configs(
                           solver_order=SCREAMING_SNAKE_CASE			,      solver_type=SCREAMING_SNAKE_CASE			,      prediction_type=SCREAMING_SNAKE_CASE			,      algorithm_type=SCREAMING_SNAKE_CASE			,      )
                    _lowerCamelCase  :				Any      =		self.full_loop(
                        solver_order=SCREAMING_SNAKE_CASE			,      solver_type=SCREAMING_SNAKE_CASE			,      prediction_type=SCREAMING_SNAKE_CASE			,      algorithm_type=SCREAMING_SNAKE_CASE			,      )
                    assert not torch.isnan(SCREAMING_SNAKE_CASE).any(), "Samples have nan numbers"
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE)
        self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     str:
        self.check_over_configs(lambda_min_clipped=-float("""inf"""))
        self.check_over_configs(lambda_min_clipped=-5.1)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE)
        self.check_over_configs(variance_type="""learned_range""")
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
           self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE			,      time_step=0)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Any:
        _lowerCamelCase  :				int      =		self.full_loop()
        _lowerCamelCase  :				Tuple      =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE))
  
        assert abs(result_mean.item() - 0.33_01) < 1e-3
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				Any      =		self.full_loop(thresholding=SCREAMING_SNAKE_CASE			,      dynamic_thresholding_ratio=0.87			,      sample_max_value=0.5)
        _lowerCamelCase  :				Tuple      =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE))
  
        assert abs(result_mean.item() - 1.13_64) < 1e-3
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Any:
        _lowerCamelCase  :				Optional[int]      =		self.full_loop(prediction_type="""v_prediction""")
        _lowerCamelCase  :				Optional[Any]      =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE))
  
        assert abs(result_mean.item() - 0.22_51) < 1e-3
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase  :				List[Any]      =		self.full_loop(prediction_type="""v_prediction"""			,      use_karras_sigmas=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Union[str, Any]      =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE))
  
        assert abs(result_mean.item() - 0.20_96) < 1e-3
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     str:
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        _lowerCamelCase  :				Any      =		DPMSolverMultistepScheduler(**self.get_scheduler_config())
        _lowerCamelCase  :				int      =		self.full_loop(scheduler=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Tuple      =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE))
  
        assert abs(result_mean.item() - 0.33_01) < 1e-3
  
        _lowerCamelCase  :				Any      =		DPMSolverSinglestepScheduler.from_config(scheduler.config)
        _lowerCamelCase  :				str      =		UniPCMultistepScheduler.from_config(scheduler.config)
        _lowerCamelCase  :				Optional[Any]      =		DEISMultistepScheduler.from_config(scheduler.config)
        _lowerCamelCase  :				List[Any]      =		DPMSolverMultistepScheduler.from_config(scheduler.config)
  
        _lowerCamelCase  :				List[str]      =		self.full_loop(scheduler=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Union[str, Any]      =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE))
  
        assert abs(result_mean.item() - 0.33_01) < 1e-3
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        _lowerCamelCase  :				List[str]      =		self.scheduler_classes[0]
        _lowerCamelCase  :				str      =		self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE			,      dynamic_thresholding_ratio=0)
        _lowerCamelCase  :				str      =		scheduler_class(**SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Union[str, Any]      =		10
        _lowerCamelCase  :				Dict      =		self.dummy_model()
        _lowerCamelCase  :				Union[str, Any]      =		self.dummy_sample_deter.half()
        scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
  
        for i, t in enumerate(scheduler.timesteps):
           _lowerCamelCase  :				str      =		model(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				str      =		scheduler.step(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE).prev_sample
  
        assert sample.dtype == torch.floataa
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      **SCREAMING_SNAKE_CASE)     ->     Optional[Any]:
        for scheduler_class in self.scheduler_classes:
           _lowerCamelCase  :				Optional[Any]      =		self.get_scheduler_config(**SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				int      =		scheduler_class(**SCREAMING_SNAKE_CASE)
  
           scheduler.set_timesteps(scheduler.config.num_train_timesteps)
           assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps
  
  
style_context: 
  
  
  """simple docstring"""
  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   lowercase__							(				A_							):
  
  
  
  
  
     @staticmethod
     def 		UpperCamelCase_     (       SCREAMING_SNAKE_CASE)     ->     Dict:
        _lowerCamelCase  :				Optional[Any]      =		parser.add_parser("""login"""			,      help="""Log in using the same credentials as on huggingface.co""")
        login_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE: LoginCommand(SCREAMING_SNAKE_CASE))
        _lowerCamelCase  :				Any      =		parser.add_parser("""whoami"""			,      help="""Find out which huggingface.co account you are logged in as.""")
        whoami_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE: WhoamiCommand(SCREAMING_SNAKE_CASE))
        _lowerCamelCase  :				Optional[int]      =		parser.add_parser("""logout"""			,      help="""Log out""")
        logout_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE: LogoutCommand(SCREAMING_SNAKE_CASE))
  
        # new system: git-based repo system
        _lowerCamelCase  :				Optional[int]      =		parser.add_parser(
            """repo"""			,      help="""Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos."""			,      )
        _lowerCamelCase  :				Optional[Any]      =		repo_parser.add_subparsers(
            help="""Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands""")
        _lowerCamelCase  :				Dict      =		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=SCREAMING_SNAKE_CASE			,      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=SCREAMING_SNAKE_CASE			,      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 SCREAMING_SNAKE_CASE: RepoCreateCommand(SCREAMING_SNAKE_CASE))
  
  
  
  
  
  
  
  class   lowercase__							:
     __UpperCAmelCase       					=				'''\u001b[1m'''
     __UpperCAmelCase       					=				'''\u001b[31m'''
     __UpperCAmelCase       					=				'''\u001b[90m'''
     __UpperCAmelCase       					=				'''\u001b[0m'''
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE)     ->     Dict:
        return F'{cls._bold}{s}{cls._reset}'
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        return F'{cls._bold}{cls._red}{s}{cls._reset}'
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE)     ->     Tuple:
        return F'{cls._gray}{s}{cls._reset}'
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			List[List[Union[str, int]]]	,   __snake_case       :			List[str]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				str      =		[max(len(str(__snake_case      )      ) for x in col      ) for col in zip(*__snake_case	,   __snake_case      )]
     _lowerCamelCase  :				Any      =		("""{{:{}}} """ * len(__snake_case      )).format(*__snake_case      )
     _lowerCamelCase  :				Optional[Any]      =		[]
     lines.append(row_format.format(*__snake_case      )      )
     lines.append(row_format.format(*["""-""" * w for w in col_widths]      )      )
     for row in rows:
        lines.append(row_format.format(*__snake_case      )      )
     return "\n".join(__snake_case      )
  
  
  
  
  class   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE)     ->     List[str]:
        _lowerCamelCase  :				Any      =		args
  
  
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       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   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        print(
            ANSI.red(
                """WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use """
                """`huggingface-cli whoami` instead."""))
        _lowerCamelCase  :				Tuple      =		HfFolder.get_token()
        if token is None:
           print("""Not logged in""")
           exit()
        try:
           _lowerCamelCase      ,						_lowerCamelCase  :				int      =		whoami(SCREAMING_SNAKE_CASE)
           print(SCREAMING_SNAKE_CASE)
           if orgs:
              print(ANSI.bold("""orgs: """)			,      """,""".join(SCREAMING_SNAKE_CASE))
        except HTTPError as e:
           print(SCREAMING_SNAKE_CASE)
           print(ANSI.red(e.response.text))
           exit(1)
  
  
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       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   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        print(
            ANSI.red(
                """WARNING! Managing repositories through transformers-cli is deprecated. """
                """Please use `huggingface-cli` instead."""))
        _lowerCamelCase  :				Union[str, Any]      =		HfFolder.get_token()
        if token is None:
           print("""Not logged in""")
           exit(1)
        try:
           _lowerCamelCase  :				Union[str, 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:
           _lowerCamelCase  :				List[str]      =		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("""""")
  
        _lowerCamelCase      ,						_lowerCamelCase  :				str      =		whoami(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				str      =		self.args.organization if self.args.organization is not None else user
        _lowerCamelCase  :				Union[str, Any]      =		F'{namespace}/{self.args.name}'
        print(F'You are about to create {ANSI.bold(SCREAMING_SNAKE_CASE)}')
  
        if not self.args.yes:
           _lowerCamelCase  :				Tuple      =		input("""Proceed? [Y/n] """).lower()
           if not (choice == "" or choice == "y" or choice == "yes"):
              print("""Abort""")
              exit()
        try:
           _lowerCamelCase  :				int      =		create_repo(SCREAMING_SNAKE_CASE			,      name=self.args.name			,      organization=self.args.organization)
        except HTTPError as e:
           print(SCREAMING_SNAKE_CASE)
           print(ANSI.red(e.response.text))
           exit(1)
        print("""\nYour repo now lives at:""")
        print(F'  {ANSI.bold(SCREAMING_SNAKE_CASE)}')
        print("""\nYou can clone it locally with the command below, and commit/push as usual.""")
        print(F'\n  git clone {url}')
        print("""""")
  
  