


"""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





"""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("""""")

