code: 
  import importlib.util
  import weakref
  from functools import partialmethod
  
  from .dependency_versions_check import dep_version_check
  from .utils import is_accelerate_available, is_torch_available, logging
  
  
  if is_torch_available():
  							import torch
  
  __lowerCAmelCase				      =					logging.get_logger(__name__)
  
  
  def       a			(   )    ->List[Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			return importlib.util.find_spec('''deepspeed'''  ) is not None
  
  
  if is_accelerate_available() and is_deepspeed_available():
  							from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
  else:
  							# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
  							# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
  							from builtins import object as DeepSpeedConfig
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def __init__(    self			:str      ,    lowercase			:Union[str, Any] )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				set_hf_deepspeed_config(self )
  				dep_version_check('''accelerate''' )
  				dep_version_check('''deepspeed''' )
  				super().__init__(lowercase )
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def __init__(    self			:str      ,    lowercase			:List[Any] )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__(lowercase )
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   []
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if self._dtype is None:
  							raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype''' )
  				return self._dtype
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any]      ,    lowercase			:str )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.get_value(lowercase )
  				if val is None:
  							return False
  				else:
  							return val == "auto"
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:List[str]      ,    lowercase			:str      ,    lowercase			:Any=None      ,    lowercase			:Optional[Any]=True )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   self.find_config_node(lowercase )
  				if config is None:
  							return
  
  				if config.get(lowercase ) == "auto":
  							SCREAMING_SNAKE_CASE    	=   hf_val
  							return
  
  				if not must_match:
  							return
  
  				SCREAMING_SNAKE_CASE    	=   config.get(lowercase )
  				if ds_val is not None and ds_val != hf_val:
  							self.mismatches.append(f"""- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}""" )
  
  	UpperCamelCase_     :				Union[str, Any]								=  partialmethod(__lowerCamelCase   ,		must_match=__lowerCamelCase				)
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str]      ,    lowercase			:Any )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
  				self.fill_match(
  				    '''train_micro_batch_size_per_gpu'''      ,    args.per_device_train_batch_size      ,    '''per_device_train_batch_size''' )
  				self.fill_match('''gradient_accumulation_steps'''      ,    args.gradient_accumulation_steps      ,    '''gradient_accumulation_steps''' )
  				self.fill_match('''train_batch_size'''      ,    lowercase      ,    '''train_batch_size (calculated)''' )
  				self.fill_match('''gradient_clipping'''      ,    args.max_grad_norm      ,    '''max_grad_norm''' )
  
  				self.fill_match('''optimizer.params.lr'''      ,    args.learning_rate      ,    '''learning_rate''' )
  				self.fill_match('''optimizer.params.betas'''      ,    [args.adam_betaa, args.adam_betaa]      ,    '''adam_beta1+adam_beta2''' )
  				self.fill_match('''optimizer.params.eps'''      ,    args.adam_epsilon      ,    '''adam_epsilon''' )
  				self.fill_match('''optimizer.params.weight_decay'''      ,    args.weight_decay      ,    '''weight_decay''' )
  
  				self.fill_only('''scheduler.params.warmup_min_lr'''      ,    0 )  # not a trainer arg
  				self.fill_match('''scheduler.params.warmup_max_lr'''      ,    args.learning_rate      ,    '''learning_rate''' )
  				# total_num_steps - will get set in trainer_config_finalize
  
  				# fp16
  				if args.fpaa or args.fpaa_full_eval:
  							SCREAMING_SNAKE_CASE    	=   '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
  				else:
  							SCREAMING_SNAKE_CASE    	=   None
  
  				if args.save_on_each_node:
  							# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
  							SCREAMING_SNAKE_CASE    	=   self.config.get('''checkpoint'''      ,    {} )
  							SCREAMING_SNAKE_CASE    	=   args.save_on_each_node
  
  				# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
  				# any here unless the user did the work
  				self.fill_match(
  				    '''fp16.enabled'''      ,    ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')      ,    '''fp16|fp16_full_eval+fp16_backend(amp)'''      ,    )
  
  				# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
  				# ZeRO features
  				self.fill_match('''amp.enabled'''      ,    fpaa_backend == '''apex'''      ,    '''fp16+fp16_backend(apex)''' )
  				self.fill_match('''amp.opt_level'''      ,    args.fpaa_opt_level      ,    '''fp16_opt_level''' )
  
  				self.fill_match('''bf16.enabled'''      ,    (args.bfaa or args.bfaa_full_eval)      ,    '''bf16|bf16_full_eval''' )
  
  				# deepspeed's default mode is fp16 unless there is a config that says differently
  				if self.is_true('''bf16.enabled''' ):
  							SCREAMING_SNAKE_CASE    	=   torch.bfloataa
  				elif self.is_false('''fp16.enabled''' ):
  							SCREAMING_SNAKE_CASE    	=   torch.floataa
  				else:
  							SCREAMING_SNAKE_CASE    	=   torch.floataa
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any      ,    lowercase			:Any      ,    lowercase			:str      ,    lowercase			:Any )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   [
  				    '''zero_optimization.reduce_bucket_size''',
  				    '''zero_optimization.stage3_prefetch_bucket_size''',
  				    '''zero_optimization.stage3_param_persistence_threshold''',
  				]
  				SCREAMING_SNAKE_CASE    	=   [x for x in hidden_size_based_keys if self.is_auto(lowercase )]
  
  				if len(lowercase ) > 0:
  							if hasattr(model.config      ,    '''hidden_size''' ):
  										SCREAMING_SNAKE_CASE    	=   model.config.hidden_size
  							elif hasattr(model.config      ,    '''hidden_sizes''' ):
  										# if there are many hidden sizes pick the largest one
  										SCREAMING_SNAKE_CASE    	=   max(model.config.hidden_sizes )
  							else:
  										raise ValueError(
  										    '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
  										    '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
  										    f"""in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing """
  										    '''`auto` values for these keys with an integer value of your choice.''' )
  
  							self.fill_only('''zero_optimization.reduce_bucket_size'''      ,    hidden_size * hidden_size )
  							if self.is_zeroa():
  										# automatically assign the optimal config values based on model config
  										self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''      ,    0.9 * hidden_size * hidden_size )
  										self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''      ,    1_0 * hidden_size )
  
          # scheduler
  				self.fill_match('''scheduler.params.total_num_steps'''      ,    lowercase      ,    '''num_training_steps (calculated)''' )
  				self.fill_match('''scheduler.params.warmup_num_steps'''      ,    args.get_warmup_steps(lowercase )      ,    '''warmup_steps''' )
  
  				if len(self.mismatches ) > 0:
  							SCREAMING_SNAKE_CASE    	=   '''\n'''.join(self.mismatches )
  							raise ValueError(
  							    '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
  							    f""" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'.""" )
  
  
  
  __lowerCAmelCase				      =					None
  
  
  def       a			(   a  )    ->List[Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			global _hf_deepspeed_config_weak_ref
  			# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  			SCREAMING_SNAKE_CASE    	=   weakref.ref(a  )
  
  
  def       a			(   )    ->Optional[int]:
  
  
  
  
  			'''simple docstring'''
  
  
  			global _hf_deepspeed_config_weak_ref
  			SCREAMING_SNAKE_CASE    	=   None
  
  
  def       a			(   )    ->Dict:
  
  
  
  
  			'''simple docstring'''
  
  
  			if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
  						return _hf_deepspeed_config_weak_ref().is_zeroa()
  			else:
  						return False
  
  
  def       a			(   )    ->Union[str, Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
  						return _hf_deepspeed_config_weak_ref().config
  			else:
  						return None
  
  
  def       a			(   a		,	a		,	a		,	a		,	a  )    ->int:
  
  
  
  
  			'''simple docstring'''
  
  
  			from accelerate.utils import DummyOptim, DummyScheduler
  
  			SCREAMING_SNAKE_CASE    	=   hf_deepspeed_config.config
  
  			# Optimizer + Scheduler
  			# Currently supported combos:
  			# 1. DS scheduler + DS optimizer: Yes
  			# 2. HF scheduler + HF optimizer: Yes
  			# 3. DS scheduler + HF optimizer: Yes
  			# 4. HF scheduler + DS optimizer: No
  			#
  			# Unless Offload is enabled in which case it's:
  			# 1. DS scheduler + DS optimizer: Yes
  			# 2. HF scheduler + HF optimizer: Mostly*
  			# 3. DS scheduler + HF optimizer: Mostly*
  			# 4. HF scheduler + DS optimizer: No
  			#
  			# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)
  
  			SCREAMING_SNAKE_CASE    	=   None
  			if "optimizer" in config:
  						if args.adafactor:
  									raise ValueError(
  									    '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
  									    '''Only one optimizer can be configured.'''  )
  						SCREAMING_SNAKE_CASE    	=   DummyOptim(params=a  )
  			else:
  						if hf_deepspeed_config.is_offload():
  									logger.info(
  									    '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
  									    ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''  )
  
  						# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
  						# But trainer uses AdamW by default.
  						SCREAMING_SNAKE_CASE    	=   trainer.create_optimizer()
  						# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
  						SCREAMING_SNAKE_CASE    	=   True
  
  			SCREAMING_SNAKE_CASE    	=   None
  			if "scheduler" in config:
  						SCREAMING_SNAKE_CASE    	=   DummyScheduler(a  )
  			else:
  						if isinstance(a		,	a  ):
  									raise ValueError(
  									    '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
  									    '''Please configure a scheduler in the DeepSpeed config.'''  )
  						SCREAMING_SNAKE_CASE    	=   trainer.create_scheduler(num_training_steps=a		,	optimizer=a  )
  
  			return optimizer, lr_scheduler
  
  
  def       a			(   a		,	a		,	a=False  )    ->Optional[int]:
  
  
  
  
  			'''simple docstring'''
  
  
  			from deepspeed.utils import logger as ds_logger
  
  			SCREAMING_SNAKE_CASE    	=   trainer.model
  			SCREAMING_SNAKE_CASE    	=   trainer.args
  
  			SCREAMING_SNAKE_CASE    	=   trainer.accelerator.state.deepspeed_plugin.hf_ds_config
  
  			# resume config update - some bits like `model` and `num_training_steps` only become available during train
  			hf_deepspeed_config.trainer_config_finalize(a		,	a		,	a  )
  
  			# set the Deepspeed log level consistent with the Trainer
  			ds_logger.setLevel(args.get_process_log_level()  )
  
  			if inference:
  						# only Z3 makes sense for the inference
  						if not hf_deepspeed_config.is_zeroa():
  									raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''  )
  
  						# in case the training config is re-used for inference
  						hf_deepspeed_config.del_config_sub_tree('''optimizer'''  )
  						hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''  )
  						SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   None, None
  						SCREAMING_SNAKE_CASE    	=   None
  			else:
  						SCREAMING_SNAKE_CASE    	=   None  # important for when deepspeed_init is used as re-init
  						SCREAMING_SNAKE_CASE    	=   list(filter(lambda a  : p.requires_grad		,	model.parameters()  )  )
  						SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   deepspeed_optim_sched(
  						    a		,	a		,	a		,	a		,	a  )
  
  			# keep for quick debug:
  			# from pprint import pprint; pprint(config)
  
  			return optimizer, lr_scheduler
  
  
  
  
  
  def       a			(   a		,	a  )    ->int:
  
  
  
  
  			'''simple docstring'''
  
  
  			import glob
  
  			SCREAMING_SNAKE_CASE    	=   sorted(glob.glob(F"""{checkpoint_path}/global_step*"""  )  )
  
  			if len(a  ) > 0:
  						logger.info(F"""Attempting to resume from {checkpoint_path}"""  )
  						# this magically updates self.optimizer and self.lr_scheduler
  						SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   deepspeed_engine.load_checkpoint(
  						    a		,	load_optimizer_states=a		,	load_lr_scheduler_states=a  )
  						if load_path is None:
  									raise ValueError(F"""[deepspeed] failed to resume from checkpoint {checkpoint_path}"""  )
  			else:
  						raise ValueError(F"""Can't find a valid checkpoint at {checkpoint_path}"""  )
style_context: 
  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				):
  
  
  
  
  
  
  
  	@staticmethod
  	def    snake_case__    (    lowercase			:ArgumentParser )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''login'''      ,    help='''Log in using the same credentials as on huggingface.co''' )
  				login_parser.set_defaults(func=lambda lowercase : LoginCommand(lowercase ) )
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''whoami'''      ,    help='''Find out which huggingface.co account you are logged in as.''' )
  				whoami_parser.set_defaults(func=lambda lowercase : WhoamiCommand(lowercase ) )
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''logout'''      ,    help='''Log out''' )
  				logout_parser.set_defaults(func=lambda lowercase : LogoutCommand(lowercase ) )
  
  				# new system: git-based repo system
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser(
  				    '''repo'''      ,    help='''Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.'''      ,    )
  				SCREAMING_SNAKE_CASE    	=   repo_parser.add_subparsers(
  				    help='''Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands''' )
  				SCREAMING_SNAKE_CASE    	=   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=lowercase      ,    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=lowercase      ,    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 lowercase : RepoCreateCommand(lowercase ) )
  
  
  
  
  
  class 						lowerCamelCase			:
  	UpperCamelCase_     :				List[str]								=  '\u001b[1m'
  	UpperCamelCase_     :				Union[str, Any]								=  '\u001b[31m'
  	UpperCamelCase_     :				Any								=  '\u001b[90m'
  	UpperCamelCase_     :				List[str]								=  '\u001b[0m'
  
  
  
  
  
  
  
  	@classmethod
  	def    snake_case__    (    cls			:List[Any]      ,    lowercase			:Any )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return f"""{cls._bold}{s}{cls._reset}"""
  
  
  
  
  
  
  
  	@classmethod
  	def    snake_case__    (    cls			:Optional[Any]      ,    lowercase			:str )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return f"""{cls._bold}{cls._red}{s}{cls._reset}"""
  
  
  
  
  
  
  
  	@classmethod
  	def    snake_case__    (    cls			:List[str]      ,    lowercase			:Any )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return f"""{cls._gray}{s}{cls._reset}"""
  
  
  
  def       a			(   a		,	a  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   [max(len(str(a  )  ) for x in col  ) for col in zip(*a		,	a  )]
  			SCREAMING_SNAKE_CASE    	=   ('''{{:{}}} ''' * len(a  )).format(*a  )
  			SCREAMING_SNAKE_CASE    	=   []
  			lines.append(row_format.format(*a  )  )
  			lines.append(row_format.format(*['''-''' * w for w in col_widths]  )  )
  			for row in rows:
  						lines.append(row_format.format(*a  )  )
  			return "\n".join(a  )
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:List[str]      ,    lowercase			:Optional[Any] )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   args
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str] )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				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				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any] )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				print(
  				    ANSI.red(
  				        '''WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use '''
  				        '''`huggingface-cli whoami` instead.''' ) )
  				SCREAMING_SNAKE_CASE    	=   HfFolder.get_token()
  				if token is None:
  							print('''Not logged in''' )
  							exit()
  				try:
  							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   whoami(lowercase )
  							print(lowercase )
  							if orgs:
  										print(ANSI.bold('''orgs: ''' )      ,    ''','''.join(lowercase ) )
  				except HTTPError as e:
  							print(lowercase )
  							print(ANSI.red(e.response.text ) )
  							exit(1 )
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				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				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				print(
  				    ANSI.red(
  				        '''WARNING! Managing repositories through transformers-cli is deprecated. '''
  				        '''Please use `huggingface-cli` instead.''' ) )
  				SCREAMING_SNAKE_CASE    	=   HfFolder.get_token()
  				if token is None:
  							print('''Not logged in''' )
  							exit(1 )
  				try:
  							SCREAMING_SNAKE_CASE    	=   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:
  							SCREAMING_SNAKE_CASE    	=   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('''''' )
  
  				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   whoami(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.args.organization if self.args.organization is not None else user
  				SCREAMING_SNAKE_CASE    	=   f"""{namespace}/{self.args.name}"""
  				print(f"""You are about to create {ANSI.bold(lowercase )}""" )
  
  				if not self.args.yes:
  							SCREAMING_SNAKE_CASE    	=   input('''Proceed? [Y/n] ''' ).lower()
  							if not (choice == "" or choice == "y" or choice == "yes"):
  										print('''Abort''' )
  										exit()
  				try:
  							SCREAMING_SNAKE_CASE    	=   create_repo(lowercase      ,    name=self.args.name      ,    organization=self.args.organization )
  				except HTTPError as e:
  							print(lowercase )
  							print(ANSI.red(e.response.text ) )
  							exit(1 )
  				print('''\nYour repo now lives at:''' )
  				print(f"""  {ANSI.bold(lowercase )}""" )
  				print('''\nYou can clone it locally with the command below, and commit/push as usual.''' )
  				print(f"""\n  git clone {url}""" )
  				print('''''' )