code: 
  import argparse
  import json
  import os
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
  from transformers.image_utils import PILImageResampling
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  __lowerCAmelCase				      =					logging.get_logger(__name__)
  
  
  def       a			(   a  )    ->List[str]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   ConvNextVaConfig()
  
  			if "atto" in checkpoint_url:
  						SCREAMING_SNAKE_CASE    	=   [2, 2, 6, 2]
  						SCREAMING_SNAKE_CASE    	=   [40, 80, 160, 320]
  			if "femto" in checkpoint_url:
  						SCREAMING_SNAKE_CASE    	=   [2, 2, 6, 2]
  						SCREAMING_SNAKE_CASE    	=   [48, 96, 192, 384]
  			if "pico" in checkpoint_url:
  						SCREAMING_SNAKE_CASE    	=   [2, 2, 6, 2]
  						SCREAMING_SNAKE_CASE    	=   [64, 128, 256, 512]
  			if "nano" in checkpoint_url:
  						SCREAMING_SNAKE_CASE    	=   [2, 2, 8, 2]
  						SCREAMING_SNAKE_CASE    	=   [80, 160, 320, 640]
  			if "tiny" in checkpoint_url:
  						SCREAMING_SNAKE_CASE    	=   [3, 3, 9, 3]
  						SCREAMING_SNAKE_CASE    	=   [96, 192, 384, 768]
  			if "base" in checkpoint_url:
  						SCREAMING_SNAKE_CASE    	=   [3, 3, 27, 3]
  						SCREAMING_SNAKE_CASE    	=   [128, 256, 512, 1024]
  			if "large" in checkpoint_url:
  						SCREAMING_SNAKE_CASE    	=   [3, 3, 27, 3]
  						SCREAMING_SNAKE_CASE    	=   [192, 384, 768, 1536]
  			if "huge" in checkpoint_url:
  						SCREAMING_SNAKE_CASE    	=   [3, 3, 27, 3]
  						SCREAMING_SNAKE_CASE    	=   [352, 704, 1408, 2816]
  
  			SCREAMING_SNAKE_CASE    	=   1000
  			SCREAMING_SNAKE_CASE    	=   '''imagenet-1k-id2label.json'''
  			SCREAMING_SNAKE_CASE    	=   (1, 1000)
  
  			SCREAMING_SNAKE_CASE    	=   '''huggingface/label-files'''
  			SCREAMING_SNAKE_CASE    	=   num_labels
  			SCREAMING_SNAKE_CASE    	=   json.load(open(hf_hub_download(a		,	a		,	repo_type='''dataset'''  )		,	'''r'''  )  )
  			SCREAMING_SNAKE_CASE    	=   {int(a  ): v for k, v in idalabel.items()}
  
  			SCREAMING_SNAKE_CASE    	=   idalabel
  			SCREAMING_SNAKE_CASE    	=   {v: k for k, v in idalabel.items()}
  			SCREAMING_SNAKE_CASE    	=   hidden_sizes
  			SCREAMING_SNAKE_CASE    	=   depths
  
  			return config, expected_shape
  
  
  def       a			(   a  )    ->Optional[Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			if "downsample_layers.0.0" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''downsample_layers.0.0'''		,	'''embeddings.patch_embeddings'''  )
  			if "downsample_layers.0.1" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''downsample_layers.0.1'''		,	'''embeddings.norm'''  )  # we rename to layernorm later on
  			if "downsample_layers.1.0" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''downsample_layers.1.0'''		,	'''stages.1.downsampling_layer.0'''  )
  			if "downsample_layers.1.1" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''downsample_layers.1.1'''		,	'''stages.1.downsampling_layer.1'''  )
  			if "downsample_layers.2.0" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''downsample_layers.2.0'''		,	'''stages.2.downsampling_layer.0'''  )
  			if "downsample_layers.2.1" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''downsample_layers.2.1'''		,	'''stages.2.downsampling_layer.1'''  )
  			if "downsample_layers.3.0" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''downsample_layers.3.0'''		,	'''stages.3.downsampling_layer.0'''  )
  			if "downsample_layers.3.1" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''downsample_layers.3.1'''		,	'''stages.3.downsampling_layer.1'''  )
  			if "stages" in name and "downsampling_layer" not in name:
  						# stages.0.0. for instance should be renamed to stages.0.layers.0.
  						SCREAMING_SNAKE_CASE    	=   name[: len('''stages.0'''  )] + '''.layers''' + name[len('''stages.0'''  ) :]
  			if "gamma" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''gamma'''		,	'''weight'''  )
  			if "beta" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''beta'''		,	'''bias'''  )
  			if "stages" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''stages'''		,	'''encoder.stages'''  )
  			if "norm" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''norm'''		,	'''layernorm'''  )
  			if "head" in name:
  						SCREAMING_SNAKE_CASE    	=   name.replace('''head'''		,	'''classifier'''  )
  
  			return name
  
  
  def       a			(   )    ->Any:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  			SCREAMING_SNAKE_CASE    	=   Image.open(requests.get(a		,	stream=a  ).raw  )
  			return im
  
  
  def       a			(   a  )    ->Any:
  
  
  
  
  			'''simple docstring'''
  
  
  			if "224" in checkpoint_url:
  						SCREAMING_SNAKE_CASE    	=   224
  						SCREAMING_SNAKE_CASE    	=   224 / 256
  			elif "384" in checkpoint_url:
  						SCREAMING_SNAKE_CASE    	=   384
  						SCREAMING_SNAKE_CASE    	=   None
  			else:
  						SCREAMING_SNAKE_CASE    	=   512
  						SCREAMING_SNAKE_CASE    	=   None
  
  			return ConvNextImageProcessor(
  			    size=a		,	crop_pct=a		,	image_mean=[0.4_85, 0.4_56, 0.4_06]		,	image_std=[0.2_29, 0.2_24, 0.2_25]		,	resample=PILImageResampling.BICUBIC		,	)
  
  
  @torch.no_grad()
  def       a			(   a		,	a		,	a		,	a  )    ->Optional[Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			print('''Downloading original model from checkpoint...'''  )
  			# define ConvNeXTV2 configuration based on URL
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   get_convnextva_config(a  )
  			# load original state_dict from URL
  			SCREAMING_SNAKE_CASE    	=   torch.hub.load_state_dict_from_url(a  )['''model''']
  
  			print('''Converting model parameters...'''  )
  			# rename keys
  			for key in state_dict.copy().keys():
  						SCREAMING_SNAKE_CASE    	=   state_dict.pop(a  )
  						SCREAMING_SNAKE_CASE    	=   val
  			# add prefix to all keys expect classifier head
  			for key in state_dict.copy().keys():
  						SCREAMING_SNAKE_CASE    	=   state_dict.pop(a  )
  						if not key.startswith('''classifier'''  ):
  									SCREAMING_SNAKE_CASE    	=   '''convnextv2.''' + key
  						SCREAMING_SNAKE_CASE    	=   val
  
  			# load HuggingFace model
  			SCREAMING_SNAKE_CASE    	=   ConvNextVaForImageClassification(a  )
  			model.load_state_dict(a  )
  			model.eval()
  
  			# Check outputs on an image, prepared by ConvNextImageProcessor
  			SCREAMING_SNAKE_CASE    	=   convert_preprocessor(a  )
  			SCREAMING_SNAKE_CASE    	=   preprocessor(images=prepare_img()		,	return_tensors='''pt'''  )
  			SCREAMING_SNAKE_CASE    	=   model(**a  ).logits
  
  			# note: the logits below were obtained without center cropping
  			if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([-0.39_30, 0.17_47, -0.52_46, 0.41_77, 0.42_95]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([-0.17_27, -0.53_41, -0.78_18, -0.47_45, -0.65_66]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([-0.03_33, 0.15_63, -0.91_37, 0.10_54, 0.03_81]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([-0.17_44, -0.15_55, -0.07_13, 0.09_50, -0.14_31]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([0.99_96, 0.19_66, -0.43_86, -0.34_72, 0.66_61]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([-0.25_53, -0.67_08, -0.13_59, 0.25_18, -0.24_88]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([-0.06_73, -0.56_27, -0.37_53, -0.27_22, 0.01_78]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([-0.63_77, -0.74_58, -0.21_50, 0.11_84, -0.05_97]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([1.07_99, 0.23_22, -0.88_60, 1.02_19, 0.62_31]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([0.37_66, 0.49_17, -1.14_26, 0.99_42, 0.60_24]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([0.42_20, -0.69_19, -0.43_17, -0.28_81, -0.66_09]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([0.10_82, -0.82_86, -0.50_95, 0.46_81, -0.80_85]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([-0.24_19, -0.62_21, 0.21_76, -0.09_80, -0.75_27]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([0.03_91, -0.43_71, 0.37_86, 0.12_51, -0.27_84]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([-0.05_04, 0.56_36, -0.17_29, -0.65_07, -0.39_49]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([0.35_60, 0.94_86, 0.31_49, -0.26_67, -0.51_38]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([-0.24_69, -0.45_50, -0.58_53, -0.08_10, 0.03_09]  )
  			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
  						SCREAMING_SNAKE_CASE    	=   torch.tensor([-0.30_90, 0.08_02, -0.06_82, -0.19_79, -0.28_26]  )
  			else:
  						raise ValueError(F"""Unknown URL: {checkpoint_url}"""  )
  
  			assert torch.allclose(logits[0, :5]		,	a		,	atol=1E-3  )
  			assert logits.shape == expected_shape
  			print('''Model outputs match the original results!'''  )
  
  			if save_model:
  						print('''Saving model to local...'''  )
  						# Create folder to save model
  						if not os.path.isdir(a  ):
  									os.mkdir(a  )
  
  						model.save_pretrained(a  )
  						preprocessor.save_pretrained(a  )
  
  			SCREAMING_SNAKE_CASE    	=   '''convnextv2'''
  			if "atto" in checkpoint_url:
  						model_name += "-atto"
  			if "femto" in checkpoint_url:
  						model_name += "-femto"
  			if "pico" in checkpoint_url:
  						model_name += "-pico"
  			if "nano" in checkpoint_url:
  						model_name += "-nano"
  			elif "tiny" in checkpoint_url:
  						model_name += "-tiny"
  			elif "base" in checkpoint_url:
  						model_name += "-base"
  			elif "large" in checkpoint_url:
  						model_name += "-large"
  			elif "huge" in checkpoint_url:
  						model_name += "-huge"
  			if "22k" in checkpoint_url and "1k" not in checkpoint_url:
  						model_name += "-22k"
  			elif "22k" in checkpoint_url and "1k" in checkpoint_url:
  						model_name += "-22k-1k"
  			elif "1k" in checkpoint_url:
  						model_name += "-1k"
  			if "224" in checkpoint_url:
  						model_name += "-224"
  			elif "384" in checkpoint_url:
  						model_name += "-384"
  			elif "512" in checkpoint_url:
  						model_name += "-512"
  
  			if push_to_hub:
  						print(F"""Pushing {model_name} to the hub..."""  )
  						model.push_to_hub(a  )
  						preprocessor.push_to_hub(a  )
  
  
  if __name__ == "__main__":
  	__lowerCAmelCase				      =					argparse.ArgumentParser()
  	# Required parameters
  	parser.add_argument(
  	    '--checkpoint_url',
  	    default='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
  	    type=str,
  	    help='URL of the original ConvNeXTV2 checkpoint you\'d like to convert.',
  	)
  	parser.add_argument(
  	    '--pytorch_dump_folder_path',
  	    default='model',
  	    type=str,
  	    help='Path to the output PyTorch model directory.',
  	)
  	parser.add_argument('--save_model', action='store_true', help='Save model to local')
  	parser.add_argument('--push_to_hub', action='store_true', help='Push model and image preprocessor to the hub')
  
  	__lowerCAmelCase				      =					parser.parse_args()
  	convert_convnextva_checkpoint(
  	    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
  	)
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('''''' )