code: 
  """simple docstring"""
  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()
  __magic_name__		       =				logging.get_logger(__name__)
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						ConvNextVaConfig()
  
  	if "atto" in checkpoint_url:
  		_UpperCAmelCase =						[2, 2, 6, 2]
  		_UpperCAmelCase =						[40, 80, 160, 320]
  	if "femto" in checkpoint_url:
  		_UpperCAmelCase =						[2, 2, 6, 2]
  		_UpperCAmelCase =						[48, 96, 192, 384]
  	if "pico" in checkpoint_url:
  		_UpperCAmelCase =						[2, 2, 6, 2]
  		_UpperCAmelCase =						[64, 128, 256, 512]
  	if "nano" in checkpoint_url:
  		_UpperCAmelCase =						[2, 2, 8, 2]
  		_UpperCAmelCase =						[80, 160, 320, 640]
  	if "tiny" in checkpoint_url:
  		_UpperCAmelCase =						[3, 3, 9, 3]
  		_UpperCAmelCase =						[96, 192, 384, 768]
  	if "base" in checkpoint_url:
  		_UpperCAmelCase =						[3, 3, 27, 3]
  		_UpperCAmelCase =						[128, 256, 512, 1024]
  	if "large" in checkpoint_url:
  		_UpperCAmelCase =						[3, 3, 27, 3]
  		_UpperCAmelCase =						[192, 384, 768, 1536]
  	if "huge" in checkpoint_url:
  		_UpperCAmelCase =						[3, 3, 27, 3]
  		_UpperCAmelCase =						[352, 704, 1408, 2816]
  
  	_UpperCAmelCase =						1000
  	_UpperCAmelCase =						"imagenet-1k-id2label.json"
  	_UpperCAmelCase =						(1, 1000)
  
  	_UpperCAmelCase =						"huggingface/label-files"
  	_UpperCAmelCase =						num_labels
  	_UpperCAmelCase =						json.load(open(hf_hub_download(UpperCamelCase__				,     UpperCamelCase__				,     repo_type="dataset"      )				,     "r"      )      )
  	_UpperCAmelCase =						{int(UpperCamelCase__      ): v for k, v in idalabel.items()}
  
  	_UpperCAmelCase =						idalabel
  	_UpperCAmelCase =						{v: k for k, v in idalabel.items()}
  	_UpperCAmelCase =						hidden_sizes
  	_UpperCAmelCase =						depths
  
  	return config, expected_shape
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	if "downsample_layers.0.0" in name:
  		_UpperCAmelCase =						name.replace("downsample_layers.0.0"				,     "embeddings.patch_embeddings"      )
  	if "downsample_layers.0.1" in name:
  		_UpperCAmelCase =						name.replace("downsample_layers.0.1"				,     "embeddings.norm"      )  # we rename to layernorm later on
  	if "downsample_layers.1.0" in name:
  		_UpperCAmelCase =						name.replace("downsample_layers.1.0"				,     "stages.1.downsampling_layer.0"      )
  	if "downsample_layers.1.1" in name:
  		_UpperCAmelCase =						name.replace("downsample_layers.1.1"				,     "stages.1.downsampling_layer.1"      )
  	if "downsample_layers.2.0" in name:
  		_UpperCAmelCase =						name.replace("downsample_layers.2.0"				,     "stages.2.downsampling_layer.0"      )
  	if "downsample_layers.2.1" in name:
  		_UpperCAmelCase =						name.replace("downsample_layers.2.1"				,     "stages.2.downsampling_layer.1"      )
  	if "downsample_layers.3.0" in name:
  		_UpperCAmelCase =						name.replace("downsample_layers.3.0"				,     "stages.3.downsampling_layer.0"      )
  	if "downsample_layers.3.1" in name:
  		_UpperCAmelCase =						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.
  		_UpperCAmelCase =						name[: len("stages.0"      )] + ".layers" + name[len("stages.0"      ) :]
  	if "gamma" in name:
  		_UpperCAmelCase =						name.replace("gamma"				,     "weight"      )
  	if "beta" in name:
  		_UpperCAmelCase =						name.replace("beta"				,     "bias"      )
  	if "stages" in name:
  		_UpperCAmelCase =						name.replace("stages"				,     "encoder.stages"      )
  	if "norm" in name:
  		_UpperCAmelCase =						name.replace("norm"				,     "layernorm"      )
  	if "head" in name:
  		_UpperCAmelCase =						name.replace("head"				,     "classifier"      )
  
  	return name
  
  
  
  
  def     __lowerCamelCase  (		):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						"http://images.cocodataset.org/val2017/000000039769.jpg"
  	_UpperCAmelCase =						Image.open(requests.get(UpperCamelCase__				,     stream=UpperCamelCase__      ).raw      )
  	return im
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	if "224" in checkpoint_url:
  		_UpperCAmelCase =						224
  		_UpperCAmelCase =						224 / 256
  	elif "384" in checkpoint_url:
  		_UpperCAmelCase =						384
  		_UpperCAmelCase =						None
  	else:
  		_UpperCAmelCase =						512
  		_UpperCAmelCase =						None
  
  	return ConvNextImageProcessor(
  	    size=UpperCamelCase__				,     crop_pct=UpperCamelCase__				,     image_mean=[0.485, 0.456, 0.406]				,     image_std=[0.229, 0.224, 0.225]				,     resample=PILImageResampling.BICUBIC				,     )
  
  
  
  
  @torch.no_grad()
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	print("Downloading original model from checkpoint..."      )
  	# define ConvNeXTV2 configuration based on URL
  	_UpperCAmelCase ,      _UpperCAmelCase =						get_convnextva_config(UpperCamelCase__      )
  	# load original state_dict from URL
  	_UpperCAmelCase =						torch.hub.load_state_dict_from_url(UpperCamelCase__      )["model"]
  
  	print("Converting model parameters..."      )
  	# rename keys
  	for key in state_dict.copy().keys():
  		_UpperCAmelCase =						state_dict.pop(UpperCamelCase__      )
  		_UpperCAmelCase =						val
  	# add prefix to all keys expect classifier head
  	for key in state_dict.copy().keys():
  		_UpperCAmelCase =						state_dict.pop(UpperCamelCase__      )
  		if not key.startswith("classifier"      ):
  			_UpperCAmelCase =						"convnextv2." + key
  		_UpperCAmelCase =						val
  
  	# load HuggingFace model
  	_UpperCAmelCase =						ConvNextVaForImageClassification(UpperCamelCase__      )
  	model.load_state_dict(UpperCamelCase__      )
  	model.eval()
  
  	# Check outputs on an image, prepared by ConvNextImageProcessor
  	_UpperCAmelCase =						convert_preprocessor(UpperCamelCase__      )
  	_UpperCAmelCase =						preprocessor(images=prepare_img()				,     return_tensors="pt"      )
  	_UpperCAmelCase =						model(**UpperCamelCase__      ).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":
  		_UpperCAmelCase =						torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
  		_UpperCAmelCase =						torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
  		_UpperCAmelCase =						torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
  		_UpperCAmelCase =						torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
  		_UpperCAmelCase =						torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
  		_UpperCAmelCase =						torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
  		_UpperCAmelCase =						torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
  		_UpperCAmelCase =						torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
  		_UpperCAmelCase =						torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
  		_UpperCAmelCase =						torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
  		_UpperCAmelCase =						torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
  		_UpperCAmelCase =						torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
  		_UpperCAmelCase =						torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
  		_UpperCAmelCase =						torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
  		_UpperCAmelCase =						torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
  		_UpperCAmelCase =						torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
  		_UpperCAmelCase =						torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]      )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
  		_UpperCAmelCase =						torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]      )
  	else:
  		raise ValueError(f"Unknown URL: {checkpoint_url}"      )
  
  	assert torch.allclose(logits[0, :5]				,     UpperCamelCase__				,     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(UpperCamelCase__      ):
  			os.mkdir(UpperCamelCase__      )
  
  		model.save_pretrained(UpperCamelCase__      )
  		preprocessor.save_pretrained(UpperCamelCase__      )
  
  	_UpperCAmelCase =						"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(UpperCamelCase__      )
  		preprocessor.push_to_hub(UpperCamelCase__      )
  
  
  if __name__ == "__main__":
  			__magic_name__		       =				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''')
  
  			__magic_name__		       =				parser.parse_args()
  			convert_convnextva_checkpoint(
  			    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
  			)
  
  
  
  
  
style_context: 
  """simple docstring"""
  from .stack import Stack
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						Stack()
  	_UpperCAmelCase =						{"(": ")", "[": "]", "{": "}"}
  	for bracket in parentheses:
  		if bracket in bracket_pairs:
  			stack.push(UpperCamelCase__      )
  		elif bracket in (")", "]", "}"):
  			if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
  				return False
  	return stack.is_empty()
  
  
  if __name__ == "__main__":
  			from doctest import testmod
  
  			testmod()
  
  			__magic_name__		       =				['''((()))''', '''((())''', '''(()))''']
  			print('''Balanced parentheses demonstration:\n''')
  			for example in examples:
  						__magic_name__		       =				'''''' if balanced_parentheses(example) else '''not '''
  						print(f'''{example} is {not_str}balanced''')
  
  
  
  
  