




"""simple docstring"""

from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if not inplace:
		A_	:					str						 =		ma + ma
	else:
		ma += ma

	return ma

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Optional[Any]						 =		-1 * len(SCREAMING_SNAKE_CASE__						)
	A_	:					Optional[Any]						 =		list(range(len(tensor.shape[:zero_index]						)						)						)
	return tensor.permute(first_inds + [zero_index + i for i in inds]						)

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	return t.reshape(t.shape[:-no_dims] + (-1,)						)

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = 1E-4						):




	"""simple docstring"""






	A_	:					Union[str, Any]						 =		mask.expand(*value.shape						)
	return torch.sum(mask * value	,   dim=SCREAMING_SNAKE_CASE__						) / (eps + torch.sum(SCREAMING_SNAKE_CASE__	,   dim=SCREAMING_SNAKE_CASE__						))

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase = 2.3125	,   _UpperCAmelCase = 21.6875	,   _UpperCAmelCase = 64						):




	"""simple docstring"""






	A_	:					Optional[int]						 =		torch.linspace(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   no_bins - 1	,   device=pts.device						)
	A_	:					Union[str, Any]						 =		torch.sqrt(torch.sum((pts.unsqueeze(-2						) - pts.unsqueeze(-3						)) ** 2	,   dim=-1						)						)
	return torch.bucketize(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						)

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					List[Any]						 =		dicts[0]
	A_	:					Optional[Any]						 =		{}
	for k, v in first.items():
		A_	:					List[Any]						 =		[d[k] for d in dicts]
		if isinstance(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						):
			A_	:					List[Any]						 =		dict_multimap(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						)
		else:
			A_	:					int						 =		fn(SCREAMING_SNAKE_CASE__						)

	return new_dict

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Optional[int]						 =		v_bins.view(((1,) * len(x.shape						)) + (len(SCREAMING_SNAKE_CASE__						),)						)
	A_	:					List[str]						 =		x[..., None] - reshaped_bins
	A_	:					Union[str, Any]						 =		torch.argmin(torch.abs(SCREAMING_SNAKE_CASE__						)	,   dim=-1						)
	return nn.functional.one_hot(SCREAMING_SNAKE_CASE__	,   num_classes=len(SCREAMING_SNAKE_CASE__						)						).float()

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0						):




	"""simple docstring"""






	A_	:					List[Union[slice, torch.Tensor]]						 =		[]
	for i, s in enumerate(data.shape[:no_batch_dims]						):
		A_	:					Union[str, Any]						 =		torch.arange(SCREAMING_SNAKE_CASE__						)
		A_	:					Union[str, Any]						 =		r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape						) - i - 1)))						)
		ranges.append(SCREAMING_SNAKE_CASE__						)

	A_	:					List[Union[slice, torch.Tensor]]						 =		[slice(SCREAMING_SNAKE_CASE__						) for _ in range(len(data.shape						) - no_batch_dims						)]
	A_	:					Optional[Any]						 =		inds
	ranges.extend(SCREAMING_SNAKE_CASE__						)
	# Matt note: Editing this to get around the behaviour of using a list as an array index changing
	# in recent Numpy versions
	return data[tuple(SCREAMING_SNAKE_CASE__						)]


_lowerCamelCase	:      List[str]   =							TypeVar('T')

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Dict[Any, Union[dict, list, tuple, Any]]						 =		{}
	for k, v in dic.items():
		if isinstance(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						):
			A_	:					Union[str, Any]						 =		dict_map(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						)
		else:
			A_	:					int						 =		tree_map(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						)

	return new_dict

@overload
def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	...

@overload
def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	...

@overload
def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	...

@overload
def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	...

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if isinstance(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						):
		return dict_map(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						)
	elif isinstance(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						):
		return [tree_map(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						) for x in tree]
	elif isinstance(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						):
		return tuple(tree_map(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						) for x in tree						)
	elif isinstance(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__						):
		return fn(SCREAMING_SNAKE_CASE__						)
	else:
		print(type(SCREAMING_SNAKE_CASE__						)						)
		raise ValueError('''Not supported'''						)
_lowerCamelCase	:      str   =							partial(tree_map, leaf_type=torch.Tensor)












'''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()
snake_case_  :							Optional[int]            =	logging.get_logger(__name__)





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int] )			->						Dict:
						UpperCAmelCase_      :   int      =	ConvNextVaConfig()

						if "atto" in checkpoint_url:
												UpperCAmelCase_      :   Tuple      =	[2, 2, 6, 2]
												UpperCAmelCase_      :   Tuple      =	[40, 80, 160, 320]
						if "femto" in checkpoint_url:
												UpperCAmelCase_      :   List[Any]      =	[2, 2, 6, 2]
												UpperCAmelCase_      :   Any      =	[48, 96, 192, 384]
						if "pico" in checkpoint_url:
												UpperCAmelCase_      :   Union[str, Any]      =	[2, 2, 6, 2]
												UpperCAmelCase_      :   int      =	[64, 128, 256, 512]
						if "nano" in checkpoint_url:
												UpperCAmelCase_      :   str      =	[2, 2, 8, 2]
												UpperCAmelCase_      :   List[str]      =	[80, 160, 320, 640]
						if "tiny" in checkpoint_url:
												UpperCAmelCase_      :   Optional[Any]      =	[3, 3, 9, 3]
												UpperCAmelCase_      :   Any      =	[96, 192, 384, 768]
						if "base" in checkpoint_url:
												UpperCAmelCase_      :   Union[str, Any]      =	[3, 3, 27, 3]
												UpperCAmelCase_      :   Optional[int]      =	[128, 256, 512, 1024]
						if "large" in checkpoint_url:
												UpperCAmelCase_      :   int      =	[3, 3, 27, 3]
												UpperCAmelCase_      :   List[Any]      =	[192, 384, 768, 1536]
						if "huge" in checkpoint_url:
												UpperCAmelCase_      :   Tuple      =	[3, 3, 27, 3]
												UpperCAmelCase_      :   Dict      =	[352, 704, 1408, 2816]

						UpperCAmelCase_      :   Union[str, Any]      =	1000
						UpperCAmelCase_      :   Optional[Any]      =	'''imagenet-1k-id2label.json'''
						UpperCAmelCase_      :   int      =	(1, 1000)

						UpperCAmelCase_      :   Any      =	'''huggingface/label-files'''
						UpperCAmelCase_      :   List[str]      =	num_labels
						UpperCAmelCase_      :   Optional[int]      =	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      repo_type='''dataset''' ),      '''r''' ) )
						UpperCAmelCase_      :   Tuple      =	{int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}

						UpperCAmelCase_      :   Union[str, Any]      =	idalabel
						UpperCAmelCase_      :   List[Any]      =	{v: k for k, v in idalabel.items()}
						UpperCAmelCase_      :   str      =	hidden_sizes
						UpperCAmelCase_      :   Dict      =	depths

						return config, expected_shape





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict )			->						Optional[Any]:
						if "downsample_layers.0.0" in name:
												UpperCAmelCase_      :   str      =	name.replace('''downsample_layers.0.0''',      '''embeddings.patch_embeddings''' )
						if "downsample_layers.0.1" in name:
												UpperCAmelCase_      :   Optional[Any]      =	name.replace('''downsample_layers.0.1''',      '''embeddings.norm''' )  # we rename to layernorm later on
						if "downsample_layers.1.0" in name:
												UpperCAmelCase_      :   Dict      =	name.replace('''downsample_layers.1.0''',      '''stages.1.downsampling_layer.0''' )
						if "downsample_layers.1.1" in name:
												UpperCAmelCase_      :   Any      =	name.replace('''downsample_layers.1.1''',      '''stages.1.downsampling_layer.1''' )
						if "downsample_layers.2.0" in name:
												UpperCAmelCase_      :   Optional[int]      =	name.replace('''downsample_layers.2.0''',      '''stages.2.downsampling_layer.0''' )
						if "downsample_layers.2.1" in name:
												UpperCAmelCase_      :   List[Any]      =	name.replace('''downsample_layers.2.1''',      '''stages.2.downsampling_layer.1''' )
						if "downsample_layers.3.0" in name:
												UpperCAmelCase_      :   Optional[int]      =	name.replace('''downsample_layers.3.0''',      '''stages.3.downsampling_layer.0''' )
						if "downsample_layers.3.1" in name:
												UpperCAmelCase_      :   Optional[int]      =	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_      :   Any      =	name[: len('''stages.0''' )] + '''.layers''' + name[len('''stages.0''' ) :]
						if "gamma" in name:
												UpperCAmelCase_      :   Optional[Any]      =	name.replace('''gamma''',      '''weight''' )
						if "beta" in name:
												UpperCAmelCase_      :   Dict      =	name.replace('''beta''',      '''bias''' )
						if "stages" in name:
												UpperCAmelCase_      :   str      =	name.replace('''stages''',      '''encoder.stages''' )
						if "norm" in name:
												UpperCAmelCase_      :   Union[str, Any]      =	name.replace('''norm''',      '''layernorm''' )
						if "head" in name:
												UpperCAmelCase_      :   Union[str, Any]      =	name.replace('''head''',      '''classifier''' )

						return name





def 							lowerCamelCase_   (	)			->						Union[str, Any]:
						UpperCAmelCase_      :   int      =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						UpperCAmelCase_      :   Optional[int]      =	Image.open(requests.get(SCREAMING_SNAKE_CASE__,      stream=SCREAMING_SNAKE_CASE__ ).raw )
						return im





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int] )			->						int:
						if "224" in checkpoint_url:
												UpperCAmelCase_      :   Optional[Any]      =	224
												UpperCAmelCase_      :   Any      =	224 / 256
						elif "384" in checkpoint_url:
												UpperCAmelCase_      :   int      =	384
												UpperCAmelCase_      :   Optional[Any]      =	None
						else:
												UpperCAmelCase_      :   List[Any]      =	512
												UpperCAmelCase_      :   Union[str, Any]      =	None

						return ConvNextImageProcessor(
						    size=SCREAMING_SNAKE_CASE__,      crop_pct=SCREAMING_SNAKE_CASE__,      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 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      str )			->						Optional[Any]:
						print('''Downloading original model from checkpoint...''' )
						# define ConvNeXTV2 configuration based on URL
						UpperCAmelCase_    ,							UpperCAmelCase_      :   Tuple      =	get_convnextva_config(SCREAMING_SNAKE_CASE__ )
						# load original state_dict from URL
						UpperCAmelCase_      :   int      =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__ )['''model''']

						print('''Converting model parameters...''' )
						# rename keys
						for key in state_dict.copy().keys():
												UpperCAmelCase_      :   Optional[Any]      =	state_dict.pop(SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   List[str]      =	val
						# add prefix to all keys expect classifier head
						for key in state_dict.copy().keys():
												UpperCAmelCase_      :   List[str]      =	state_dict.pop(SCREAMING_SNAKE_CASE__ )
												if not key.startswith('''classifier''' ):
																		UpperCAmelCase_      :   Optional[Any]      =	'''convnextv2.''' + key
												UpperCAmelCase_      :   Union[str, Any]      =	val

						# load HuggingFace model
						UpperCAmelCase_      :   Optional[int]      =	ConvNextVaForImageClassification(SCREAMING_SNAKE_CASE__ )
						model.load_state_dict(SCREAMING_SNAKE_CASE__ )
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						UpperCAmelCase_      :   Any      =	convert_preprocessor(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Tuple      =	preprocessor(images=prepare_img(),      return_tensors='''pt''' )
						UpperCAmelCase_      :   Optional[Any]      =	model(**SCREAMING_SNAKE_CASE__ ).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_      :   Optional[Any]      =	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":
												UpperCAmelCase_      :   List[Any]      =	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":
												UpperCAmelCase_      :   Dict      =	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":
												UpperCAmelCase_      :   Tuple      =	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":
												UpperCAmelCase_      :   Optional[int]      =	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":
												UpperCAmelCase_      :   Optional[Any]      =	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":
												UpperCAmelCase_      :   int      =	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":
												UpperCAmelCase_      :   Optional[int]      =	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":
												UpperCAmelCase_      :   List[Any]      =	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":
												UpperCAmelCase_      :   Optional[int]      =	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":
												UpperCAmelCase_      :   Dict      =	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":
												UpperCAmelCase_      :   Dict      =	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":
												UpperCAmelCase_      :   Optional[int]      =	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":
												UpperCAmelCase_      :   Optional[int]      =	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":
												UpperCAmelCase_      :   Any      =	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":
												UpperCAmelCase_      :   Optional[Any]      =	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":
												UpperCAmelCase_      :   List[Any]      =	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":
												UpperCAmelCase_      :   Tuple      =	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],      SCREAMING_SNAKE_CASE__,      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(SCREAMING_SNAKE_CASE__ ):
																		os.mkdir(SCREAMING_SNAKE_CASE__ )

												model.save_pretrained(SCREAMING_SNAKE_CASE__ )
												preprocessor.save_pretrained(SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   Dict      =	'''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(SCREAMING_SNAKE_CASE__ )
												preprocessor.push_to_hub(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
					snake_case_  :							Tuple            =	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")

					snake_case_  :							Optional[int]            =	parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)





