

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()
A__							=				logging.get_logger(__name__)




def        _lowercase  (					a_					:       List[str]					)					->						Union[str, Any]:




					'''simple docstring'''







					__magic_name__				=     ConvNextVaConfig()

					if "atto" in checkpoint_url:
										__magic_name__				=     [2, 2, 6, 2]
										__magic_name__				=     [4_0, 8_0, 1_6_0, 3_2_0]
					if "femto" in checkpoint_url:
										__magic_name__				=     [2, 2, 6, 2]
										__magic_name__				=     [4_8, 9_6, 1_9_2, 3_8_4]
					if "pico" in checkpoint_url:
										__magic_name__				=     [2, 2, 6, 2]
										__magic_name__				=     [6_4, 1_2_8, 2_5_6, 5_1_2]
					if "nano" in checkpoint_url:
										__magic_name__				=     [2, 2, 8, 2]
										__magic_name__				=     [8_0, 1_6_0, 3_2_0, 6_4_0]
					if "tiny" in checkpoint_url:
										__magic_name__				=     [3, 3, 9, 3]
										__magic_name__				=     [9_6, 1_9_2, 3_8_4, 7_6_8]
					if "base" in checkpoint_url:
										__magic_name__				=     [3, 3, 2_7, 3]
										__magic_name__				=     [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
					if "large" in checkpoint_url:
										__magic_name__				=     [3, 3, 2_7, 3]
										__magic_name__				=     [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
					if "huge" in checkpoint_url:
										__magic_name__				=     [3, 3, 2_7, 3]
										__magic_name__				=     [3_5_2, 7_0_4, 1_4_0_8, 2_8_1_6]

					__magic_name__				=     1_0_0_0
					__magic_name__				=     'imagenet-1k-id2label.json'
					__magic_name__				=     (1, 1_0_0_0)

					__magic_name__				=     'huggingface/label-files'
					__magic_name__				=     num_labels
					__magic_name__				=     json.load(open(hf_hub_download(a_       ,a_       ,repo_type='dataset'					)       ,'r'					)					)
					__magic_name__				=     {int(a_					): v for k, v in idalabel.items()}

					__magic_name__				=     idalabel
					__magic_name__				=     {v: k for k, v in idalabel.items()}
					__magic_name__				=     hidden_sizes
					__magic_name__				=     depths

					return config, expected_shape




def        _lowercase  (					a_					:       Any					)					->						Optional[int]:




					'''simple docstring'''







					if "downsample_layers.0.0" in name:
										__magic_name__				=     name.replace('downsample_layers.0.0'       ,'embeddings.patch_embeddings'					)
					if "downsample_layers.0.1" in name:
										__magic_name__				=     name.replace('downsample_layers.0.1'       ,'embeddings.norm'					)  # we rename to layernorm later on
					if "downsample_layers.1.0" in name:
										__magic_name__				=     name.replace('downsample_layers.1.0'       ,'stages.1.downsampling_layer.0'					)
					if "downsample_layers.1.1" in name:
										__magic_name__				=     name.replace('downsample_layers.1.1'       ,'stages.1.downsampling_layer.1'					)
					if "downsample_layers.2.0" in name:
										__magic_name__				=     name.replace('downsample_layers.2.0'       ,'stages.2.downsampling_layer.0'					)
					if "downsample_layers.2.1" in name:
										__magic_name__				=     name.replace('downsample_layers.2.1'       ,'stages.2.downsampling_layer.1'					)
					if "downsample_layers.3.0" in name:
										__magic_name__				=     name.replace('downsample_layers.3.0'       ,'stages.3.downsampling_layer.0'					)
					if "downsample_layers.3.1" in name:
										__magic_name__				=     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.
										__magic_name__				=     name[: len('stages.0'					)] + '.layers' + name[len('stages.0'					) :]
					if "gamma" in name:
										__magic_name__				=     name.replace('gamma'       ,'weight'					)
					if "beta" in name:
										__magic_name__				=     name.replace('beta'       ,'bias'					)
					if "stages" in name:
										__magic_name__				=     name.replace('stages'       ,'encoder.stages'					)
					if "norm" in name:
										__magic_name__				=     name.replace('norm'       ,'layernorm'					)
					if "head" in name:
										__magic_name__				=     name.replace('head'       ,'classifier'					)

					return name




def        _lowercase  (					)					->						Dict:




					'''simple docstring'''







					__magic_name__				=     'http://images.cocodataset.org/val2017/000000039769.jpg'
					__magic_name__				=     Image.open(requests.get(a_       ,stream=a_					).raw					)
					return im




def        _lowercase  (					a_					:       List[Any]					)					->						List[str]:




					'''simple docstring'''







					if "224" in checkpoint_url:
										__magic_name__				=     2_2_4
										__magic_name__				=     2_2_4 / 2_5_6
					elif "384" in checkpoint_url:
										__magic_name__				=     3_8_4
										__magic_name__				=     None
					else:
										__magic_name__				=     5_1_2
										__magic_name__				=     None

					return ConvNextImageProcessor(
					    size=a_       ,crop_pct=a_       ,image_mean=[0.485, 0.456, 0.406]       ,image_std=[0.229, 0.224, 0.225]       ,resample=PILImageResampling.BICUBIC       ,)




@torch.no_grad()
def        _lowercase  (					a_					:       List[str]       ,a_					:       int       ,a_					:       Any       ,a_					:       int					)					->						List[Any]:




					'''simple docstring'''







					print('Downloading original model from checkpoint...'					)
					# define ConvNeXTV2 configuration based on URL
					__magic_name__,						__magic_name__				=     get_convnextva_config(a_					)
					# load original state_dict from URL
					__magic_name__				=     torch.hub.load_state_dict_from_url(a_					)['model']

					print('Converting model parameters...'					)
					# rename keys
					for key in state_dict.copy().keys():
										__magic_name__				=     state_dict.pop(a_					)
										__magic_name__				=     val
					# add prefix to all keys expect classifier head
					for key in state_dict.copy().keys():
										__magic_name__				=     state_dict.pop(a_					)
										if not key.startswith('classifier'					):
															__magic_name__				=     'convnextv2.' + key
										__magic_name__				=     val

					# load HuggingFace model
					__magic_name__				=     ConvNextVaForImageClassification(a_					)
					model.load_state_dict(a_					)
					model.eval()

					# Check outputs on an image, prepared by ConvNextImageProcessor
					__magic_name__				=     convert_preprocessor(a_					)
					__magic_name__				=     preprocessor(images=prepare_img()       ,return_tensors='pt'					)
					__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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":
										__magic_name__				=     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]       ,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_					)

					__magic_name__				=     '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__":
						A__							=				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")

						A__							=				parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)







from hashlib import mda, shaaaa

A__							=				(shaaaa, mda)

class 					__UpperCamelCase					:







	def __init__( self:			Optional[int]	,			__UpperCamelCase:			int = 8   ):

						'''simple docstring'''





						__magic_name__				=     0b0
						__magic_name__				=     size







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.hash_(__UpperCamelCase   )
						self.bitarray |= h







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.hash_(__UpperCamelCase   )
						return (h & self.bitarray) == h







	def __contains__( self:			Optional[int]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						return self.exists(__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     bin(__UpperCamelCase   )[2:]
						return res.zfill(self.size   )







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						return self.format_bin(self.bitarray   )







	def 						_SCREAMING_SNAKE_CASE				( self:			str	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     0b0
						for func in HASH_FUNCTIONS:
											__magic_name__				=     (
											    int.from_bytes(func(value.encode()   ).digest()	,			'little'   ) % self.size
											)
											res |= 2**position
						return res







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						return self.format_bin(self.hash_(__UpperCamelCase   )   )







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     bin(self.bitarray   ).count('1'   )
						return (n_ones / self.size) ** len(__UpperCamelCase   )




