






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()
UpperCAmelCase__			 =	logging.get_logger(__name__)







def 						a_      (__A			)							->	List[str]:



					"""simple docstring"""
					__a				:	List[str]			=				ConvNextVaConfig()

					if "atto" in checkpoint_url:
										__a				:	Optional[Any]			=				[2, 2, 6, 2]
										__a				:	Any			=				[40, 80, 160, 320]
					if "femto" in checkpoint_url:
										__a				:	int			=				[2, 2, 6, 2]
										__a				:	Optional[Any]			=				[48, 96, 192, 384]
					if "pico" in checkpoint_url:
										__a				:	Tuple			=				[2, 2, 6, 2]
										__a				:	Union[str, Any]			=				[64, 128, 256, 512]
					if "nano" in checkpoint_url:
										__a				:	Any			=				[2, 2, 8, 2]
										__a				:	List[str]			=				[80, 160, 320, 640]
					if "tiny" in checkpoint_url:
										__a				:	List[Any]			=				[3, 3, 9, 3]
										__a				:	int			=				[96, 192, 384, 768]
					if "base" in checkpoint_url:
										__a				:	Dict			=				[3, 3, 27, 3]
										__a				:	List[Any]			=				[128, 256, 512, 1_024]
					if "large" in checkpoint_url:
										__a				:	Tuple			=				[3, 3, 27, 3]
										__a				:	Optional[int]			=				[192, 384, 768, 1_536]
					if "huge" in checkpoint_url:
										__a				:	Any			=				[3, 3, 27, 3]
										__a				:	Tuple			=				[352, 704, 1_408, 2_816]

					__a				:	Optional[Any]			=				1_000
					__a				:	Tuple			=				"imagenet-1k-id2label.json"
					__a				:	str			=				(1, 1_000)

					__a				:	List[Any]			=				"huggingface/label-files"
					__a				:	List[str]			=				num_labels
					__a				:	Any			=				json.load(open(hf_hub_download(__A							,					__A							,					repo_type="dataset"			)							,					"r"			)			)
					__a				:	Dict			=				{int(__A			): v for k, v in idalabel.items()}

					__a				:	str			=				idalabel
					__a				:	Dict			=				{v: k for k, v in idalabel.items()}
					__a				:	Optional[int]			=				hidden_sizes
					__a				:	Any			=				depths

					return config, expected_shape







def 						a_      (__A			)							->	int:



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

					return name







def 						a_      ()							->	int:



					"""simple docstring"""
					__a				:	str			=				"http://images.cocodataset.org/val2017/000000039769.jpg"
					__a				:	Tuple			=				Image.open(requests.get(__A							,					stream=__A			).raw			)
					return im







def 						a_      (__A			)							->	List[str]:



					"""simple docstring"""
					if "224" in checkpoint_url:
										__a				:	Tuple			=				224
										__a				:	Tuple			=				224 / 256
					elif "384" in checkpoint_url:
										__a				:	Tuple			=				384
										__a				:	Tuple			=				None
					else:
										__a				:	List[Any]			=				512
										__a				:	Optional[int]			=				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 						a_      (__A							,					__A							,					__A							,					__A			)							->	Dict:



					"""simple docstring"""
					print("Downloading original model from checkpoint..."			)
					# define ConvNeXTV2 configuration based on URL
					__a				:	List[str]			=				get_convnextva_config(__A			)
					# load original state_dict from URL
					__a				:	Optional[int]			=				torch.hub.load_state_dict_from_url(__A			)["model"]

					print("Converting model parameters..."			)
					# rename keys
					for key in state_dict.copy().keys():
										__a				:	Optional[int]			=				state_dict.pop(__A			)
										__a				:	Dict			=				val
					# add prefix to all keys expect classifier head
					for key in state_dict.copy().keys():
										__a				:	List[Any]			=				state_dict.pop(__A			)
										if not key.startswith("classifier"			):
															__a				:	str			=				"convnextv2." + key
										__a				:	Dict			=				val

					# load HuggingFace model
					__a				:	int			=				ConvNextVaForImageClassification(__A			)
					model.load_state_dict(__A			)
					model.eval()

					# Check outputs on an image, prepared by ConvNextImageProcessor
					__a				:	List[str]			=				convert_preprocessor(__A			)
					__a				:	List[str]			=				preprocessor(images=prepare_img()							,					return_tensors="pt"			)
					__a				:	Union[str, Any]			=				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":
										__a				:	Tuple			=				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":
										__a				:	Any			=				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":
										__a				:	List[Any]			=				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":
										__a				:	Any			=				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":
										__a				:	Dict			=				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":
										__a				:	Tuple			=				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":
										__a				:	Union[str, Any]			=				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":
										__a				:	List[Any]			=				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":
										__a				:	List[Any]			=				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":
										__a				:	Tuple			=				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":
										__a				:	int			=				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":
										__a				:	Optional[int]			=				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":
										__a				:	str			=				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":
										__a				:	Optional[Any]			=				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":
										__a				:	Any			=				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":
										__a				:	List[Any]			=				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":
										__a				:	Optional[Any]			=				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":
										__a				:	Union[str, Any]			=				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			)

					__a				:	int			=				"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__":
			UpperCAmelCase__			 =	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''')

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













import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput




@dataclass
class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			42
						snake_case__   					=			None
						snake_case__   					=			None




class        snake_case_					(						unittest.TestCase			):

						"""simple docstring"""



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Tuple:




											'''simple docstring'''



											__a				:	List[str]			=				ModelOutputTest(a=30      )
											self.assertEqual(x.a   ,				30      )
											self.assertIsNone(x.b      )
											self.assertIsNone(x.c      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Tuple			=				x.d



						def 					UpperCAmelCase__				(self:				Dict      )						->    Union[str, Any]:




											'''simple docstring'''



											__a				:	Union[str, Any]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(x[0]   ,				30      )
											self.assertEqual(x[1]   ,				10      )
											self.assertEqual(x[:2]   ,				(30, 10)      )
											self.assertEqual(x[:]   ,				(30, 10)      )

											__a				:	int			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(x[0]   ,				30      )
											self.assertEqual(x[1]   ,				10      )
											self.assertEqual(x[:2]   ,				(30, 10)      )
											self.assertEqual(x[:]   ,				(30, 10)      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    List[Any]:




											'''simple docstring'''



											__a				:	Optional[Any]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(x["a"]   ,				30      )
											self.assertEqual(x["b"]   ,				10      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	List[str]			=				x["c"]

											__a				:	Union[str, Any]			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(x["a"]   ,				30      )
											self.assertEqual(x["c"]   ,				10      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	int			=				x["b"]



						def 					UpperCAmelCase__				(self:				Union[str, Any]      )						->    Optional[int]:




											'''simple docstring'''



											__a				:	Union[str, Any]			=				ModelOutputTest(a=30      )
											self.assertEqual(list(x.keys()      )   ,				["a"]      )
											self.assertEqual(list(x.values()      )   ,				[30]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a"]      )

											__a				:	List[str]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(list(x.values()      )   ,				[30, 10]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30), ("b", 10)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a", "b"]      )

											__a				:	str			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(list(x.keys()      )   ,				["a", "c"]      )
											self.assertEqual(list(x.values()      )   ,				[30, 10]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30), ("c", 10)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a", "c"]      )

											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Dict			=				x.update({"d": 20}      )
											with self.assertRaises(__UpperCAmelCase      ):
																del x["a"]
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Any			=				x.pop("a"      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Any			=				x.setdefault("d"   ,				32      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Optional[Any]:




											'''simple docstring'''



											__a				:	Optional[Any]			=				ModelOutputTest(a=30      )
											__a				:	int			=				10
											self.assertEqual(x.a   ,				10      )
											self.assertEqual(x["a"]   ,				10      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Any:




											'''simple docstring'''



											__a				:	Optional[int]			=				ModelOutputTest(a=30      )
											__a				:	int			=				10
											self.assertEqual(x.a   ,				10      )
											self.assertEqual(x["a"]   ,				10      )



						def 					UpperCAmelCase__				(self:				List[str]      )						->    Any:




											'''simple docstring'''



											__a				:	Dict			=				ModelOutputTest({"a": 30, "b": 10}      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(x.a   ,				30      )
											self.assertEqual(x.b   ,				10      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    List[str]:




											'''simple docstring'''



											__a				:	List[Any]			=				ModelOutputTest([("a", 30), ("b", 10)]      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(x.a   ,				30      )
											self.assertEqual(x.b   ,				10      )

											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	List[Any]			=				ModelOutputTest([("a", 30), (10, 10)]      )

											__a				:	Optional[Any]			=				ModelOutputTest(a=(30, 30)      )
											self.assertEqual(list(x.keys()      )   ,				["a"]      )
											self.assertEqual(x.a   ,				(30, 30)      )





