





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__    :      List[str]       					=logging.get_logger(__name__)





def   _lowercase       (    _UpperCAmelCase					)    ->	int:
				lowerCamelCase									=ConvNextVaConfig()

				if "atto" in checkpoint_url:
								lowerCamelCase									=[2, 2, 6, 2]
								lowerCamelCase									=[40, 80, 1_60, 3_20]
				if "femto" in checkpoint_url:
								lowerCamelCase									=[2, 2, 6, 2]
								lowerCamelCase									=[48, 96, 1_92, 3_84]
				if "pico" in checkpoint_url:
								lowerCamelCase									=[2, 2, 6, 2]
								lowerCamelCase									=[64, 1_28, 2_56, 5_12]
				if "nano" in checkpoint_url:
								lowerCamelCase									=[2, 2, 8, 2]
								lowerCamelCase									=[80, 1_60, 3_20, 6_40]
				if "tiny" in checkpoint_url:
								lowerCamelCase									=[3, 3, 9, 3]
								lowerCamelCase									=[96, 1_92, 3_84, 7_68]
				if "base" in checkpoint_url:
								lowerCamelCase									=[3, 3, 27, 3]
								lowerCamelCase									=[1_28, 2_56, 5_12, 10_24]
				if "large" in checkpoint_url:
								lowerCamelCase									=[3, 3, 27, 3]
								lowerCamelCase									=[1_92, 3_84, 7_68, 15_36]
				if "huge" in checkpoint_url:
								lowerCamelCase									=[3, 3, 27, 3]
								lowerCamelCase									=[3_52, 7_04, 14_08, 28_16]

				lowerCamelCase									=10_00
				lowerCamelCase									="""imagenet-1k-id2label.json"""
				lowerCamelCase									=(1, 10_00)

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

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

				return config, expected_shape





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

				return name





def   _lowercase       (    )    ->	Dict:
				lowerCamelCase									="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowerCamelCase									=Image.open(requests.get(_UpperCAmelCase      ,   stream=_UpperCAmelCase					).raw					)
				return im





def   _lowercase       (    _UpperCAmelCase					)    ->	List[str]:
				if "224" in checkpoint_url:
								lowerCamelCase									=2_24
								lowerCamelCase									=2_24 / 2_56
				elif "384" in checkpoint_url:
								lowerCamelCase									=3_84
								lowerCamelCase									=None
				else:
								lowerCamelCase									=5_12
								lowerCamelCase									=None

				return ConvNextImageProcessor(
				    size=_UpperCAmelCase      ,   crop_pct=_UpperCAmelCase      ,   image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]      ,   image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]      ,   resample=PILImageResampling.BICUBIC      ,   )





@torch.no_grad()
def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Union[str, Any]:
				print("""Downloading original model from checkpoint..."""					)
				# define ConvNeXTV2 configuration based on URL
				lowerCamelCase			,	lowerCamelCase									=get_convnextva_config(_UpperCAmelCase					)
				# load original state_dict from URL
				lowerCamelCase									=torch.hub.load_state_dict_from_url(_UpperCAmelCase					)["""model"""]

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

				# load HuggingFace model
				lowerCamelCase									=ConvNextVaForImageClassification(_UpperCAmelCase					)
				model.load_state_dict(_UpperCAmelCase					)
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				lowerCamelCase									=convert_preprocessor(_UpperCAmelCase					)
				lowerCamelCase									=preprocessor(images=prepare_img()      ,   return_tensors="""pt"""					)
				lowerCamelCase									=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":
								lowerCamelCase									=torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
								lowerCamelCase									=torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
								lowerCamelCase									=torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
								lowerCamelCase									=torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
								lowerCamelCase									=torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
								lowerCamelCase									=torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
								lowerCamelCase									=torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
								lowerCamelCase									=torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
								lowerCamelCase									=torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
								lowerCamelCase									=torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
								lowerCamelCase									=torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
								lowerCamelCase									=torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
								lowerCamelCase									=torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
								lowerCamelCase									=torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
								lowerCamelCase									=torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
								lowerCamelCase									=torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
								lowerCamelCase									=torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]					)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
								lowerCamelCase									=torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]					)
				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					)

				lowerCamelCase									="""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__":
							UpperCAmelCase__    :      Any       					=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__    :      Any       					=parser.parse_args()
							convert_convnextva_checkpoint(
							    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
							)












def   _lowercase       (    _UpperCAmelCase					)    ->	bool:
				if not isinstance(_UpperCAmelCase      ,   _UpperCAmelCase					):
								lowerCamelCase									=F"""Input value of [number={number}] must be an integer"""
								raise TypeError(_UpperCAmelCase					)
				if number < 0 or number % 2 == 1:
								return False
				lowerCamelCase									=int(number**0.5					)
				return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





