





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()
lowerCAmelCase_												=       logging.get_logger(__name__)






def   lowerCamelCase_		(       _UpperCamelCase				)	->		Any:






				"""simple docstring"""


				snake_case_		:					Optional[Any]			    =						ConvNextVaConfig()

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

				snake_case_		:					Optional[Any]			    =						1_000
				snake_case_		:					str			    =						'''imagenet-1k-id2label.json'''
				snake_case_		:					Optional[int]			    =						(1, 1_000)

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

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

				return config, expected_shape






def   lowerCamelCase_		(       _UpperCamelCase				)	->		Any:






				"""simple docstring"""


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

				return name






def   lowerCamelCase_		(       )	->		Union[str, Any]:






				"""simple docstring"""


				snake_case_		:					Tuple			    =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				snake_case_		:					List[Any]			    =						Image.open(requests.get(_UpperCamelCase				,       stream=_UpperCamelCase				).raw				)
				return im






def   lowerCamelCase_		(       _UpperCamelCase				)	->		Optional[int]:






				"""simple docstring"""


				if "224" in checkpoint_url:
								snake_case_		:					str			    =						224
								snake_case_		:					int			    =						224 / 256
				elif "384" in checkpoint_url:
								snake_case_		:					List[str]			    =						384
								snake_case_		:					str			    =						None
				else:
								snake_case_		:					List[Any]			    =						512
								snake_case_		:					Tuple			    =						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				)	->		Tuple:






				"""simple docstring"""


				print('''Downloading original model from checkpoint...'''				)
				# define ConvNeXTV2 configuration based on URL
				snake_case_    ,							snake_case_		:					Union[str, Any]			    =						get_convnextva_config(_UpperCamelCase				)
				# load original state_dict from URL
				snake_case_		:					Optional[int]			    =						torch.hub.load_state_dict_from_url(_UpperCamelCase				)['''model''']

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

				# load HuggingFace model
				snake_case_		:					Tuple			    =						ConvNextVaForImageClassification(_UpperCamelCase				)
				model.load_state_dict(_UpperCamelCase				)
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				snake_case_		:					int			    =						convert_preprocessor(_UpperCamelCase				)
				snake_case_		:					Dict			    =						preprocessor(images=prepare_img()				,       return_tensors='''pt'''				)
				snake_case_		:					List[Any]			    =						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":
								snake_case_		:					str			    =						torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
								snake_case_		:					List[Any]			    =						torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
								snake_case_		:					Any			    =						torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
								snake_case_		:					Optional[int]			    =						torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
								snake_case_		:					Optional[Any]			    =						torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
								snake_case_		:					Any			    =						torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
								snake_case_		:					Any			    =						torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
								snake_case_		:					List[Any]			    =						torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
								snake_case_		:					Tuple			    =						torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
								snake_case_		:					List[str]			    =						torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
								snake_case_		:					List[Any]			    =						torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
								snake_case_		:					Tuple			    =						torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
								snake_case_		:					Union[str, Any]			    =						torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
								snake_case_		:					Union[str, Any]			    =						torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
								snake_case_		:					List[Any]			    =						torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
								snake_case_		:					Union[str, Any]			    =						torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
								snake_case_		:					Optional[int]			    =						torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]				)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
								snake_case_		:					Union[str, Any]			    =						torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]				)
				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				)

				snake_case_		:					Union[str, Any]			    =						'''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__":
	lowerCAmelCase_												=       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''')

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







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


lowerCAmelCase_												=       logging.get_logger(__name__)



class       __lowerCAmelCase (    _a  ):






			def __init__(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     None:


							'''simple docstring'''



							warnings.warn(
							    '''The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
							    ''' use LevitImageProcessor instead.'''      ,							__magic_name__      ,							)
							super().__init__(*__magic_name__      ,							**__magic_name__					)
