




'''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()
_lowerCamelCase				:		str				      =    logging.get_logger(__name__)






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			ConvNextVaConfig()

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

		_snake_case      			=			1000
		_snake_case      			=			"imagenet-1k-id2label.json"
		_snake_case      			=			(1, 1000)

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

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

		return config, expected_shape






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







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

		return name






def 						_lowercase						(      ):



		'''simple docstring'''







		_snake_case      			=			"http://images.cocodataset.org/val2017/000000039769.jpg"
		_snake_case      			=			Image.open(requests.get(lowercase_		,				stream=lowercase_    ).raw    )
		return im






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		if "224" in checkpoint_url:
				_snake_case      			=			224
				_snake_case      			=			224 / 256
		elif "384" in checkpoint_url:
				_snake_case      			=			384
				_snake_case      			=			None
		else:
				_snake_case      			=			512
				_snake_case      			=			None

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






@torch.no_grad()
def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







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

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

		# load HuggingFace model
		_snake_case      			=			ConvNextVaForImageClassification(lowercase_    )
		model.load_state_dict(lowercase_    )
		model.eval()

		# Check outputs on an image, prepared by ConvNextImageProcessor
		_snake_case      			=			convert_preprocessor(lowercase_    )
		_snake_case      			=			preprocessor(images=prepare_img()		,				return_tensors="pt"    )
		_snake_case      			=			model(**lowercase_    ).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      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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":
				_snake_case      			=			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]		,				lowercase_		,				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(lowercase_    ):
						os.mkdir(lowercase_    )

				model.save_pretrained(lowercase_    )
				preprocessor.save_pretrained(lowercase_    )

		_snake_case      			=			"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(lowercase_    )
				preprocessor.push_to_hub(lowercase_    )


if __name__ == "__main__":
						_lowerCamelCase				:		Dict				      =    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				:		int				      =    parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)











'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

_lowerCamelCase				:		Optional[Any]				      =    {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					"""swiftformer"""






					def __init__(       self,    __UpperCamelCase=3,    __UpperCamelCase=[3, 3, 6, 4],    __UpperCamelCase=[48, 56, 112, 220],    __UpperCamelCase=4,    __UpperCamelCase=[True, True, True, True],    __UpperCamelCase="gelu",    __UpperCamelCase=3,    __UpperCamelCase=2,    __UpperCamelCase=1,    __UpperCamelCase=0.0,    __UpperCamelCase=True,    __UpperCamelCase=1E-5,    __UpperCamelCase=1E-5,    **__UpperCamelCase,    ):
							super().__init__(**__UpperCamelCase					)
							_snake_case      			=			num_channels
							_snake_case      			=			depths
							_snake_case      			=			embed_dims
							_snake_case      			=			mlp_ratio
							_snake_case      			=			downsamples
							_snake_case      			=			hidden_act
							_snake_case      			=			down_patch_size
							_snake_case      			=			down_stride
							_snake_case      			=			down_pad
							_snake_case      			=			drop_path_rate
							_snake_case      			=			use_layer_scale
							_snake_case      			=			layer_scale_init_value
							_snake_case      			=			batch_norm_eps





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					version.parse("""1.11"""				)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return 1E-4





