
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   :					Tuple    =			logging.get_logger(__name__)


def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	ConvNextVaConfig()

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

		UpperCamelCase            =	1_000
		UpperCamelCase            =	'imagenet-1k-id2label.json'
		UpperCamelCase            =	(1, 1_000)

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

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

		return config, expected_shape


def  A			(   lowercase					)  ->    Tuple:
		'''simple docstring'''

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

		return name


def  A			(   )  ->    Dict:
		'''simple docstring'''

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


def  A			(   lowercase					)  ->    Tuple:
		'''simple docstring'''

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

		return ConvNextImageProcessor(
		    size=lowercase       ,			crop_pct=lowercase       ,			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  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase					)  ->    Any:
		'''simple docstring'''

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

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

		# load HuggingFace model
		UpperCamelCase            =	ConvNextVaForImageClassification(lowercase					)
		model.load_state_dict(lowercase					)
		model.eval()

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

		UpperCamelCase            =	'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__":
					_UpperCAmelCase   :					Optional[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   :					int    =			parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase   :					int    =			{"configuration_levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig", "LevitOnnxConfig"]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Optional[Any]    =			["LevitFeatureExtractor"]
					_UpperCAmelCase   :					Optional[int]    =			["LevitImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Optional[int]    =			[
					    "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "LevitForImageClassification",
					    "LevitForImageClassificationWithTeacher",
					    "LevitModel",
					    "LevitPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_levit import LevitFeatureExtractor
										from .image_processing_levit import LevitImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_levit import (
										    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LevitForImageClassification,
										    LevitForImageClassificationWithTeacher,
										    LevitModel,
										    LevitPreTrainedModel,
										)
else:
					import sys

					_UpperCAmelCase   :					Optional[Any]    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
