





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase		:     Tuple 					=logging.get_logger(__name__)


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		lowerCamelCase_  :    Dict   	=			ConvNextConfig()

		if "tiny" in checkpoint_url:
				lowerCamelCase_  :    Tuple   	=			[3, 3, 9, 3]
				lowerCamelCase_  :    List[Any]   	=			[96, 1_92, 3_84, 7_68]
		if "small" in checkpoint_url:
				lowerCamelCase_  :    List[Any]   	=			[3, 3, 27, 3]
				lowerCamelCase_  :    Tuple   	=			[96, 1_92, 3_84, 7_68]
		if "base" in checkpoint_url:
				lowerCamelCase_  :    Any   	=			[3, 3, 27, 3]
				lowerCamelCase_  :    Union[str, Any]   	=			[1_28, 2_56, 5_12, 10_24]
		if "large" in checkpoint_url:
				lowerCamelCase_  :    Optional[Any]   	=			[3, 3, 27, 3]
				lowerCamelCase_  :    Union[str, Any]   	=			[1_92, 3_84, 7_68, 15_36]
		if "xlarge" in checkpoint_url:
				lowerCamelCase_  :    List[str]   	=			[3, 3, 27, 3]
				lowerCamelCase_  :    str   	=			[2_56, 5_12, 10_24, 20_48]

		if "1k" in checkpoint_url:
				lowerCamelCase_  :    Optional[Any]   	=			10_00
				lowerCamelCase_  :    Union[str, Any]   	=			'imagenet-1k-id2label.json'
				lowerCamelCase_  :    Optional[Any]   	=			(1, 10_00)
		else:
				lowerCamelCase_  :    Optional[int]   	=			2_18_41
				lowerCamelCase_  :    Optional[Any]   	=			'imagenet-22k-id2label.json'
				lowerCamelCase_  :    Tuple   	=			(1, 2_18_41)

		lowerCamelCase_  :    int   	=			'huggingface/label-files'
		lowerCamelCase_  :    Optional[int]   	=			num_labels
		lowerCamelCase_  :    List[Any]   	=			json.load(open(hf_hub_download(lowerCAmelCase__       ,lowerCAmelCase__       ,repo_type='dataset'	)       ,'r'	)	)
		lowerCamelCase_  :    List[Any]   	=			{int(lowerCAmelCase__	): v for k, v in idalabel.items()}
		if "1k" not in checkpoint_url:
				# this dataset contains 21843 labels but the model only has 21841
				# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
				del idalabel[92_05]
				del idalabel[1_50_27]
		lowerCamelCase_  :    Dict   	=			idalabel
		lowerCamelCase_  :    int   	=			{v: k for k, v in idalabel.items()}
		lowerCamelCase_  :    Union[str, Any]   	=			hidden_sizes
		lowerCamelCase_  :    List[Any]   	=			depths

		return config, expected_shape


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

		return name


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    Union[str, Any]   	=			'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase_  :    Optional[int]   	=			Image.open(requests.get(lowerCAmelCase__       ,stream=lowerCAmelCase__	).raw	)
		return im


@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    int   	=			get_convnext_config(lowerCAmelCase__	)
		# load original state_dict from URL
		lowerCamelCase_  :    Optional[int]   	=			torch.hub.load_state_dict_from_url(lowerCAmelCase__	)['model']
		# rename keys
		for key in state_dict.copy().keys():
				lowerCamelCase_  :    List[Any]   	=			state_dict.pop(lowerCAmelCase__	)
				lowerCamelCase_  :    List[str]   	=			val
		# add prefix to all keys expect classifier head
		for key in state_dict.copy().keys():
				lowerCamelCase_  :    Optional[int]   	=			state_dict.pop(lowerCAmelCase__	)
				if not key.startswith('classifier'	):
						lowerCamelCase_  :    Union[str, Any]   	=			'convnext.' + key
				lowerCamelCase_  :    List[Any]   	=			val

		# load HuggingFace model
		lowerCamelCase_  :    List[Any]   	=			ConvNextForImageClassification(lowerCAmelCase__	)
		model.load_state_dict(lowerCAmelCase__	)
		model.eval()

		# Check outputs on an image, prepared by ConvNextImageProcessor
		lowerCamelCase_  :    Dict   	=			2_24 if '224' in checkpoint_url else 3_84
		lowerCamelCase_  :    Union[str, Any]   	=			ConvNextImageProcessor(size=lowerCAmelCase__	)
		lowerCamelCase_  :    Dict   	=			image_processor(images=prepare_img()       ,return_tensors='pt'	).pixel_values

		lowerCamelCase_  :    List[Any]   	=			model(lowerCAmelCase__	).logits

		# note: the logits below were obtained without center cropping
		if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
				lowerCamelCase_  :    List[str]   	=			torch.tensor([-0.1_210, -0.6_605, 0.1_918]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
				lowerCamelCase_  :    Dict   	=			torch.tensor([-0.4_473, -0.1_847, -0.6_365]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
				lowerCamelCase_  :    Any   	=			torch.tensor([0.4_525, 0.7_539, 0.0_308]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
				lowerCamelCase_  :    List[str]   	=			torch.tensor([0.3_561, 0.6_350, -0.0_384]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
				lowerCamelCase_  :    Dict   	=			torch.tensor([0.4_174, -0.0_989, 0.1_489]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
				lowerCamelCase_  :    Optional[Any]   	=			torch.tensor([0.2_513, -0.1_349, -0.1_613]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
				lowerCamelCase_  :    List[Any]   	=			torch.tensor([1.2_980, 0.3_631, -0.1_198]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
				lowerCamelCase_  :    Optional[int]   	=			torch.tensor([1.2_963, 0.1_227, 0.1_723]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
				lowerCamelCase_  :    str   	=			torch.tensor([1.7_956, 0.8_390, 0.2_820]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
				lowerCamelCase_  :    Tuple   	=			torch.tensor([-0.2_822, -0.0_502, -0.0_878]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
				lowerCamelCase_  :    Union[str, Any]   	=			torch.tensor([-0.5_672, -0.0_730, -0.4_348]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
				lowerCamelCase_  :    Optional[Any]   	=			torch.tensor([0.2_681, 0.2_365, 0.6_246]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
				lowerCamelCase_  :    Optional[int]   	=			torch.tensor([-0.2_642, 0.3_931, 0.5_116]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
				lowerCamelCase_  :    Optional[int]   	=			torch.tensor([-0.6_677, -0.1_873, -0.8_379]	)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
				lowerCamelCase_  :    Optional[int]   	=			torch.tensor([-0.7_749, -0.2_967, -0.6_444]	)
		else:
				raise ValueError(F"Unknown URL: {checkpoint_url}"	)

		assert torch.allclose(logits[0, :3]       ,lowerCAmelCase__       ,atol=1e-3	)
		assert logits.shape == expected_shape

		Path(lowerCAmelCase__	).mkdir(exist_ok=lowerCAmelCase__	)
		print(F"Saving model to {pytorch_dump_folder_path}"	)
		model.save_pretrained(lowerCAmelCase__	)
		print(F"Saving image processor to {pytorch_dump_folder_path}"	)
		image_processor.save_pretrained(lowerCAmelCase__	)

		print('Pushing model to the hub...'	)
		lowerCamelCase_  :    int   	=			'convnext'
		if "tiny" in checkpoint_url:
				model_name += "-tiny"
		elif "small" in checkpoint_url:
				model_name += "-small"
		elif "base" in checkpoint_url:
				model_name += "-base"
		elif "xlarge" in checkpoint_url:
				model_name += "-xlarge"
		elif "large" in checkpoint_url:
				model_name += "-large"
		if "224" in checkpoint_url:
				model_name += "-224"
		elif "384" in checkpoint_url:
				model_name += "-384"
		if "22k" in checkpoint_url and "1k" not in checkpoint_url:
				model_name += "-22k"
		if "22k" in checkpoint_url and "1k" in checkpoint_url:
				model_name += "-22k-1k"

		model.push_to_hub(
		    repo_path_or_name=Path(lowerCAmelCase__       ,lowerCAmelCase__	)       ,organization='nielsr'       ,commit_message='Add model'       ,)


if __name__ == "__main__":
		_lowercase		:     int 					=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--checkpoint_url""",
		    default="""https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth""",
		    type=str,
		    help="""URL of the original ConvNeXT checkpoint you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to the output PyTorch model directory.""",
		)

		_lowercase		:     List[Any] 					=parser.parse_args()
		convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase		:     Dict 					=logging.get_logger(__name__)

_lowercase		:     Any 					={
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}






class 	UpperCamelCase_     (     snake_case__       ):
					_a			:	List[str]							=				'vilt'



					def __init__(		self    :    int					,			lowerCamelCase    :    Any=3_05_22					,			lowerCamelCase    :    Union[str, Any]=2					,			lowerCamelCase    :    Optional[Any]=2					,			lowerCamelCase    :    str=40					,			lowerCamelCase    :    List[str]=7_68					,			lowerCamelCase    :    Optional[int]=12					,			lowerCamelCase    :    List[Any]=12					,			lowerCamelCase    :    List[str]=30_72					,			lowerCamelCase    :    Union[str, Any]="gelu"					,			lowerCamelCase    :    int=0.0					,			lowerCamelCase    :    str=0.0					,			lowerCamelCase    :    int=0.02					,			lowerCamelCase    :    Union[str, Any]=1E-12					,			lowerCamelCase    :    Dict=3_84					,			lowerCamelCase    :    List[str]=32					,			lowerCamelCase    :    Dict=3					,			lowerCamelCase    :    List[str]=True					,			lowerCamelCase    :    int=-1					,			lowerCamelCase    :    List[Any]=False					,			lowerCamelCase    :    Any=-1					,			**lowerCamelCase    :    str					,			):
							super().__init__(tie_word_embeddings=lowerCamelCase					,			**lowerCamelCase	)

							lowerCamelCase_  :    str   	=			vocab_size
							lowerCamelCase_  :    Dict   	=			type_vocab_size
							lowerCamelCase_  :    str   	=			modality_type_vocab_size
							lowerCamelCase_  :    Optional[Any]   	=			max_position_embeddings

							lowerCamelCase_  :    List[Any]   	=			hidden_size
							lowerCamelCase_  :    Union[str, Any]   	=			num_hidden_layers
							lowerCamelCase_  :    Any   	=			num_attention_heads
							lowerCamelCase_  :    List[Any]   	=			intermediate_size
							lowerCamelCase_  :    Union[str, Any]   	=			hidden_act
							lowerCamelCase_  :    Any   	=			hidden_dropout_prob
							lowerCamelCase_  :    Optional[int]   	=			attention_probs_dropout_prob
							lowerCamelCase_  :    Any   	=			initializer_range
							lowerCamelCase_  :    str   	=			layer_norm_eps

							lowerCamelCase_  :    Optional[Any]   	=			image_size
							lowerCamelCase_  :    List[Any]   	=			patch_size
							lowerCamelCase_  :    Tuple   	=			num_channels
							lowerCamelCase_  :    Optional[Any]   	=			qkv_bias
							lowerCamelCase_  :    Optional[int]   	=			max_image_length
							lowerCamelCase_  :    Optional[Any]   	=			num_images
