
import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)


def 					__lowerCAmelCase    (    a__		,					a__=False		,					a__=False		,					a__="data2vec."						) ->						Any:
				__a    							=					'''backbone.''' if is_semantic else ''''''

				__a    							=					[]
				for i in range(config.num_hidden_layers						):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (F"""{prefix}blocks.{i}.norm1.weight""", F"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")						)
								rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")						)
								rename_keys.append(
								    (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")						)
								rename_keys.append(
								    (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")						)
								rename_keys.append(
								    (F"""{prefix}blocks.{i}.norm2.weight""", F"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")						)
								rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")						)
								rename_keys.append(
								    (F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")						)
								rename_keys.append(
								    (F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")						)
								rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")						)
								rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")						)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (F"""{prefix}cls_token""", F"""{hf_prefix}embeddings.cls_token"""),
				        (F"""{prefix}patch_embed.proj.weight""", F"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
				        (F"""{prefix}patch_embed.proj.bias""", F"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
				    ]						)

				if has_lm_head:
								# mask token + shared relative position bias + layernorm
								rename_keys.extend(
								    [
								        ('''mask_token''', F"""{hf_prefix}embeddings.mask_token"""),
								        (
								            '''rel_pos_bias.relative_position_bias_table''',
								            F"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
								        ),
								        (
								            '''rel_pos_bias.relative_position_index''',
								            F"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
								        ),
								        ('''norm.weight''', '''layernorm.weight'''),
								        ('''norm.bias''', '''layernorm.bias'''),
								    ]						)
				elif is_semantic:
								# semantic segmentation classification heads
								rename_keys.extend(
								    [
								        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
								        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
								        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
								        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
								    ]						)
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ('''fc_norm.weight''', F"""{hf_prefix}pooler.layernorm.weight"""),
								        ('''fc_norm.bias''', F"""{hf_prefix}pooler.layernorm.bias"""),
								        ('''head.weight''', '''classifier.weight'''),
								        ('''head.bias''', '''classifier.bias'''),
								    ]						)

				return rename_keys


def 					__lowerCAmelCase    (    a__		,					a__		,					a__=False		,					a__=False		,					a__="data2vec_vision."						) ->						Any:
				for i in range(config.num_hidden_layers						):
								__a    							=					'''backbone.''' if is_semantic else ''''''
								# queries, keys and values
								__a    							=					state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""						)
								__a    							=					state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""						)
								__a    							=					state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""						)

								__a    							=					in_proj_weight[
								    : config.hidden_size, :
								]
								__a    							=					q_bias
								__a    							=					in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__a    							=					in_proj_weight[
								    -config.hidden_size :, :
								]
								__a    							=					v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								__a    							=					state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""						)
								__a    							=					state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""						)

								__a    							=					gamma_a
								__a    							=					gamma_a

								# relative_position bias table + index
								if not has_lm_head:
												# each layer has its own relative position bias
												__a    							=					state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""						)
												__a    							=					state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""						)

												__a    							=					table
												__a    							=					index


def 					__lowerCAmelCase    (    ) ->						Dict:
				__a    							=					argparse.ArgumentParser(
				    '''Convert Data2VecVision to HF for image classification and pretraining'''		,					add_help=a__						)
				parser.add_argument('''--hf_checkpoint_name'''		,					type=a__						)
				parser.add_argument('''--input_size'''		,					default=224		,					type=a__		,					help='''images input size'''						)
				parser.add_argument('''--beit_checkpoint'''		,					default=''''''		,					help='''beit checkpoint'''						)

				return parser.parse_args()


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						Any:
				def load_state_dict(a__		,					a__		,					a__=""		,					a__="relative_position_index"						):
								__a    							=					[]
								__a    							=					[]
								__a    							=					[]
								# copy state_dict so _load_from_state_dict can modify it
								__a    							=					getattr(a__		,					'''_metadata'''		,					a__						)
								__a    							=					state_dict.copy()
								if metadata is not None:
												__a    							=					metadata

								def load(a__		,					a__=""						):
												__a    							=					{} if metadata is None else metadata.get(prefix[:-1]		,					{}						)
												module._load_from_state_dict(
												    a__		,					a__		,					a__		,					a__		,					a__		,					a__		,					a__						)
												for name, child in module._modules.items():
																if child is not None:
																				load(a__		,					prefix + name + '''.'''						)

								load(a__		,					prefix=a__						)

								__a    							=					[]
								__a    							=					[]
								for key in missing_keys:
												__a    							=					True
												for ignore_key in ignore_missing.split('''|'''						):
																if ignore_key in key:
																				__a    							=					False
																				break
												if keep_flag:
																warn_missing_keys.append(a__						)
												else:
																ignore_missing_keys.append(a__						)

								__a    							=					warn_missing_keys

								if len(a__						) > 0:
												print(
												    '''Weights of {} not initialized from pretrained model: {}'''.format(
												        model.__class__.__name__		,					a__						)						)
								if len(a__						) > 0:
												print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__		,					a__						)						)
								if len(a__						) > 0:
												print(
												    '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
												        model.__class__.__name__		,					a__						)						)
								if len(a__						) > 0:
												print('''\n'''.join(a__						)						)

				__a    							=					{
				    '''pretrained''': False,
				    '''use_shared_rel_pos_bias''': True,
				    '''use_abs_pos_emb''': False,
				    '''init_values''': 0.1,
				}

				if is_finetuned:
								model_kwargs.update(
								    {
								        '''num_classes''': 1000,
								        '''use_mean_pooling''': True,
								        '''init_scale''': 0.001,
								        '''use_rel_pos_bias''': True,
								    }						)

				__a    							=					create_model(
				    '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''		,					**a__		,					)
				__a    							=					model.patch_embed.patch_size
				__a    							=					(args.input_size // patch_size[0], args.input_size // patch_size[1])
				__a    							=					torch.load(args.beit_checkpoint		,					map_location='''cpu'''						)

				print(F"""Load ckpt from {args.beit_checkpoint}"""						)
				__a    							=					None
				for model_key in ("model", "module"):
								if model_key in checkpoint:
												__a    							=					checkpoint[model_key]
												print(F"""Load state_dict by model_key = {model_key}"""						)
												break

				__a    							=					list(checkpoint_model.keys()						)
				for key in all_keys:
								if "relative_position_index" in key:
												checkpoint_model.pop(a__						)

								if "relative_position_bias_table" in key:
												__a    							=					checkpoint_model[key]
												__a							,  __a    							=					rel_pos_bias.size()
												__a							,  __a    							=					model.state_dict()[key].size()
												__a    							=					model.patch_embed.patch_shape
												if dst_patch_shape[0] != dst_patch_shape[1]:
																raise NotImplementedError()

				load_state_dict(a__		,					a__		,					prefix=''''''						)

				return model


def 					__lowerCAmelCase    (    ) ->						Tuple:
				__a    							=					get_args()

				__a    							=					'''ft1k''' in args.hf_checkpoint_name
				__a    							=					'''large''' in args.hf_checkpoint_name

				if is_finetuned:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
								# into this folder.
								import modeling_finetune  # noqa: F401
				else:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
								# into this folder
								# IMPORTANT: Note that for now we've only converted the down-stream
								# model and not the full pretrained model. This means for the integration
								# test you need to add a `return x` after the following line:
								# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
								# to make the integration test pass.
								import modeling_cyclical  # noqa: F401

				# 1. Create model config
				__a    							=					DataaVecVisionConfig()
				if is_finetuned:
								__a    							=					True
								__a    							=					False
								__a    							=					True
								__a    							=					1000

								__a    							=					'''huggingface/label-files'''
								__a    							=					'''imagenet-1k-id2label.json'''
								__a    							=					json.load(open(hf_hub_download(a__		,					a__		,					repo_type='''dataset'''						)		,					'''r'''						)						)
								__a    							=					{int(a__						): v for k, v in idalabel.items()}
								__a    							=					idalabel
								__a    							=					{v: k for k, v in idalabel.items()}
				else:
								__a    							=					False
								__a    							=					True
								__a    							=					False

				if is_large:
								__a    							=					1024
								__a    							=					4096
								__a    							=					24
								__a    							=					16

				# 2. Load Beit model
				__a    							=					load_beit_model(a__		,					a__		,					a__						)
				orig_model.eval()

				# 3. Forward Beit model
				__a    							=					BeitImageProcessor(size=config.image_size		,					do_center_crop=a__						)
				__a    							=					Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''						)
				__a    							=					image_processor(images=a__		,					return_tensors='''pt'''						)
				__a    							=					encoding['''pixel_values''']

				__a    							=					(pixel_values,) if is_finetuned else (pixel_values, None)
				with torch.no_grad():
								__a    							=					orig_model(*a__						)

				# 4. Load HF Data2VecVision model
				if is_finetuned:
								__a    							=					DataaVecVisionForImageClassification(a__						)
								hf_model.eval()
								__a    							=					False
								__a    							=					'''data2vec_vision.'''
				else:
								__a    							=					DataaVecVisionModel(a__						)
								hf_model.eval()
								__a    							=					True
								__a    							=					''''''

				__a    							=					create_rename_keys(a__		,					hf_prefix=a__		,					has_lm_head=a__						)
				__a    							=					orig_model.state_dict()
				for src, dest in rename_keys:
								__a    							=					state_dict.pop(a__						)
								__a    							=					val

				read_in_q_k_v(a__		,					a__		,					hf_prefix=a__		,					has_lm_head=a__						)
				__a							,  __a    							=					hf_model.load_state_dict(a__		,					strict=a__						)
				print('''HF missing'''		,					a__						)
				print('''HF unexpected_keys'''		,					a__						)

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								__a    							=					hf_model(a__						)

				__a    							=					hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

				# 6. Compare
				__a    							=					torch.max(torch.abs(hf_output - orig_model_output						)						).item()

				print(F"""max_absolute_diff = {max_absolute_diff}"""						)
				__a    							=					torch.allclose(a__		,					a__		,					atol=1e-3						)
				print('''Do both models output the same tensors?'''		,					'''🔥''' if success else '''💩'''						)
				if not success:
								raise Exception('''Something went wRoNg'''						)

				# 7. Save
				print(F"""Saving to {args.hf_checkpoint_name}"""						)
				hf_model.save_pretrained(args.hf_checkpoint_name						)
				image_processor.save_pretrained(args.hf_checkpoint_name						)


if __name__ == "__main__":
					main()
					# Run the following to convert checkpoints
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./pretrained_base.pt \
					#          --hf_checkpoint_name "./data2vec-vision-base"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./finetuned_base.pt \
					#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./pretrained_large.pt \
					#          --hf_checkpoint_name "./data2vec-vision-large"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./finetuned_large.pt \
					#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"

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()
A  :			Tuple     				=				logging.get_logger(__name__)


def 					__lowerCAmelCase    (    a__						) ->						Optional[int]:
				__a    							=					ConvNextVaConfig()

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

				__a    							=					1000
				__a    							=					'''imagenet-1k-id2label.json'''
				__a    							=					(1, 1000)

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

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

				return config, expected_shape


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

				return name


def 					__lowerCAmelCase    (    ) ->						Tuple:
				__a    							=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__a    							=					Image.open(requests.get(a__		,					stream=a__						).raw						)
				return im


def 					__lowerCAmelCase    (    a__						) ->						List[str]:
				if "224" in checkpoint_url:
								__a    							=					224
								__a    							=					224 / 256
				elif "384" in checkpoint_url:
								__a    							=					384
								__a    							=					None
				else:
								__a    							=					512
								__a    							=					None

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


@torch.no_grad()
def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						List[Any]:
				print('''Downloading original model from checkpoint...'''						)
				# define ConvNeXTV2 configuration based on URL
				__a							,  __a    							=					get_convnextva_config(a__						)
				# load original state_dict from URL
				__a    							=					torch.hub.load_state_dict_from_url(a__						)['''model''']

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

				# load HuggingFace model
				__a    							=					ConvNextVaForImageClassification(a__						)
				model.load_state_dict(a__						)
				model.eval()

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

								model.save_pretrained(a__						)
								preprocessor.save_pretrained(a__						)

				__a    							=					'''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(a__						)
								preprocessor.push_to_hub(a__						)


if __name__ == "__main__":
					A  :			Optional[int]     				=				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')

					A  :			int     				=				parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)