


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


def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		ConvNextConfig()

						if "tiny" in checkpoint_url:
												__SCREAMING_SNAKE_CASE						:					List[str]		      =		[3, 3, 9, 3]
												__SCREAMING_SNAKE_CASE						:					int		      =		[96, 192, 384, 768]
						if "small" in checkpoint_url:
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		[3, 3, 27, 3]
												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		[96, 192, 384, 768]
						if "base" in checkpoint_url:
												__SCREAMING_SNAKE_CASE						:					List[str]		      =		[3, 3, 27, 3]
												__SCREAMING_SNAKE_CASE						:					Tuple		      =		[128, 256, 512, 1024]
						if "large" in checkpoint_url:
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		[3, 3, 27, 3]
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		[192, 384, 768, 1536]
						if "xlarge" in checkpoint_url:
												__SCREAMING_SNAKE_CASE						:					List[str]		      =		[3, 3, 27, 3]
												__SCREAMING_SNAKE_CASE						:					List[str]		      =		[256, 512, 1024, 2048]

						if "1k" in checkpoint_url:
												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		1000
												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		'''imagenet-1k-id2label.json'''
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		(1, 1000)
						else:
												__SCREAMING_SNAKE_CASE						:					int		      =		21841
												__SCREAMING_SNAKE_CASE						:					List[str]		      =		'''imagenet-22k-id2label.json'''
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		(1, 21841)

						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		'''huggingface/label-files'''
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		num_labels
						__SCREAMING_SNAKE_CASE						:					Any		      =		json.load(open(hf_hub_download(lowercase__   ,	lowercase__   ,	repo_type='''dataset'''				)   ,	'''r'''				)				)
						__SCREAMING_SNAKE_CASE						:					Dict		      =		{int(lowercase__				): 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[9205]
												del idalabel[15027]
						__SCREAMING_SNAKE_CASE						:					str		      =		idalabel
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		{v: k for k, v in idalabel.items()}
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		hidden_sizes
						__SCREAMING_SNAKE_CASE						:					Any		      =		depths

						return config, expected_shape


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

						return name


def 	_UpperCamelCase							(				):
						__SCREAMING_SNAKE_CASE						:					Dict		      =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__SCREAMING_SNAKE_CASE						:					Dict		      =		Image.open(requests.get(lowercase__   ,	stream=lowercase__				).raw				)
						return im


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

						# load HuggingFace model
						__SCREAMING_SNAKE_CASE						:					Dict		      =		ConvNextForImageClassification(lowercase__				)
						model.load_state_dict(lowercase__				)
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						__SCREAMING_SNAKE_CASE						:					Dict		      =		224 if '''224''' in checkpoint_url else 384
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		ConvNextImageProcessor(size=lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		image_processor(images=prepare_img()   ,	return_tensors='''pt'''				).pixel_values

						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model(lowercase__				).logits

						# note: the logits below were obtained without center cropping
						if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.tensor([-0.1210, -0.6605, 0.1918]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
												__SCREAMING_SNAKE_CASE						:					Dict		      =		torch.tensor([-0.4473, -0.1847, -0.6365]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
												__SCREAMING_SNAKE_CASE						:					Any		      =		torch.tensor([0.4525, 0.7539, 0.0308]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
												__SCREAMING_SNAKE_CASE						:					str		      =		torch.tensor([0.3561, 0.6350, -0.0384]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.tensor([0.4174, -0.0989, 0.1489]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
												__SCREAMING_SNAKE_CASE						:					int		      =		torch.tensor([0.2513, -0.1349, -0.1613]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
												__SCREAMING_SNAKE_CASE						:					int		      =		torch.tensor([1.2980, 0.3631, -0.1198]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
												__SCREAMING_SNAKE_CASE						:					Any		      =		torch.tensor([1.2963, 0.1227, 0.1723]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		torch.tensor([1.7956, 0.8390, 0.2820]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
												__SCREAMING_SNAKE_CASE						:					str		      =		torch.tensor([-0.2822, -0.0502, -0.0878]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
												__SCREAMING_SNAKE_CASE						:					int		      =		torch.tensor([-0.5672, -0.0730, -0.4348]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
												__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.tensor([0.2681, 0.2365, 0.6246]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
												__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.tensor([-0.2642, 0.3931, 0.5116]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.tensor([-0.6677, -0.1873, -0.8379]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		torch.tensor([-0.7749, -0.2967, -0.6444]				)
						else:
												raise ValueError(F'''Unknown URL: {checkpoint_url}'''				)

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

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

						print('''Pushing model to the hub...'''				)
						__SCREAMING_SNAKE_CASE						:					Any		      =		'''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(lowercase__   ,	lowercase__				)   ,	organization='''nielsr'''   ,	commit_message='''Add model'''   ,	)


if __name__ == "__main__":
						__lowerCAmelCase     :			Tuple      =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.',
						)

						__lowerCAmelCase     :			int      =parser.parse_args()
						convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




def 	_UpperCamelCase							(				lowercase__				):
						barcode //= 10  # exclude the last digit
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		False
						__SCREAMING_SNAKE_CASE						:					Dict		      =		0

						# extract and check each digit
						while barcode != 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		1 if checker else 3
												s += mult * (barcode % 10)
												barcode //= 10
												__SCREAMING_SNAKE_CASE						:					Dict		      =		not checker

						return (10 - (s % 10)) % 10


def 	_UpperCamelCase							(				lowercase__				):
						return len(str(lowercase__				)				) == 13 and get_check_digit(lowercase__				) == barcode % 10


def 	_UpperCamelCase							(				lowercase__				):
						if str(lowercase__				).isalpha():
												__SCREAMING_SNAKE_CASE						:					str		      =		F'''Barcode \'{barcode}\' has alphabetic characters.'''
												raise ValueError(lowercase__				)
						elif int(lowercase__				) < 0:
												raise ValueError('''The entered barcode has a negative value. Try again.'''				)
						else:
												return int(lowercase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase     :			Union[str, Any]      =get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(f"""'{barcode}' is a valid barcode.""")
						else:
												print(f"""'{barcode}' is NOT a valid barcode.""")
