





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       :					Optional[Any]     					=     logging.get_logger(__name__)





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       Tuple:







			'''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, 1024]
			if "large" in checkpoint_url:
						UpperCAmelCase     	=       [3, 3, 27, 3]
						UpperCAmelCase     	=       [192, 384, 768, 1536]
			if "huge" in checkpoint_url:
						UpperCAmelCase     	=       [3, 3, 27, 3]
						UpperCAmelCase     	=       [352, 704, 1408, 2816]

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

			UpperCAmelCase     	=       '''huggingface/label-files'''
			UpperCAmelCase     	=       num_labels
			UpperCAmelCase     	=       json.load(open(hf_hub_download(UpperCamelCase__    ,    UpperCamelCase__    ,    repo_type='''dataset'''				)    ,    '''r'''				)				)
			UpperCAmelCase     	=       {int(UpperCamelCase__				): 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 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       Any:







			'''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 		__SCREAMING_SNAKE_CASE						(       )						->       Optional[Any]:







			'''simple docstring'''

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





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       List[Any]:







			'''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=UpperCamelCase__    ,    crop_pct=UpperCamelCase__    ,    image_mean=[0.485, 0.456, 0.406]    ,    image_std=[0.229, 0.224, 0.225]    ,    resample=PILImageResampling.BICUBIC    ,    )





@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       Dict:







			'''simple docstring'''

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

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

			# load HuggingFace model
			UpperCAmelCase     	=       ConvNextVaForImageClassification(UpperCamelCase__				)
			model.load_state_dict(UpperCamelCase__				)
			model.eval()

			# Check outputs on an image, prepared by ConvNextImageProcessor
			UpperCAmelCase     	=       convert_preprocessor(UpperCamelCase__				)
			UpperCAmelCase     	=       preprocessor(images=prepare_img()    ,    return_tensors='''pt'''				)
			UpperCAmelCase     	=       model(**UpperCamelCase__				).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([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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":
						UpperCAmelCase     	=       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]    ,    UpperCamelCase__    ,    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(UpperCamelCase__				):
									os.mkdir(UpperCamelCase__				)

						model.save_pretrained(UpperCamelCase__				)
						preprocessor.save_pretrained(UpperCamelCase__				)

			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(UpperCamelCase__				)
						preprocessor.push_to_hub(UpperCamelCase__				)


if __name__ == "__main__":
				__A       :					List[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")

				__A       :					Union[str, Any]     					=     parser.parse_args()
				convert_convnextva_checkpoint(
				    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
				)










import numpy as np





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       int:







			'''simple docstring'''

			UpperCAmelCase     	=       n_draws * class_counts / class_counts.sum()
			# floored means we don't overshoot n_samples, but probably undershoot
			UpperCAmelCase     	=       np.floor(UpperCamelCase__				)
			# we add samples according to how much "left over" probability
			# they had, until we arrive at n_samples
			UpperCAmelCase     	=       int(n_draws - floored.sum()				)
			if need_to_add > 0:
						UpperCAmelCase     	=       continuous - floored
						UpperCAmelCase     	=       np.sort(np.unique(UpperCamelCase__				)				)[::-1]
						# add according to remainder, but break ties
						# randomly to avoid biases
						for value in values:
									((UpperCAmelCase)	,    )     	=       np.where(remainder == value				)
									# if we need_to_add less than what's in inds
									# we draw randomly from them.
									# if we need to add more, we add them all and
									# go to the next value
									UpperCAmelCase     	=       min(len(UpperCamelCase__				)    ,    UpperCamelCase__				)
									UpperCAmelCase     	=       rng.choice(UpperCamelCase__    ,    size=UpperCamelCase__    ,    replace=UpperCamelCase__				)
									floored[inds] += 1
									need_to_add -= add_now
									if need_to_add == 0:
												break
			return floored.astype(np.intaa				)






def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__=10				)						->       Any:







			'''simple docstring'''

			UpperCAmelCase	,    UpperCAmelCase     	=       np.unique(UpperCamelCase__    ,    return_inverse=UpperCamelCase__				)
			UpperCAmelCase     	=       classes.shape[0]
			UpperCAmelCase     	=       np.bincount(UpperCamelCase__				)
			if np.min(UpperCamelCase__				) < 2:
						raise ValueError('''Minimum class count error'''				)
			if n_train < n_classes:
						raise ValueError(
						    '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)				)
			if n_test < n_classes:
						raise ValueError(
						    '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)				)
			UpperCAmelCase     	=       np.split(np.argsort(UpperCamelCase__    ,    kind='''mergesort'''				)    ,    np.cumsum(UpperCamelCase__				)[:-1]				)
			for _ in range(UpperCamelCase__				):
						UpperCAmelCase     	=       approximate_mode(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)
						UpperCAmelCase     	=       class_counts - n_i
						UpperCAmelCase     	=       approximate_mode(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)

						UpperCAmelCase     	=       []
						UpperCAmelCase     	=       []

						for i in range(UpperCamelCase__				):
									UpperCAmelCase     	=       rng.permutation(class_counts[i]				)
									UpperCAmelCase     	=       class_indices[i].take(UpperCamelCase__    ,    mode='''clip'''				)
									train.extend(perm_indices_class_i[: n_i[i]]				)
									test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]				)
						UpperCAmelCase     	=       rng.permutation(UpperCamelCase__				)
						UpperCAmelCase     	=       rng.permutation(UpperCamelCase__				)

						yield train, test



