
'''simple docstring'''





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__		: List[Any]  							=							logging.get_logger(__name__)





def 							snake_case				(		UpperCAmelCase						)->      List[str]:







      """simple docstring"""
      __A													=      ConvNextVaConfig()

      if "atto" in checkpoint_url:
            __A													=      [2, 2, 6, 2]
            __A													=      [4_0, 8_0, 1_6_0, 3_2_0]
      if "femto" in checkpoint_url:
            __A													=      [2, 2, 6, 2]
            __A													=      [4_8, 9_6, 1_9_2, 3_8_4]
      if "pico" in checkpoint_url:
            __A													=      [2, 2, 6, 2]
            __A													=      [6_4, 1_2_8, 2_5_6, 5_1_2]
      if "nano" in checkpoint_url:
            __A													=      [2, 2, 8, 2]
            __A													=      [8_0, 1_6_0, 3_2_0, 6_4_0]
      if "tiny" in checkpoint_url:
            __A													=      [3, 3, 9, 3]
            __A													=      [9_6, 1_9_2, 3_8_4, 7_6_8]
      if "base" in checkpoint_url:
            __A													=      [3, 3, 2_7, 3]
            __A													=      [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
      if "large" in checkpoint_url:
            __A													=      [3, 3, 2_7, 3]
            __A													=      [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
      if "huge" in checkpoint_url:
            __A													=      [3, 3, 2_7, 3]
            __A													=      [3_5_2, 7_0_4, 1_4_0_8, 2_8_1_6]

      __A													=      1_0_0_0
      __A													=      'imagenet-1k-id2label.json'
      __A													=      (1, 1_0_0_0)

      __A													=      'huggingface/label-files'
      __A													=      num_labels
      __A													=      json.load(open(hf_hub_download(UpperCAmelCase   ,	UpperCAmelCase   ,	repo_type='dataset'						)   ,	'r'						)						)
      __A													=      {int(UpperCAmelCase						): 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 							snake_case				(		UpperCAmelCase						)->      List[str]:







      """simple docstring"""
      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 							snake_case				(		)->      Union[str, Any]:







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





def 							snake_case				(		UpperCAmelCase						)->      Tuple:







      """simple docstring"""
      if "224" in checkpoint_url:
            __A													=      2_2_4
            __A													=      2_2_4 / 2_5_6
      elif "384" in checkpoint_url:
            __A													=      3_8_4
            __A													=      None
      else:
            __A													=      5_1_2
            __A													=      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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Any:







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

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

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

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

      __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(UpperCAmelCase						)
            preprocessor.push_to_hub(UpperCAmelCase						)


if __name__ == "__main__":
 a__		: 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__		: Any  							=							parser.parse_args()
 convert_convnextva_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
 )











UpperCAmelCase__  =			10**8





def 				A      (	_UpperCAmelCase							:  int       )							->					bool:







			'''simple docstring'''
			if n % 10 == 0:
						return False
			_UpperCAmelCase									=  str(_UpperCAmelCase       )
			return s == s[::-1]





def 				A      (	)							->					int:







			'''simple docstring'''
			_UpperCAmelCase									=  set()
			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  5
			while sum_squares < LIMIT:
						_UpperCAmelCase									=  first_square + 1
						while sum_squares < LIMIT:
									if is_palindrome(_UpperCAmelCase       ):
												answer.add(_UpperCAmelCase       )
									last_square += 1
									sum_squares += last_square**2
						first_square += 1
						_UpperCAmelCase									=  first_square**2 + (first_square + 1) ** 2

			return sum(_UpperCAmelCase       )


if __name__ == "__main__":
							print(solution())
