





import math


def  A							(			snake_case__   :						Dict				)					->				Any:
 '''simple docstring'''


 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5 ,				int(math.sqrt(snake_case__				) + 1				) ,				6				):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True


def  A							(			snake_case__   :						List[Any] = 1_0001				)					->				List[str]:
 '''simple docstring'''


 try:
  __snake_case         =			int(snake_case__				)
 except (TypeError, ValueError):
  raise TypeError('Parameter nth must be int or castable to int.'				) from None
 if nth <= 0:
  raise ValueError('Parameter nth must be greater than or equal to one.'				)
 __snake_case         =			[]
 __snake_case         =			2
 while len(snake_case__				) < nth:
  if is_prime(snake_case__				):
   primes.append(snake_case__				)
   num += 1
  else:
   num += 1
 return primes[len(snake_case__				) - 1]


if __name__ == "__main__":
  print(F"""{solution() = }""")








'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if "cls_token" in name:
            _lowerCAmelCase					=				name.replace("""cls_token"""				,      """vit.embeddings.cls_token"""    )
      if "mask_token" in name:
            _lowerCAmelCase					=				name.replace("""mask_token"""				,      """decoder.mask_token"""    )
      if "decoder_pos_embed" in name:
            _lowerCAmelCase					=				name.replace("""decoder_pos_embed"""				,      """decoder.decoder_pos_embed"""    )
      if "pos_embed" in name and "decoder" not in name:
            _lowerCAmelCase					=				name.replace("""pos_embed"""				,      """vit.embeddings.position_embeddings"""    )
      if "patch_embed.proj" in name:
            _lowerCAmelCase					=				name.replace("""patch_embed.proj"""				,      """vit.embeddings.patch_embeddings.projection"""    )
      if "patch_embed.norm" in name:
            _lowerCAmelCase					=				name.replace("""patch_embed.norm"""				,      """vit.embeddings.norm"""    )
      if "decoder_blocks" in name:
            _lowerCAmelCase					=				name.replace("""decoder_blocks"""				,      """decoder.decoder_layers"""    )
      if "blocks" in name:
            _lowerCAmelCase					=				name.replace("""blocks"""				,      """vit.encoder.layer"""    )
      if "attn.proj" in name:
            _lowerCAmelCase					=				name.replace("""attn.proj"""				,      """attention.output.dense"""    )
      if "attn" in name:
            _lowerCAmelCase					=				name.replace("""attn"""				,      """attention.self"""    )
      if "norm1" in name:
            _lowerCAmelCase					=				name.replace("""norm1"""				,      """layernorm_before"""    )
      if "norm2" in name:
            _lowerCAmelCase					=				name.replace("""norm2"""				,      """layernorm_after"""    )
      if "mlp.fc1" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc1"""				,      """intermediate.dense"""    )
      if "mlp.fc2" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc2"""				,      """output.dense"""    )
      if "decoder_embed" in name:
            _lowerCAmelCase					=				name.replace("""decoder_embed"""				,      """decoder.decoder_embed"""    )
      if "decoder_norm" in name:
            _lowerCAmelCase					=				name.replace("""decoder_norm"""				,      """decoder.decoder_norm"""    )
      if "decoder_pred" in name:
            _lowerCAmelCase					=				name.replace("""decoder_pred"""				,      """decoder.decoder_pred"""    )
      if "norm.weight" in name and "decoder" not in name:
            _lowerCAmelCase					=				name.replace("""norm.weight"""				,      """vit.layernorm.weight"""    )
      if "norm.bias" in name and "decoder" not in name:
            _lowerCAmelCase					=				name.replace("""norm.bias"""				,      """vit.layernorm.bias"""    )

      return name
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      for key in orig_state_dict.copy().keys():
            _lowerCAmelCase					=				orig_state_dict.pop(lowerCAmelCase    )

            if "qkv" in key:
                  _lowerCAmelCase					=				key.split("""."""    )
                  _lowerCAmelCase					=				int(key_split[1]    )
                  if "decoder_blocks" in key:
                        _lowerCAmelCase					=				config.decoder_hidden_size
                        _lowerCAmelCase					=				"""decoder.decoder_layers."""
                        if "weight" in key:
                              _lowerCAmelCase					=				val[:dim, :]
                              _lowerCAmelCase					=				val[dim : dim * 2, :]
                              _lowerCAmelCase					=				val[-dim:, :]
                        elif "bias" in key:
                              _lowerCAmelCase					=				val[:dim]
                              _lowerCAmelCase					=				val[dim : dim * 2]
                              _lowerCAmelCase					=				val[-dim:]
                  else:
                        _lowerCAmelCase					=				config.hidden_size
                        _lowerCAmelCase					=				"""vit.encoder.layer."""
                        if "weight" in key:
                              _lowerCAmelCase					=				val[:dim, :]
                              _lowerCAmelCase					=				val[dim : dim * 2, :]
                              _lowerCAmelCase					=				val[-dim:, :]
                        elif "bias" in key:
                              _lowerCAmelCase					=				val[:dim]
                              _lowerCAmelCase					=				val[dim : dim * 2]
                              _lowerCAmelCase					=				val[-dim:]

            else:
                  _lowerCAmelCase					=				val

      return orig_state_dict
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				ViTMAEConfig()
      if "large" in checkpoint_url:
            _lowerCAmelCase					=				10_24
            _lowerCAmelCase					=				40_96
            _lowerCAmelCase					=				24
            _lowerCAmelCase					=				16
      elif "huge" in checkpoint_url:
            _lowerCAmelCase					=				14
            _lowerCAmelCase					=				12_80
            _lowerCAmelCase					=				51_20
            _lowerCAmelCase					=				32
            _lowerCAmelCase					=				16

      _lowerCAmelCase					=				ViTMAEForPreTraining(lowerCAmelCase    )

      _lowerCAmelCase					=				torch.hub.load_state_dict_from_url(lowerCAmelCase				,      map_location="""cpu"""    )["""model"""]

      _lowerCAmelCase					=				ViTMAEImageProcessor(size=config.image_size    )

      _lowerCAmelCase					=				convert_state_dict(lowerCAmelCase				,      lowerCAmelCase    )

      model.load_state_dict(lowerCAmelCase    )
      model.eval()

      _lowerCAmelCase					=				"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

      _lowerCAmelCase					=				Image.open(requests.get(lowerCAmelCase				,      stream=lowerCAmelCase    ).raw    )
      _lowerCAmelCase					=				ViTMAEImageProcessor(size=config.image_size    )
      _lowerCAmelCase					=				image_processor(images=lowerCAmelCase				,      return_tensors="""pt"""    )

      # forward pass
      torch.manual_seed(2    )
      _lowerCAmelCase					=				model(**lowerCAmelCase    )
      _lowerCAmelCase					=				outputs.logits

      if "large" in checkpoint_url:
            _lowerCAmelCase					=				torch.tensor(
                [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]    )
      elif "huge" in checkpoint_url:
            _lowerCAmelCase					=				torch.tensor(
                [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]    )
      else:
            _lowerCAmelCase					=				torch.tensor(
                [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]    )

      # verify logits
      assert torch.allclose(logits[0, :3, :3]				,      lowerCAmelCase				,      atol=1e-4    )

      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    )


if __name__ == "__main__":
      A__							:  Dict    =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_url''',
          default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
          type=str,
          help='''URL of the checkpoint you\'d like to convert.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      A__							:  List[Any]    =parser.parse_args()
      convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






