


'''simple docstring'''


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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__			=      logging.get_logger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Union[str, Any]=False):
      A_						:  Optional[int]          =				[]
      for i in range(config.num_hidden_layers):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight'))
            rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias'))
            rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight'))
            rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias'))
            rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight'))
            rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias'))
            rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight'))
            rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias'))
            rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight'))
            rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias'))

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("""cls_token""", """vit.embeddings.cls_token"""),
              ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
              ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
              ("""pos_embed""", """vit.embeddings.position_embeddings"""),
          ])

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ("""norm.weight""", """layernorm.weight"""),
                    ("""norm.bias""", """layernorm.bias"""),
                ])

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            A_						:  int          =				[(pair[0], pair[1][4:]) if pair[1].startswith("""vit""") else pair for pair in rename_keys]
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ("""norm.weight""", """vit.layernorm.weight"""),
                    ("""norm.bias""", """vit.layernorm.bias"""),
                    ("""head.weight""", """classifier.weight"""),
                    ("""head.bias""", """classifier.bias"""),
                ])

      return rename_keys




def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      str							,						lowerCamelCase  :      List[Any]=False):
      for i in range(config.num_hidden_layers):
            if base_model:
                  A_						:  str          =				""""""
            else:
                  A_						:  Any          =				"""vit."""
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            A_						:  Optional[int]          =				state_dict.pop(F'blocks.{i}.attn.qkv.weight')
            A_						:  Tuple          =				state_dict.pop(F'blocks.{i}.attn.qkv.bias')
            # next, add query, keys and values (in that order) to the state dict
            A_						:  List[Any]          =				in_proj_weight[
                : config.hidden_size, :
            ]
            A_						:  str          =				in_proj_bias[: config.hidden_size]
            A_						:  Tuple          =				in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            A_						:  Tuple          =				in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            A_						:  Union[str, Any]          =				in_proj_weight[
                -config.hidden_size :, :
            ]
            A_						:  Tuple          =				in_proj_bias[-config.hidden_size :]




def      lowerCamelCase					(      lowerCamelCase  :      int):
      A_						:  Union[str, Any]          =				["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(lowerCamelCase							,						lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      Dict							,						lowerCamelCase  :      int):
      A_						:  List[str]          =				dct.pop(lowerCamelCase)
      A_						:  Tuple          =				val




def      lowerCamelCase					(      ):
      A_						:  Any          =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      A_						:  Optional[Any]          =				Image.open(requests.get(lowerCamelCase							,						stream=lowerCamelCase).raw)
      return im




@torch.no_grad()
def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Union[str, Any]=True):
      A_						:  Any          =				ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            A_						:  Tuple          =				8
      # set labels if required
      if not base_model:
            A_						:  str          =				1000
            A_						:  Any          =				"""huggingface/label-files"""
            A_						:  Optional[int]          =				"""imagenet-1k-id2label.json"""
            A_						:  Dict          =				json.load(open(hf_hub_download(lowerCamelCase							,						lowerCamelCase							,						repo_type="""dataset""")							,						"""r"""))
            A_						:  Any          =				{int(lowerCamelCase): v for k, v in idalabel.items()}
            A_						:  List[str]          =				idalabel
            A_						:  int          =				{v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            A_						:  Any          =				384
            A_						:  Tuple          =				1536
            A_						:  Union[str, Any]          =				12
            A_						:  Union[str, Any]          =				6

      # load original model from torch hub
      A_						:  Tuple          =				torch.hub.load("""facebookresearch/dino:main"""							,						lowerCamelCase)
      original_model.eval()

      # load state_dict of original model, remove and rename some keys
      A_						:  Tuple          =				original_model.state_dict()
      if base_model:
            remove_classification_head_(lowerCamelCase)
      A_						:  Optional[Any]          =				create_rename_keys(lowerCamelCase							,						base_model=lowerCamelCase)
      for src, dest in rename_keys:
            rename_key(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)
      read_in_q_k_v(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)

      # load HuggingFace model
      if base_model:
            A_						:  Tuple          =				ViTModel(lowerCamelCase							,						add_pooling_layer=lowerCamelCase).eval()
      else:
            A_						:  Union[str, Any]          =				ViTForImageClassification(lowerCamelCase).eval()
      model.load_state_dict(lowerCamelCase)

      # Check outputs on an image, prepared by ViTImageProcessor
      A_						:  Tuple          =				ViTImageProcessor()
      A_						:  str          =				image_processor(images=prepare_img()							,						return_tensors="""pt""")
      A_						:  List[Any]          =				encoding["""pixel_values"""]
      A_						:  Tuple          =				model(lowerCamelCase)

      if base_model:
            A_						:  str          =				original_model(lowerCamelCase)
            assert torch.allclose(lowerCamelCase							,						outputs.last_hidden_state[:, 0, :]							,						atol=1E-1)
      else:
            A_						:  int          =				original_model(lowerCamelCase)
            assert logits.shape == outputs.logits.shape
            assert torch.allclose(lowerCamelCase							,						outputs.logits							,						atol=1E-3)

      Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
      print(F'Saving model {model_name} 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__":
     __magic_name__			=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='dino_vitb16',
         type=str,
         help='Name of the model trained with DINO you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--base_model',
         action='store_true',
         help='Whether to only convert the base model (no projection head weights).',
     )

     parser.set_defaults(base_model=True)
     __magic_name__			=      parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)




'''simple docstring'''


from __future__ import annotations

import math




def      lowerCamelCase					(      lowerCamelCase  :      int):

      if num <= 0:
            A_						:  List[Any]          =				F'{num}: Invalid input, please enter a positive integer.'
            raise ValueError(lowerCamelCase)

      A_						:  str          =				[True] * (num + 1)
      A_						:  Tuple          =				[]
      A_						:  str          =				2
      A_						:  Any          =				int(math.sqrt(lowerCamelCase))

      while start <= end:
            # If start is a prime
            if sieve[start] is True:
                  prime.append(lowerCamelCase)

                  # Set multiples of start be False
                  for i in range(start * start							,						num + 1							,						lowerCamelCase):
                        if sieve[i] is True:
                              A_						:  Union[str, Any]          =				False

            start += 1

      for j in range(end + 1							,						num + 1):
            if sieve[j] is True:
                  prime.append(lowerCamelCase)

      return prime


if __name__ == "__main__":
     print(prime_sieve(int(input('Enter a positive integer: ').strip())))
