
"""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()
A__   :					Dict							=   logging.get_logger(__name__)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=False						):






  """simple docstring"""



  _lowercase:			List[Any]       =     []
  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"
    _lowercase:			Dict       =     [(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		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase=False						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     ['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(_UpperCamelCase ,				_UpperCamelCase						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			int       =     dct.pop(_UpperCamelCase						)
  _lowercase:			str       =     val




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			List[str]       =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _lowercase:			Union[str, Any]       =     Image.open(requests.get(_UpperCamelCase ,				stream=_UpperCamelCase						).raw						)
  return im




@torch.no_grad()
def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase=True						):






  """simple docstring"""



  _lowercase:			Dict       =     ViTConfig()
  # patch_size
  if model_name[-1] == "8":
    _lowercase:			Optional[Any]       =     8
  # set labels if required
  if not base_model:
    _lowercase:			List[str]       =     1_000
    _lowercase:			List[Any]       =     '''huggingface/label-files'''
    _lowercase:			Optional[int]       =     '''imagenet-1k-id2label.json'''
    _lowercase:			Dict       =     json.load(open(hf_hub_download(_UpperCamelCase ,				_UpperCamelCase ,				repo_type='''dataset'''						) ,				'''r'''						)						)
    _lowercase:			int       =     {int(_UpperCamelCase						): v for k, v in idalabel.items()}
    _lowercase:			Optional[int]       =     idalabel
    _lowercase:			List[Any]       =     {v: k for k, v in idalabel.items()}
  # size of the architecture
  if model_name in ["dino_vits8", "dino_vits16"]:
    _lowercase:			Optional[Any]       =     384
    _lowercase:			List[Any]       =     1_536
    _lowercase:			str       =     12
    _lowercase:			Optional[Any]       =     6

  # load original model from torch hub
  _lowercase:			Tuple       =     torch.hub.load('''facebookresearch/dino:main''' ,				_UpperCamelCase						)
  original_model.eval()

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

  # load HuggingFace model
  if base_model:
    _lowercase:			Optional[int]       =     ViTModel(_UpperCamelCase ,				add_pooling_layer=_UpperCamelCase						).eval()
  else:
    _lowercase:			Optional[Any]       =     ViTForImageClassification(_UpperCamelCase						).eval()
  model.load_state_dict(_UpperCamelCase						)

  # Check outputs on an image, prepared by ViTImageProcessor
  _lowercase:			Optional[Any]       =     ViTImageProcessor()
  _lowercase:			Dict       =     image_processor(images=prepare_img() ,				return_tensors='''pt'''						)
  _lowercase:			List[Any]       =     encoding['''pixel_values''']
  _lowercase:			List[str]       =     model(_UpperCamelCase						)

  if base_model:
    _lowercase:			Optional[Any]       =     original_model(_UpperCamelCase						)
    assert torch.allclose(_UpperCamelCase ,				outputs.last_hidden_state[:, 0, :] ,				atol=1e-1						)
  else:
    _lowercase:			Union[str, Any]       =     original_model(_UpperCamelCase						)
    assert logits.shape == outputs.logits.shape
    assert torch.allclose(_UpperCamelCase ,				outputs.logits ,				atol=1e-3						)

  Path(_UpperCamelCase						).mkdir(exist_ok=_UpperCamelCase						)
  print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''						)
  model.save_pretrained(_UpperCamelCase						)
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
  image_processor.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      A__   :					List[Any]							=   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)
      A__   :					Optional[int]							=   parser.parse_args()
      convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if num <= 0:
    raise ValueError('''Input must be a positive integer'''						)

  _lowercase:			Tuple       =     [True] * (num + 1)

  _lowercase:			List[str]       =     2
  while p * p <= num:
    if primes[p]:
      for i in range(p * p ,				num + 1 ,				_UpperCamelCase						):
        _lowercase:			List[str]       =     False
    p += 1

  return [prime for prime in range(2 ,				num + 1						) if primes[prime]]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  A__   :					List[Any]							=   int(input('Enter a positive integer: ').strip())
  print(prime_sieve_eratosthenes(user_num))
