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()
_lowercase							:      Union[str, Any]      							=logging.get_logger(__name__)



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



def A__  (			lowercase:     Tuple					)				->		Tuple:
       A						:     List[str]         =['head.weight', 'head.bias']
       for k in ignore_keys:
              state_dict.pop(lowercase,		lowercase					)



def A__  (			lowercase:     Optional[int],		lowercase:     Optional[Any],		lowercase:     str					)				->		Any:
       A						:     str         =dct.pop(lowercase					)
       A						:     Tuple         =val



def A__  (			)				->		Optional[Any]:
       A						:     int         ='http://images.cocodataset.org/val2017/000000039769.jpg'
       A						:     List[str]         =Image.open(requests.get(lowercase,		stream=lowercase					).raw					)
       return im



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

       # load original model from torch hub
       A						:     List[str]         =torch.hub.load('facebookresearch/dino:main',		lowercase					)
       original_model.eval()

       # load state_dict of original model, remove and rename some keys
       A						:     int         =original_model.state_dict()
       if base_model:
              remove_classification_head_(lowercase					)
       A						:     Dict         =create_rename_keys(lowercase,		base_model=lowercase					)
       for src, dest in rename_keys:
              rename_key(lowercase,		lowercase,		lowercase					)
       read_in_q_k_v(lowercase,		lowercase,		lowercase					)

       # load HuggingFace model
       if base_model:
              A						:     List[str]         =ViTModel(lowercase,		add_pooling_layer=lowercase					).eval()
       else:
              A						:     Dict         =ViTForImageClassification(lowercase					).eval()
       model.load_state_dict(lowercase					)

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

       if base_model:
              A						:     Dict         =original_model(lowercase					)
              assert torch.allclose(lowercase,		outputs.last_hidden_state[:, 0, :],		atol=1e-1					)
       else:
              A						:     int         =original_model(lowercase					)
              assert logits.shape == outputs.logits.shape
              assert torch.allclose(lowercase,		outputs.logits,		atol=1e-3					)

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


if __name__ == "__main__":
    _lowercase							:      Union[str, 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)
    _lowercase							:      List[Any]      							=parser.parse_args()
    convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







import math



def A__  (			lowercase:     int					)				->		list:
       A						:     Optional[Any]         =[True] * n
       A						:     Tuple         =False
       A						:     List[Any]         =False
       A						:     Dict         =True

       for i in range(3,		int(n**0.5 + 1					),		2					):
              A						:     Dict         =i * 2
              while index < n:
                     A						:     Dict         =False
                     A						:     Dict         =index + i

       A						:     Tuple         =[2]

       for i in range(3,		lowercase,		2					):
              if is_prime[i]:
                     primes.append(lowercase					)

       return primes



def A__  (			lowercase:     int = 999_966_663_333					)				->		int:
       A						:     Optional[int]         =math.floor(math.sqrt(lowercase					)					) + 100
       A						:     Optional[int]         =prime_sieve(lowercase					)

       A						:     Optional[Any]         =0
       A						:     List[Any]         =0
       A						:     Union[str, Any]         =primes[prime_index]

       while (last_prime**2) <= limit:
              A						:     Tuple         =primes[prime_index + 1]

              A						:     Optional[int]         =last_prime**2
              A						:     Tuple         =next_prime**2

              # Get numbers divisible by lps(current)
              A						:     int         =lower_bound + last_prime
              while upper_bound > current <= limit:
                     matches_sum += current
                     current += last_prime

              # Reset the upper_bound
              while (upper_bound - next_prime) > limit:
                     upper_bound -= next_prime

              # Add the numbers divisible by ups(current)
              A						:     List[Any]         =upper_bound - next_prime
              while current > lower_bound:
                     matches_sum += current
                     current -= next_prime

              # Remove the numbers divisible by both ups and lps
              A						:     Any         =0
              while upper_bound > current <= limit:
                     if current <= lower_bound:
                            # Increment the current number
                            current += last_prime * next_prime
                            continue

                     if current > limit:
                            break

                     # Remove twice since it was added by both ups and lps
                     matches_sum -= current * 2

                     # Increment the current number
                     current += last_prime * next_prime

              # Setup for next pair
              A						:     List[str]         =next_prime
              prime_index += 1

       return matches_sum


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






