

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


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







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :Tuple=False		):


       """simple docstring"""




       __snake_case       	=							[]
       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"""),
                      ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                      ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
                  ]		)

              # if just the base model, we should remove "vit" from all keys that start with "vit"
              __snake_case       	=							[(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__      ( __A    :Dict	,__A    :int	,__A    :List[str]=False		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							["""head.weight""", """head.bias"""]
       for k in ignore_keys:
              state_dict.pop(__A	,__A		)







def 						lowerCamelCase__      ( __A    :str	,__A    :List[Any]	,__A    :Dict		):


       """simple docstring"""




       __snake_case       	=							dct.pop(__A		)
       __snake_case       	=							val







def 						lowerCamelCase__      ( ):


       """simple docstring"""




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







@torch.no_grad()
def 						lowerCamelCase__      ( __A    :List[Any]	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							ViTConfig()
       __snake_case       	=							False
       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       if vit_name[-5:] == "in21k":
              __snake_case       	=							True
              __snake_case       	=							int(vit_name[-1_2:-1_0]		)
              __snake_case       	=							int(vit_name[-9:-6]		)
       else:
              __snake_case       	=							1_0_0_0
              __snake_case       	=							"""huggingface/label-files"""
              __snake_case       	=							"""imagenet-1k-id2label.json"""
              __snake_case       	=							json.load(open(hf_hub_download(__A	,__A	,repo_type="""dataset"""		)	,"""r"""		)		)
              __snake_case       	=							{int(__A		): v for k, v in idalabel.items()}
              __snake_case       	=							idalabel
              __snake_case       	=							{v: k for k, v in idalabel.items()}
              __snake_case       	=							int(vit_name[-6:-4]		)
              __snake_case       	=							int(vit_name[-3:]		)
       # size of the architecture
       if "deit" in vit_name:
              if vit_name[9:].startswith("""tiny"""		):
                     __snake_case       	=							1_9_2
                     __snake_case       	=							7_6_8
                     __snake_case       	=							1_2
                     __snake_case       	=							3
              elif vit_name[9:].startswith("""small"""		):
                     __snake_case       	=							3_8_4
                     __snake_case       	=							1_5_3_6
                     __snake_case       	=							1_2
                     __snake_case       	=							6
              else:
                     pass
       else:
              if vit_name[4:].startswith("""small"""		):
                     __snake_case       	=							7_6_8
                     __snake_case       	=							2_3_0_4
                     __snake_case       	=							8
                     __snake_case       	=							8
              elif vit_name[4:].startswith("""base"""		):
                     pass
              elif vit_name[4:].startswith("""large"""		):
                     __snake_case       	=							1_0_2_4
                     __snake_case       	=							4_0_9_6
                     __snake_case       	=							2_4
                     __snake_case       	=							1_6
              elif vit_name[4:].startswith("""huge"""		):
                     __snake_case       	=							1_2_8_0
                     __snake_case       	=							5_1_2_0
                     __snake_case       	=							3_2
                     __snake_case       	=							1_6

    # load original model from timm
       __snake_case       	=							timm.create_model(__A	,pretrained=__A		)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       __snake_case       	=							timm_model.state_dict()
       if base_model:
              remove_classification_head_(__A		)
       __snake_case       	=							create_rename_keys(__A	,__A		)
       for src, dest in rename_keys:
              rename_key(__A	,__A	,__A		)
       read_in_q_k_v(__A	,__A	,__A		)

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              __snake_case       	=							ViTModel(__A		).eval()
       else:
              __snake_case       	=							ViTForImageClassification(__A		).eval()
       model.load_state_dict(__A		)

       # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
       if "deit" in vit_name:
              __snake_case       	=							DeiTImageProcessor(size=config.image_size		)
       else:
              __snake_case       	=							ViTImageProcessor(size=config.image_size		)
       __snake_case       	=							image_processor(images=prepare_img()	,return_tensors="""pt"""		)
       __snake_case       	=							encoding["""pixel_values"""]
       __snake_case       	=							model(__A		)

       if base_model:
              __snake_case       	=							timm_model.forward_features(__A		)
              assert timm_pooled_output.shape == outputs.pooler_output.shape
              assert torch.allclose(__A	,outputs.pooler_output	,atol=1e-3		)
       else:
              __snake_case       	=							timm_model(__A		)
              assert timm_logits.shape == outputs.logits.shape
              assert torch.allclose(__A	,outputs.logits	,atol=1e-3		)

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


if __name__ == "__main__":
    UpperCamelCase__		 =     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--vit_name''',
        default='''vit_base_patch16_224''',
        type=str,
        help='''Name of the ViT timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    UpperCamelCase__		 =     parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




from math import isqrt, loga







def 						lowerCamelCase__      ( __A    :int		):


       """simple docstring"""




       __snake_case       	=							[True] * max_number
       for i in range(2	,isqrt(max_number - 1		) + 1		):
              if is_prime[i]:
                     for j in range(i**2	,__A	,__A		):
                            __snake_case       	=							False

       return [i for i in range(2	,__A		) if is_prime[i]]







def 						lowerCamelCase__      ( __A    :int = 8_0_0_8_0_0	,__A    :int = 8_0_0_8_0_0		):


       """simple docstring"""




       __snake_case       	=							degree * loga(__A		)
       __snake_case       	=							int(__A		)
       __snake_case       	=							calculate_prime_numbers(__A		)

       __snake_case       	=							0
       __snake_case       	=							0
       __snake_case       	=							len(__A		) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]		)
                  + prime_numbers[left] * loga(prime_numbers[right]		)
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


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

