





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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a__: List[str]							   =						logging.get_logger(__name__)


def       UpperCamelCase__(			UpperCamelCase__     :			str    )->YolosConfig:
       A__		   =		YolosConfig()

       # size of the architecture
       if "yolos_ti" in yolos_name:
              A__		   =		1_92
              A__		   =		7_68
              A__		   =		12
              A__		   =		3
              A__		   =		[8_00, 13_33]
              A__		   =		False
       elif yolos_name == "yolos_s_dWr":
              A__		   =		3_30
              A__		   =		14
              A__		   =		6
              A__		   =		13_20
       elif "yolos_s" in yolos_name:
              A__		   =		3_84
              A__		   =		15_36
              A__		   =		12
              A__		   =		6
       elif "yolos_b" in yolos_name:
              A__		   =		[8_00, 13_44]

       A__		   =		91
       A__		   =		'''huggingface/label-files'''
       A__		   =		'''coco-detection-id2label.json'''
       A__		   =		json.load(open(hf_hub_download(UpperCamelCase__					,	UpperCamelCase__					,	repo_type='''dataset'''    )					,	'''r'''    )    )
       A__		   =		{int(UpperCamelCase__    ): v for k, v in idalabel.items()}
       A__		   =		idalabel
       A__		   =		{v: k for k, v in idalabel.items()}

       return config


def       UpperCamelCase__(			UpperCamelCase__     :			dict					,	UpperCamelCase__     :			YolosConfig					,	UpperCamelCase__     :			bool = False    )->Dict:
       for i in range(config.num_hidden_layers    ):
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              A__		   =		state_dict.pop(f"blocks.{i}.attn.qkv.weight"    )
              A__		   =		state_dict.pop(f"blocks.{i}.attn.qkv.bias"    )
              # next, add query, keys and values (in that order) to the state dict
              A__		   =		in_proj_weight[: config.hidden_size, :]
              A__		   =		in_proj_bias[: config.hidden_size]
              A__		   =		in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              A__		   =		in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              A__		   =		in_proj_weight[-config.hidden_size :, :]
              A__		   =		in_proj_bias[-config.hidden_size :]


def       UpperCamelCase__(			UpperCamelCase__     :			str    )->str:
       if "backbone" in name:
              A__		   =		name.replace('''backbone'''					,	'''vit'''    )
       if "cls_token" in name:
              A__		   =		name.replace('''cls_token'''					,	'''embeddings.cls_token'''    )
       if "det_token" in name:
              A__		   =		name.replace('''det_token'''					,	'''embeddings.detection_tokens'''    )
       if "mid_pos_embed" in name:
              A__		   =		name.replace('''mid_pos_embed'''					,	'''encoder.mid_position_embeddings'''    )
       if "pos_embed" in name:
              A__		   =		name.replace('''pos_embed'''					,	'''embeddings.position_embeddings'''    )
       if "patch_embed.proj" in name:
              A__		   =		name.replace('''patch_embed.proj'''					,	'''embeddings.patch_embeddings.projection'''    )
       if "blocks" in name:
              A__		   =		name.replace('''blocks'''					,	'''encoder.layer'''    )
       if "attn.proj" in name:
              A__		   =		name.replace('''attn.proj'''					,	'''attention.output.dense'''    )
       if "attn" in name:
              A__		   =		name.replace('''attn'''					,	'''attention.self'''    )
       if "norm1" in name:
              A__		   =		name.replace('''norm1'''					,	'''layernorm_before'''    )
       if "norm2" in name:
              A__		   =		name.replace('''norm2'''					,	'''layernorm_after'''    )
       if "mlp.fc1" in name:
              A__		   =		name.replace('''mlp.fc1'''					,	'''intermediate.dense'''    )
       if "mlp.fc2" in name:
              A__		   =		name.replace('''mlp.fc2'''					,	'''output.dense'''    )
       if "class_embed" in name:
              A__		   =		name.replace('''class_embed'''					,	'''class_labels_classifier'''    )
       if "bbox_embed" in name:
              A__		   =		name.replace('''bbox_embed'''					,	'''bbox_predictor'''    )
       if "vit.norm" in name:
              A__		   =		name.replace('''vit.norm'''					,	'''vit.layernorm'''    )

       return name


def       UpperCamelCase__(			UpperCamelCase__     :			dict					,	UpperCamelCase__     :			YolosForObjectDetection    )->dict:
       for key in orig_state_dict.copy().keys():
              A__		   =		orig_state_dict.pop(UpperCamelCase__    )

              if "qkv" in key:
                     A__		   =		key.split('''.'''    )
                     A__		   =		int(key_split[2]    )
                     A__		   =		model.vit.encoder.layer[layer_num].attention.attention.all_head_size
                     if "weight" in key:
                            A__		   =		val[:dim, :]
                            A__		   =		val[
                                dim : dim * 2, :
                            ]
                            A__		   =		val[-dim:, :]
                     else:
                            A__		   =		val[:dim]
                            A__		   =		val[dim : dim * 2]
                            A__		   =		val[-dim:]
              else:
                     A__		   =		val

       return orig_state_dict


def       UpperCamelCase__(			)->torch.Tensor:
       A__		   =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__		   =		Image.open(requests.get(UpperCamelCase__					,	stream=UpperCamelCase__    ).raw    )
       return im


@torch.no_grad()
def       UpperCamelCase__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			str					,	UpperCamelCase__     :			str					,	UpperCamelCase__     :			bool = False    )->Optional[Any]:
       A__		   =		get_yolos_config(UpperCamelCase__    )

       # load original state_dict
       A__		   =		torch.load(UpperCamelCase__					,	map_location='''cpu'''    )['''model''']

       # load 🤗 model
       A__		   =		YolosForObjectDetection(UpperCamelCase__    )
       model.eval()
       A__		   =		convert_state_dict(UpperCamelCase__					,	UpperCamelCase__    )
       model.load_state_dict(UpperCamelCase__    )

       # Check outputs on an image, prepared by YolosImageProcessor
       A__		   =		8_00 if yolos_name != '''yolos_ti''' else 5_12
       A__		   =		YolosImageProcessor(format='''coco_detection'''					,	size=UpperCamelCase__    )
       A__		   =		image_processor(images=prepare_img()					,	return_tensors='''pt'''    )
       A__		   =		model(**UpperCamelCase__    )
       A__   ,      A__		   =		outputs.logits, outputs.pred_boxes

       A__   ,      A__		   =		None, None
       if yolos_name == "yolos_ti":
              A__		   =		torch.tensor(
                  [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]    )
              A__		   =		torch.tensor(
                  [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]    )
       elif yolos_name == "yolos_s_200_pre":
              A__		   =		torch.tensor(
                  [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]    )
              A__		   =		torch.tensor(
                  [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]    )
       elif yolos_name == "yolos_s_300_pre":
              A__		   =		torch.tensor(
                  [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]    )
              A__		   =		torch.tensor(
                  [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]    )
       elif yolos_name == "yolos_s_dWr":
              A__		   =		torch.tensor(
                  [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]    )
              A__		   =		torch.tensor(
                  [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]    )
       elif yolos_name == "yolos_base":
              A__		   =		torch.tensor(
                  [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]    )
              A__		   =		torch.tensor(
                  [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]    )
       else:
              raise ValueError(f"Unknown yolos_name: {yolos_name}"    )

       assert torch.allclose(logits[0, :3, :3]					,	UpperCamelCase__					,	atol=1e-4    )
       assert torch.allclose(pred_boxes[0, :3, :3]					,	UpperCamelCase__					,	atol=1e-4    )

       Path(UpperCamelCase__    ).mkdir(exist_ok=UpperCamelCase__    )
       print(f"Saving model {yolos_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 push_to_hub:
              A__		   =		{
                  '''yolos_ti''': '''yolos-tiny''',
                  '''yolos_s_200_pre''': '''yolos-small''',
                  '''yolos_s_300_pre''': '''yolos-small-300''',
                  '''yolos_s_dWr''': '''yolos-small-dwr''',
                  '''yolos_base''': '''yolos-base''',
              }

              print('''Pushing to the hub...'''    )
              A__		   =		model_mapping[yolos_name]
              image_processor.push_to_hub(UpperCamelCase__					,	organization='''hustvl'''    )
              model.push_to_hub(UpperCamelCase__					,	organization='''hustvl'''    )


if __name__ == "__main__":
       a__: Any							   =						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--yolos_name',
           default='yolos_s_200_pre',
           type=str,
           help=(
               'Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','
               ' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'
           ),
       )
       parser.add_argument(
           '--checkpoint_path', default=None, type=str, help='Path to the original state dict (.pth file).'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )

       a__: Any							   =						parser.parse_args()
       convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)












import itertools
import math


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->bool:

       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(UpperCamelCase__    ) + 1    )					,	6    ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


def       UpperCamelCase__(			)->Tuple:
       A__		   =		2
       while True:
              if is_prime(UpperCamelCase__    ):
                     yield num
              num += 1


def       UpperCamelCase__(			UpperCamelCase__     :			int = 1_00_01    )->int:
       return next(itertools.islice(prime_generator()					,	nth - 1					,	UpperCamelCase__    )    )


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





