'''simple docstring'''





from __future__ import annotations

SCREAMING_SNAKE_CASE_:	str    =[True] * 1_00_00_01
SCREAMING_SNAKE_CASE_:	List[Any]    =2
while i * i <= 1_00_00_00:
    if seive[i]:
        for j in range(i * i, 1_00_00_01, i):
            SCREAMING_SNAKE_CASE_:	str    =False
    i += 1





def       lowerCAmelCase_							(   snake_case_			:					int     )       -> bool:


     '''simple docstring'''




     return seive[n]





def       lowerCAmelCase_							(   snake_case_			:					int     )       -> bool:


     '''simple docstring'''




     return any(digit in "02468" for digit in str(snake_case_     )     )





def       lowerCAmelCase_							(   snake_case_			:					int = 1_00_00_00     )       -> list[int]:


     '''simple docstring'''




     UpperCAmelCase_			       =  [2]  # result already includes the number 2.
     for num in range(3   ,       limit + 1   ,       2     ):
          if is_prime(snake_case_     ) and not contains_an_even_digit(snake_case_     ):
               UpperCAmelCase_			       =  str(snake_case_     )
               UpperCAmelCase_			       =  [int(str_num[j:] + str_num[:j]     ) for j in range(len(snake_case_     )     )]
               if all(is_prime(snake_case_     ) for i in list_nums     ):
                    result.append(snake_case_     )
     return result





def       lowerCAmelCase_							(   )       -> int:


     '''simple docstring'''




     return len(find_circular_primes()     )


if __name__ == "__main__":
    print(f"{len(find_circular_primes()) = }")










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__		     =     logging.get_logger(__name__)





def 				A__ (snake_case   :		str				)     ->						YolosConfig:
					__UpperCamelCase					:	Dict             =      YolosConfig()

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

					__UpperCamelCase					:	Optional[Any]             =      91
					__UpperCamelCase					:	Union[str, Any]             =      """huggingface/label-files"""
					__UpperCamelCase					:	List[Any]             =      """coco-detection-id2label.json"""
					__UpperCamelCase					:	Any             =      json.load(open(hf_hub_download(snake_case		,	snake_case		,	repo_type="""dataset"""				)		,	"""r"""				)				)
					__UpperCamelCase					:	Tuple             =      {int(snake_case				): v for k, v in idalabel.items()}
					__UpperCamelCase					:	str             =      idalabel
					__UpperCamelCase					:	List[Any]             =      {v: k for k, v in idalabel.items()}

					return config





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





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

					return name





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

										if "qkv" in key:
															__UpperCamelCase					:	Optional[int]             =      key.split("""."""				)
															__UpperCamelCase					:	Union[str, Any]             =      int(key_split[2]				)
															__UpperCamelCase					:	Any             =      model.vit.encoder.layer[layer_num].attention.attention.all_head_size
															if "weight" in key:
																				__UpperCamelCase					:	Tuple             =      val[:dim, :]
																				__UpperCamelCase					:	str             =      val[
																				    dim : dim * 2, :
																				]
																				__UpperCamelCase					:	int             =      val[-dim:, :]
															else:
																				__UpperCamelCase					:	Tuple             =      val[:dim]
																				__UpperCamelCase					:	Any             =      val[dim : dim * 2]
																				__UpperCamelCase					:	str             =      val[-dim:]
										else:
															__UpperCamelCase					:	Optional[int]             =      val

					return orig_state_dict





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





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

					# load original state_dict
					__UpperCamelCase					:	Tuple             =      torch.load(snake_case		,	map_location="""cpu"""				)["""model"""]

					# load 🤗 model
					__UpperCamelCase					:	Dict             =      YolosForObjectDetection(snake_case				)
					model.eval()
					__UpperCamelCase					:	int             =      convert_state_dict(snake_case		,	snake_case				)
					model.load_state_dict(snake_case				)

					# Check outputs on an image, prepared by YolosImageProcessor
					__UpperCamelCase					:	Any             =      8_00 if yolos_name != """yolos_ti""" else 5_12
					__UpperCamelCase					:	int             =      YolosImageProcessor(format="""coco_detection"""		,	size=snake_case				)
					__UpperCamelCase					:	Any             =      image_processor(images=prepare_img()		,	return_tensors="""pt"""				)
					__UpperCamelCase					:	List[Any]             =      model(**snake_case				)
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      outputs.logits, outputs.pred_boxes

					__UpperCamelCase      ,			__UpperCamelCase					:	List[Any]             =      None, None
					if yolos_name == "yolos_ti":
										__UpperCamelCase					:	Dict             =      torch.tensor(
										    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]				)
										__UpperCamelCase					:	Union[str, Any]             =      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":
										__UpperCamelCase					:	Optional[Any]             =      torch.tensor(
										    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]				)
										__UpperCamelCase					:	str             =      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":
										__UpperCamelCase					:	Union[str, Any]             =      torch.tensor(
										    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]				)
										__UpperCamelCase					:	Tuple             =      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":
										__UpperCamelCase					:	str             =      torch.tensor(
										    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]				)
										__UpperCamelCase					:	Optional[Any]             =      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":
										__UpperCamelCase					:	Optional[int]             =      torch.tensor(
										    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]				)
										__UpperCamelCase					:	List[str]             =      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]		,	snake_case		,	atol=1e-4				)
					assert torch.allclose(pred_boxes[0, :3, :3]		,	snake_case		,	atol=1e-4				)

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

					if push_to_hub:
										__UpperCamelCase					:	Tuple             =      {
										    """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..."""				)
										__UpperCamelCase					:	int             =      model_mapping[yolos_name]
										image_processor.push_to_hub(snake_case		,	organization="""hustvl"""				)
										model.push_to_hub(snake_case		,	organization="""hustvl"""				)


if __name__ == "__main__":
	a__		     =     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__		     =     parser.parse_args()
	convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






