


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()
_snake_case					:		Optional[Any]   =    logging.get_logger(__name__)







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Union[str, Any]										=       YolosConfig()

							# size of the architecture
							if "yolos_ti" in yolos_name:
														__snake_case		:      Optional[int]										=       1_9_2
														__snake_case		:      List[Any]										=       7_6_8
														__snake_case		:      List[Any]										=       1_2
														__snake_case		:      Union[str, Any]										=       3
														__snake_case		:      Union[str, Any]										=       [8_0_0, 1_3_3_3]
														__snake_case		:      Optional[int]										=       False
							elif yolos_name == "yolos_s_dWr":
														__snake_case		:      Tuple										=       3_3_0
														__snake_case		:      Optional[int]										=       1_4
														__snake_case		:      str										=       6
														__snake_case		:      Tuple										=       1_3_2_0
							elif "yolos_s" in yolos_name:
														__snake_case		:      str										=       3_8_4
														__snake_case		:      List[Any]										=       1_5_3_6
														__snake_case		:      List[Any]										=       1_2
														__snake_case		:      List[str]										=       6
							elif "yolos_b" in yolos_name:
														__snake_case		:      Any										=       [8_0_0, 1_3_4_4]

							__snake_case		:      Any										=       9_1
							__snake_case		:      Tuple										=       "huggingface/label-files"
							__snake_case		:      Optional[Any]										=       "coco-detection-id2label.json"
							__snake_case		:      Any										=       json.load(open(hf_hub_download(__lowerCamelCase							,     __lowerCamelCase							,     repo_type="dataset"     )							,     "r"     )     )
							__snake_case		:      int										=       {int(__lowerCamelCase     ): v for k, v in idalabel.items()}
							__snake_case		:      Optional[Any]										=       idalabel
							__snake_case		:      Dict										=       {v: k for k, v in idalabel.items()}

							return config







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







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

							return name







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							for key in orig_state_dict.copy().keys():
														__snake_case		:      List[Any]										=       orig_state_dict.pop(__lowerCamelCase     )

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

							return orig_state_dict







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







@torch.no_grad()
def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = False     ):
							__snake_case		:      str										=       get_yolos_config(__lowerCamelCase     )

							# load original state_dict
							__snake_case		:      List[str]										=       torch.load(__lowerCamelCase							,     map_location="cpu"     )["model"]

							# load 🤗 model
							__snake_case		:      List[str]										=       YolosForObjectDetection(__lowerCamelCase     )
							model.eval()
							__snake_case		:      List[str]										=       convert_state_dict(__lowerCamelCase							,     __lowerCamelCase     )
							model.load_state_dict(__lowerCamelCase     )

							# Check outputs on an image, prepared by YolosImageProcessor
							__snake_case		:      Tuple										=       8_0_0 if yolos_name != "yolos_ti" else 5_1_2
							__snake_case		:      List[Any]										=       YolosImageProcessor(format="coco_detection"							,     size=__lowerCamelCase     )
							__snake_case		:      Optional[Any]										=       image_processor(images=prepare_img()							,     return_tensors="pt"     )
							__snake_case		:      Optional[Any]										=       model(**__lowerCamelCase     )
							__snake_case		:      Any										=       outputs.logits, outputs.pred_boxes

							__snake_case		:      Optional[Any]										=       None, None
							if yolos_name == "yolos_ti":
														__snake_case		:      int										=       torch.tensor(
														    [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]]     )
														__snake_case		:      Any										=       torch.tensor(
														    [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]]     )
							elif yolos_name == "yolos_s_200_pre":
														__snake_case		:      Optional[Any]										=       torch.tensor(
														    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]     )
														__snake_case		:      Optional[Any]										=       torch.tensor(
														    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]     )
							elif yolos_name == "yolos_s_300_pre":
														__snake_case		:      Tuple										=       torch.tensor(
														    [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]]     )
														__snake_case		:      Optional[Any]										=       torch.tensor(
														    [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]]     )
							elif yolos_name == "yolos_s_dWr":
														__snake_case		:      Tuple										=       torch.tensor(
														    [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]]     )
														__snake_case		:      List[Any]										=       torch.tensor(
														    [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]]     )
							elif yolos_name == "yolos_base":
														__snake_case		:      Optional[int]										=       torch.tensor(
														    [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]]     )
														__snake_case		:      Optional[int]										=       torch.tensor(
														    [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]]     )
							else:
														raise ValueError(F'Unknown yolos_name: {yolos_name}'     )

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

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

							if push_to_hub:
														__snake_case		:      Any										=       {
														    "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..."     )
														__snake_case		:      int										=       model_mapping[yolos_name]
														image_processor.push_to_hub(__lowerCamelCase							,     organization="hustvl"     )
														model.push_to_hub(__lowerCamelCase							,     organization="hustvl"     )


if __name__ == "__main__":
			_snake_case					:		Optional[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."
			)

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





from __future__ import annotations

from functools import lru_cache
from math import ceil

_snake_case					:		Tuple      =    100

_snake_case					:		int      =    set(range(3, NUM_PRIMES, 2))
primes.add(2)
_snake_case					:		int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
			if prime not in primes:
						continue
			primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))







@lru_cache(maxsize=1_0_0     )
def     lowerCAmelCase_					( __lowerCamelCase     ):
							if number_to_partition < 0:
														return set()
							elif number_to_partition == 0:
														return {1}

							__snake_case		:      set[int]										=       set()
							__snake_case		:      int
							__snake_case		:      int

							for prime in primes:
														if prime > number_to_partition:
																					continue
														for sub in partition(number_to_partition - prime     ):
																					ret.add(sub * prime     )

							return ret







def     lowerCAmelCase_					( __lowerCamelCase = 5_0_0_0     ):
							for number_to_partition in range(1							,     __lowerCamelCase     ):
														if len(partition(__lowerCamelCase     )     ) > number_unique_partitions:
																					return number_to_partition
							return None


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

