




"""simple docstring"""


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  UpperCAmelCase__	(snake_case__							: str   ):
		"""simple docstring"""

		_snake_case							:		List[Any]								=  YolosConfig()

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

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

		return config


def  UpperCAmelCase__	(snake_case__							: dict		,       snake_case__							: YolosConfig		,       snake_case__							: bool = False   ):
		"""simple docstring"""

		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							:		int								=  state_dict.pop(F"blocks.{i}.attn.qkv.bias"   )
				# next, add query, keys and values (in that order) to the state dict
				_snake_case							:		Optional[int]								=  in_proj_weight[: config.hidden_size, :]
				_snake_case							:		Any								=  in_proj_bias[: config.hidden_size]
				_snake_case							:		Optional[int]								=  in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				_snake_case							:		int								=  in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				_snake_case							:		int								=  in_proj_weight[-config.hidden_size :, :]
				_snake_case							:		Union[str, Any]								=  in_proj_bias[-config.hidden_size :]


def  UpperCAmelCase__	(snake_case__							: str   ):
		"""simple docstring"""

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

		return name


def  UpperCAmelCase__	(snake_case__							: dict		,       snake_case__							: YolosForObjectDetection   ):
		"""simple docstring"""

		for key in orig_state_dict.copy().keys():
				_snake_case							:		List[Any]								=  orig_state_dict.pop(__lowerCamelCase   )

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

		return orig_state_dict


def  UpperCAmelCase__	():
		"""simple docstring"""

		_snake_case							:		Optional[int]								=  "http://images.cocodataset.org/val2017/000000039769.jpg"
		_snake_case							:		List[str]								=  Image.open(requests.get(__lowerCamelCase		,       stream=__lowerCamelCase   ).raw   )
		return im


@torch.no_grad()
def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: str		,       snake_case__							: str		,       snake_case__							: bool = False   ):
		"""simple docstring"""

		_snake_case							:		Tuple								=  get_yolos_config(__lowerCamelCase   )

		# load original state_dict
		_snake_case							:		Optional[Any]								=  torch.load(__lowerCamelCase		,       map_location="""cpu"""   )["model"]

		# load 🤗 model
		_snake_case							:		Optional[Any]								=  YolosForObjectDetection(__lowerCamelCase   )
		model.eval()
		_snake_case							:		int								=  convert_state_dict(__lowerCamelCase		,       __lowerCamelCase   )
		model.load_state_dict(__lowerCamelCase   )

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

		_snake_case							:		Tuple								=  None, None
		if yolos_name == "yolos_ti":
				_snake_case							:		str								=  torch.tensor(
				    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]   )
				_snake_case							:		Dict								=  torch.tensor(
				    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]   )
		elif yolos_name == "yolos_s_200_pre":
				_snake_case							:		Optional[int]								=  torch.tensor(
				    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]   )
				_snake_case							:		str								=  torch.tensor(
				    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]   )
		elif yolos_name == "yolos_s_300_pre":
				_snake_case							:		Optional[int]								=  torch.tensor(
				    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]   )
				_snake_case							:		List[Any]								=  torch.tensor(
				    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]   )
		elif yolos_name == "yolos_s_dWr":
				_snake_case							:		List[Any]								=  torch.tensor(
				    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]   )
				_snake_case							:		List[str]								=  torch.tensor(
				    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]   )
		elif yolos_name == "yolos_base":
				_snake_case							:		Dict								=  torch.tensor(
				    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]   )
				_snake_case							:		int								=  torch.tensor(
				    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]   )
		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							:		List[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							:		str								=  model_mapping[yolos_name]
				image_processor.push_to_hub(__lowerCamelCase		,       organization="""hustvl"""   )
				model.push_to_hub(__lowerCamelCase		,       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)








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 1_00_00_00   ):
  """simple docstring"""

  _snake_case							:		Dict								=  [i - 1 for i in range(limit + 1   )]

  for i in range(2		,       limit + 1   ):
    if phi[i] == i - 1:
      for j in range(2 * i		,       limit + 1		,       snake_case__   ):
        phi[j] -= phi[j] // i

  return sum(phi[2 : limit + 1]   )


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


