





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					:    Any   =  logging.get_logger(__name__)



def        _a						(    UpperCAmelCase     )				->				Optional[int]:







   """simple docstring"""
   lowerCamelCase__   :						List[Any]					   =							YolosConfig()

   # size of the architecture
   if "yolos_ti" in yolos_name:
      lowerCamelCase__   :						Tuple					   =							192
      lowerCamelCase__   :						str					   =							768
      lowerCamelCase__   :						List[Any]					   =							12
      lowerCamelCase__   :						Optional[int]					   =							3
      lowerCamelCase__   :						Optional[Any]					   =							[800, 1333]
      lowerCamelCase__   :						Dict					   =							False
   elif yolos_name == "yolos_s_dWr":
      lowerCamelCase__   :						Tuple					   =							330
      lowerCamelCase__   :						List[str]					   =							14
      lowerCamelCase__   :						Tuple					   =							6
      lowerCamelCase__   :						List[str]					   =							1320
   elif "yolos_s" in yolos_name:
      lowerCamelCase__   :						Any					   =							384
      lowerCamelCase__   :						Union[str, Any]					   =							1536
      lowerCamelCase__   :						List[str]					   =							12
      lowerCamelCase__   :						Any					   =							6
   elif "yolos_b" in yolos_name:
      lowerCamelCase__   :						Optional[int]					   =							[800, 1344]

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

   return config



def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase = False     )				->				Optional[int]:







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



def        _a						(    UpperCAmelCase     )				->				List[str]:







   """simple docstring"""


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

   return name



def        _a						(    UpperCAmelCase			,  UpperCAmelCase     )				->				int:







   """simple docstring"""


   for key in orig_state_dict.copy().keys():
      lowerCamelCase__   :						List[str]					   =							orig_state_dict.pop(__snake_case     )

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

   return orig_state_dict



def        _a						(    )				->				str:







   """simple docstring"""
   lowerCamelCase__   :						Optional[int]					   =							'http://images.cocodataset.org/val2017/000000039769.jpg'
   lowerCamelCase__   :						Any					   =							Image.open(requests.get(__snake_case			,  stream=__snake_case     ).raw     )
   return im



@torch.no_grad()
def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase = False     )				->				Optional[int]:







   """simple docstring"""
   lowerCamelCase__   :						str					   =							get_yolos_config(__snake_case     )

   # load original state_dict
   lowerCamelCase__   :						Tuple					   =							torch.load(__snake_case			,  map_location='''cpu'''     )['model']

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

   # Check outputs on an image, prepared by YolosImageProcessor
   lowerCamelCase__   :						int					   =							800 if yolos_name != 'yolos_ti' else 512
   lowerCamelCase__   :						str					   =							YolosImageProcessor(format='''coco_detection'''			,  size=__snake_case     )
   lowerCamelCase__   :						Optional[Any]					   =							image_processor(images=prepare_img()			,  return_tensors='''pt'''     )
   lowerCamelCase__   :						Optional[Any]					   =							model(**__snake_case     )
   lowerCamelCase__   :						int					   =							outputs.logits, outputs.pred_boxes

   lowerCamelCase__   :						Optional[Any]					   =							None, None
   if yolos_name == "yolos_ti":
      lowerCamelCase__   :						Optional[Any]					   =							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]]     )
      lowerCamelCase__   :						Union[str, Any]					   =							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":
      lowerCamelCase__   :						Any					   =							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]]     )
      lowerCamelCase__   :						Dict					   =							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":
      lowerCamelCase__   :						str					   =							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]]     )
      lowerCamelCase__   :						Optional[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":
      lowerCamelCase__   :						int					   =							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]]     )
      lowerCamelCase__   :						Union[str, Any]					   =							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":
      lowerCamelCase__   :						Optional[int]					   =							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]]     )
      lowerCamelCase__   :						str					   =							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]			,  __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:
      lowerCamelCase__   :						List[str]					   =							{
          '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...'''     )
      lowerCamelCase__   :						str					   =							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					:    Optional[int]   =  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					:    Union[str, Any]   =  parser.parse_args()
       convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      list[list[int]]            =						[[0 for _ in range(__snake_case  )] for _ in range(m + 1  )]
					for i in range(m + 1  ):
										UpperCAmelCase_    :      Optional[Any]            =						1

					for n in range(m + 1  ):
										for k in range(1     ,       __snake_case  ):
															memo[n][k] += memo[n][k - 1]
															if n - k > 0:
																				memo[n][k] += memo[n - k - 1][k]

					return memo[m][m - 1]


if __name__ == "__main__":
		import sys

		if len(sys.argv) == 1:
				try:
						__UpperCAmelCase								=						int(input('Enter a number: ').strip())
						print(partition(n))
				except ValueError:
						print('Please enter a number.')
		else:
				try:
						__UpperCAmelCase								=						int(sys.argv[1])
						print(partition(n))
				except ValueError:
						print('Please pass a number.')






