





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 :   Optional[int]  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase					)    ->    YolosConfig:







   """simple docstring"""
   lowerCamelCase__       :						Any        =							YolosConfig()

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

   lowerCamelCase__       :						List[str]        =							91
   lowerCamelCase__       :						List[Any]        =							'''huggingface/label-files'''
   lowerCamelCase__       :						Dict        =							'''coco-detection-id2label.json'''
   lowerCamelCase__       :						List[str]        =							json.load(open(hf_hub_download(UpperCAmelCase      ,  UpperCAmelCase      ,  repo_type='''dataset'''					)      ,  '''r'''					)					)
   lowerCamelCase__       :						Dict        =							{int(UpperCAmelCase					): v for k, v in idalabel.items()}
   lowerCamelCase__       :						Tuple        =							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__       :						List[Any]        =							state_dict.pop(f"blocks.{i}.attn.qkv.bias"					)
      # next, add query, keys and values (in that order) to the state dict
      lowerCamelCase__       :						Any        =							in_proj_weight[: config.hidden_size, :]
      lowerCamelCase__       :						str        =							in_proj_bias[: config.hidden_size]
      lowerCamelCase__       :						int        =							in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      lowerCamelCase__       :						Dict        =							in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      lowerCamelCase__       :						Union[str, Any]        =							in_proj_weight[-config.hidden_size :, :]
      lowerCamelCase__       :						List[Any]        =							in_proj_bias[-config.hidden_size :]



def 	_a						(   UpperCAmelCase					)    ->    str:







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

   return name



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    dict:







   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      lowerCamelCase__       :						int        =							orig_state_dict.pop(UpperCAmelCase					)

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

   return orig_state_dict



def 	_a						(   )    ->    torch.Tensor:







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



@torch.no_grad()
def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    Any:







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

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

   # load 🤗 model
   lowerCamelCase__       :						int        =							YolosForObjectDetection(UpperCAmelCase					)
   model.eval()
   lowerCamelCase__       :						str        =							convert_state_dict(UpperCAmelCase      ,  UpperCAmelCase					)
   model.load_state_dict(UpperCAmelCase					)

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

   lowerCamelCase__       :						Dict        =							None, None
   if yolos_name == "yolos_ti":
      lowerCamelCase__       :						Dict        =							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__       :						int        =							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__       :						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]]					)
      lowerCamelCase__       :						List[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":
      lowerCamelCase__       :						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]]					)
      lowerCamelCase__       :						Optional[int]        =							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__       :						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]]					)
      lowerCamelCase__       :						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__       :						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__       :						List[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]      ,  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:
      lowerCamelCase__       :						Optional[int]        =							{
          '''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__       :						Union[str, Any]        =							model_mapping[yolos_name]
      image_processor.push_to_hub(UpperCAmelCase      ,  organization='''hustvl'''					)
      model.push_to_hub(UpperCAmelCase      ,  organization='''hustvl'''					)


if __name__ == "__main__":
       _A :   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)











from math import factorial


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]		, A					:     Dict		, A					:     Any      )       ->Optional[Any]:
          lowerCamelCase__       :						Tuple        =							real
          if isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[1] * rank
          else:
             lowerCamelCase__       :						List[Any]        =							rank







       def __repr__(				self					:     Tuple      )       ->str:
          return (
              F"{self.real}+"
              F"{'+'.join(str(A      )+'E'+str(n+1      )for n,dual in enumerate(self.duals      )      )}"
          )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          lowerCamelCase__       :						Tuple        =							self.duals.copy()
          while cur[-1] == 0:
             cur.pop(-1      )
          return Dual(self.real		, A      )







       def __add__(				self					:     Union[str, Any]		, A					:     int      )       ->str:
          if not isinstance(A		, A      ):
             return Dual(self.real + other		, self.duals      )
          lowerCamelCase__       :						int        =							self.duals.copy()
          lowerCamelCase__       :						int        =							other.duals.copy()
          if len(A      ) > len(A      ):
             o_dual.extend([1] * (len(A      ) - len(A      ))      )
          elif len(A      ) < len(A      ):
             s_dual.extend([1] * (len(A      ) - len(A      ))      )
          lowerCamelCase__       :						Optional[Any]        =							[]
          for i in range(len(A      )      ):
             new_duals.append(s_dual[i] + o_dual[i]      )
          return Dual(self.real + other.real		, A      )

       _UpperCAmelCase				:  List[Any]			   =   __add__







       def __sub__(				self					:     Any		, A					:     Dict      )       ->int:
          return self + other * -1







       def __mul__(				self					:     Optional[Any]		, A					:     List[Any]      )       ->Union[str, Any]:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[]
             for i in self.duals:
                new_duals.append(i * other      )
             return Dual(self.real * other		, A      )
          lowerCamelCase__       :						Tuple        =							[0] * (len(self.duals      ) + len(other.duals      ) + 1)
          for i, item in enumerate(self.duals      ):
             for j, jtem in enumerate(other.duals      ):
                new_duals[i + j + 1] += item * jtem
          for k in range(len(self.duals      )      ):
             new_duals[k] += self.duals[k] * other.real
          for index in range(len(other.duals      )      ):
             new_duals[index] += other.duals[index] * self.real
          return Dual(self.real * other.real		, A      )

       _UpperCAmelCase				:  Optional[Any]			   =   __mul__







       def __truediv__(				self					:     int		, A					:     List[Any]      )       ->Dict:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Tuple        =							[]
             for i in self.duals:
                new_duals.append(i / other      )
             return Dual(self.real / other		, A      )
          raise ValueError







       def __floordiv__(				self					:     Dict		, A					:     Union[str, Any]      )       ->Union[str, Any]:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Tuple        =							[]
             for i in self.duals:
                new_duals.append(i // other      )
             return Dual(self.real // other		, A      )
          raise ValueError







       def __pow__(				self					:     Any		, A					:     List[Any]      )       ->Tuple:
          if n < 0 or isinstance(A		, A      ):
             raise ValueError('''power must be a positive integer'''      )
          if n == 0:
             return 1
          if n == 1:
             return self
          lowerCamelCase__       :						Union[str, Any]        =							self
          for _ in range(n - 1      ):
             x *= self
          return x



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if not callable(UpperCAmelCase					):
      raise ValueError('''differentiate() requires a function as input for func'''					)
   if not isinstance(UpperCAmelCase      ,  (float, int)					):
      raise ValueError('''differentiate() requires a float as input for position'''					)
   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					):
      raise ValueError('''differentiate() requires an int as input for order'''					)
   lowerCamelCase__       :						List[str]        =							Dual(UpperCAmelCase      ,  1					)
   lowerCamelCase__       :						Any        =							func(UpperCAmelCase					)
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(UpperCAmelCase					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



       def 	_a						(   UpperCAmelCase					)    ->    int:







          """simple docstring"""
          return y**2 * y**4




       print(differentiate(f, 9, 2))



