"""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()
__magic_name__     					=       logging.get_logger(__name__)





def 	_lowerCamelCase			(			UpperCAmelCase__       )				->  YolosConfig:


   '''simple docstring'''


   a__  					=		YolosConfig()

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

   a__  					=		91
   a__  					=		"huggingface/label-files"
   a__  					=		"coco-detection-id2label.json"
   a__  					=		json.load(open(hf_hub_download(__snake_case,__snake_case,repo_type='dataset'       ),'r'       )       )
   a__  					=		{int(__snake_case       ): v for k, v in idalabel.items()}
   a__  					=		idalabel
   a__  					=		{v: k for k, v in idalabel.items()}

   return config





def 	_lowerCamelCase			(			UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__ = False       )				->  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)
      a__  					=		state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''       )
      a__  					=		state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''       )
      # next, add query, keys and values (in that order) to the state dict
      a__  					=		in_proj_weight[: config.hidden_size, :]
      a__  					=		in_proj_bias[: config.hidden_size]
      a__  					=		in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      a__  					=		in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      a__  					=		in_proj_weight[-config.hidden_size :, :]
      a__  					=		in_proj_bias[-config.hidden_size :]





def 	_lowerCamelCase			(			UpperCAmelCase__       )				->  str:


   '''simple docstring'''


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

   return name





def 	_lowerCamelCase			(			UpperCAmelCase__,UpperCAmelCase__       )				->  dict:


   '''simple docstring'''


   for key in orig_state_dict.copy().keys():
      a__  					=		orig_state_dict.pop(__snake_case       )

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

   return orig_state_dict





def 	_lowerCamelCase			(			)				->  torch.Tensor:


   '''simple docstring'''


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





@torch.no_grad()
def 	_lowerCamelCase			(			UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__ = False       )				->  Dict:


   '''simple docstring'''


   a__  					=		get_yolos_config(__snake_case       )

   # load original state_dict
   a__  					=		torch.load(__snake_case,map_location='cpu'       )["model"]

   # load 🤗 model
   a__  					=		YolosForObjectDetection(__snake_case       )
   model.eval()
   a__  					=		convert_state_dict(__snake_case,__snake_case       )
   model.load_state_dict(__snake_case       )

   # Check outputs on an image, prepared by YolosImageProcessor
   a__  					=		8_00 if yolos_name != "yolos_ti" else 5_12
   a__  					=		YolosImageProcessor(format='coco_detection',size=__snake_case       )
   a__  					=		image_processor(images=prepare_img(),return_tensors='pt'       )
   a__  					=		model(**__snake_case       )
   a__  					=		outputs.logits, outputs.pred_boxes

   a__  					=		None, None
   if yolos_name == "yolos_ti":
      a__  					=		torch.tensor(
          [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]       )
      a__  					=		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":
      a__  					=		torch.tensor(
          [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]       )
      a__  					=		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":
      a__  					=		torch.tensor(
          [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]       )
      a__  					=		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":
      a__  					=		torch.tensor(
          [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]       )
      a__  					=		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":
      a__  					=		torch.tensor(
          [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]       )
      a__  					=		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:
      a__  					=		{
          "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...'       )
      a__  					=		model_mapping[yolos_name]
      image_processor.push_to_hub(__snake_case,organization='hustvl'       )
      model.push_to_hub(__snake_case,organization='hustvl'       )


if __name__ == "__main__":
     __magic_name__     					=       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."
     )

     __magic_name__     					=       parser.parse_args()
     convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""




from __future__ import annotations

from math import pow, sqrt


def     __magic_name__      ( __snake_case							:			float	,					__snake_case							:			float	,					__snake_case							:			float    )					->							dict[str, float]:
       if (resistance, reactance, impedance).count(0    ) != 1:
              raise ValueError("One and only one argument must be 0"    )
       if resistance == 0:
              return {"resistance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif reactance == 0:
              return {"reactance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif impedance == 0:
              return {"impedance": sqrt(pow(__snake_case	,					2    ) + pow(__snake_case	,					2    )    )}
       else:
              raise ValueError("Exactly one argument must be 0"    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


