


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()
__lowerCAmelCase			=							logging.get_logger(__name__)




def        snake_case_					(  snake_case					)  ->    YolosConfig:
       lowercase__:       str        =       YolosConfig()

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

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

       return config




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




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

       return name




def        snake_case_					(  snake_case      ,							snake_case					)  ->    dict:
       for key in orig_state_dict.copy().keys():
              lowercase__:       Optional[int]        =       orig_state_dict.pop(snake_case					)

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

       return orig_state_dict




def        snake_case_					(  )  ->    torch.Tensor:
       lowercase__:       int        =       'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__:       Any        =       Image.open(requests.get(snake_case      ,							stream=snake_case					).raw					)
       return im




@torch.no_grad()
def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case = False					)  ->    List[str]:
       lowercase__:       Optional[int]        =       get_yolos_config(snake_case					)

       # load original state_dict
       lowercase__:       str        =       torch.load(snake_case      ,							map_location='cpu'					)['model']

       # load 🤗 model
       lowercase__:       int        =       YolosForObjectDetection(snake_case					)
       model.eval()
       lowercase__:       Dict        =       convert_state_dict(snake_case      ,							snake_case					)
       model.load_state_dict(snake_case					)

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

       lowercase__      ,						lowercase__:       Union[str, Any]        =       None, None
       if yolos_name == "yolos_ti":
              lowercase__:       Tuple        =       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]]					)
              lowercase__:       Optional[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":
              lowercase__:       Union[str, 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]]					)
              lowercase__:       List[str]        =       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":
              lowercase__:       Optional[Any]        =       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]]					)
              lowercase__:       Union[str, 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":
              lowercase__:       str        =       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]]					)
              lowercase__:       str        =       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":
              lowercase__:       Any        =       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]]					)
              lowercase__:       Union[str, Any]        =       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]      ,							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:
              lowercase__:       Tuple        =       {
                  '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...'					)
              lowercase__:       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__":
 __lowerCAmelCase			=							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.'''
 )

 __lowerCAmelCase			=							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




def        snake_case_					(  snake_case      ,							snake_case					)  ->    list[int]:
       lowercase__:       Tuple        =       0
       lowercase__:       str        =       len(snake_case					) - 1

       while i < j:
              if nums[i] + nums[j] == target:
                     return [i, j]
              elif nums[i] + nums[j] < target:
                     lowercase__:       str        =       i + 1
              else:
                     lowercase__:       Dict        =       j - 1

       return []


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(F'''{two_pointer([2, 7, 11, 15], 9) = }''')


