






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





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       YolosConfig()

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

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

     return config





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





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

     return name





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     for key in orig_state_dict.copy().keys():
          __lowerCAmelCase             =       orig_state_dict.pop(__snake_case							)

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

     return orig_state_dict





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





@torch.no_grad()
def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case = False							):
     __lowerCAmelCase             =       get_yolos_config(__snake_case							)

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

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

     # Check outputs on an image, prepared by YolosImageProcessor
     __lowerCAmelCase             =       800 if yolos_name != "yolos_ti" else 512
     __lowerCAmelCase             =       YolosImageProcessor(format="coco_detection"       ,			size=__snake_case							)
     __lowerCAmelCase             =       image_processor(images=prepare_img()       ,			return_tensors="pt"							)
     __lowerCAmelCase             =       model(**__snake_case							)
     __lowerCAmelCase      ,			__lowerCAmelCase             =       outputs.logits, outputs.pred_boxes

     __lowerCAmelCase      ,			__lowerCAmelCase             =       None, None
     if yolos_name == "yolos_ti":
          __lowerCAmelCase             =       torch.tensor(
              [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]]							)
          __lowerCAmelCase             =       torch.tensor(
              [[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]]							)
     elif yolos_name == "yolos_s_200_pre":
          __lowerCAmelCase             =       torch.tensor(
              [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]							)
          __lowerCAmelCase             =       torch.tensor(
              [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]							)
     elif yolos_name == "yolos_s_300_pre":
          __lowerCAmelCase             =       torch.tensor(
              [[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]]							)
          __lowerCAmelCase             =       torch.tensor(
              [[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]]							)
     elif yolos_name == "yolos_s_dWr":
          __lowerCAmelCase             =       torch.tensor(
              [[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]]							)
          __lowerCAmelCase             =       torch.tensor(
              [[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]]							)
     elif yolos_name == "yolos_base":
          __lowerCAmelCase             =       torch.tensor(
              [[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]]							)
          __lowerCAmelCase             =       torch.tensor(
              [[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]]							)
     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             =       {
              "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             =       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      :       Union[str, Any]    =						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      :       Tuple    =						parser.parse_args()
 convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)














import os

# Precomputes a list of the 100 first triangular numbers
lowerCamelCase      :       str    =						[int(0.5 * n * (n + 1)) for n in range(1, 101)]





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       os.path.dirname(os.path.realpath(__snake_case							)							)
     __lowerCAmelCase             =       os.path.join(__snake_case       ,			"words.txt"							)

     __lowerCAmelCase             =       ""
     with open(__snake_case							) as f:
          __lowerCAmelCase             =       f.readline()

     __lowerCAmelCase             =       [word.strip("\""							) for word in words.strip("\r\n"							).split(","							)]
     __lowerCAmelCase             =       [
         word
         for word in [sum(ord(__snake_case							) - 64 for x in word							) for word in words]
         if word in TRIANGULAR_NUMBERS
     ]
     return len(__snake_case							)


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






