


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()
SCREAMING_SNAKE_CASE						:int			=  logging.get_logger(__name__)



def       _lowerCAmelCase    (				lowerCAmelCase_				:str				)->YolosConfig:
   '''simple docstring'''






   snake_case_            =       YolosConfig()

   # size of the architecture
   if "yolos_ti" in yolos_name:
      snake_case_            =       192
      snake_case_            =       768
      snake_case_            =       12
      snake_case_            =       3
      snake_case_            =       [800, 1_333]
      snake_case_            =       False
   elif yolos_name == "yolos_s_dWr":
      snake_case_            =       330
      snake_case_            =       14
      snake_case_            =       6
      snake_case_            =       1_320
   elif "yolos_s" in yolos_name:
      snake_case_            =       384
      snake_case_            =       1_536
      snake_case_            =       12
      snake_case_            =       6
   elif "yolos_b" in yolos_name:
      snake_case_            =       [800, 1_344]

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

   return config



def       _lowerCAmelCase    (				lowerCAmelCase_				:dict		,    lowerCAmelCase_				:YolosConfig		,    lowerCAmelCase_				:bool = False				)->Dict:
   '''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)
      snake_case_            =       state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
      snake_case_            =       state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
      # next, add query, keys and values (in that order) to the state dict
      snake_case_            =       in_proj_weight[: config.hidden_size, :]
      snake_case_            =       in_proj_bias[: config.hidden_size]
      snake_case_            =       in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      snake_case_            =       in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      snake_case_            =       in_proj_weight[-config.hidden_size :, :]
      snake_case_            =       in_proj_bias[-config.hidden_size :]



def       _lowerCAmelCase    (				lowerCAmelCase_				:str				)->str:
   '''simple docstring'''






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

   return name



def       _lowerCAmelCase    (				lowerCAmelCase_				:dict		,    lowerCAmelCase_				:YolosForObjectDetection				)->dict:
   '''simple docstring'''






   for key in orig_state_dict.copy().keys():
      snake_case_            =       orig_state_dict.pop(lowerCAmelCase_				)

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

   return orig_state_dict



def       _lowerCAmelCase    (				)->torch.Tensor:
   '''simple docstring'''






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



@torch.no_grad()
def       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:bool = False				)->Tuple:
   '''simple docstring'''






   snake_case_            =       get_yolos_config(lowerCAmelCase_				)

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

   # load 🤗 model
   snake_case_            =       YolosForObjectDetection(lowerCAmelCase_				)
   model.eval()
   snake_case_            =       convert_state_dict(lowerCAmelCase_		,    lowerCAmelCase_				)
   model.load_state_dict(lowerCAmelCase_				)

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

   snake_case_     ,     snake_case_            =       None, None
   if yolos_name == "yolos_ti":
      snake_case_            =       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]]				)
      snake_case_            =       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":
      snake_case_            =       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]]				)
      snake_case_            =       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":
      snake_case_            =       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]]				)
      snake_case_            =       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":
      snake_case_            =       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]]				)
      snake_case_            =       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":
      snake_case_            =       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]]				)
      snake_case_            =       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]		,    lowerCAmelCase_		,    atol=1e-4				)
   assert torch.allclose(pred_boxes[0, :3, :3]		,    lowerCAmelCase_		,    atol=1e-4				)

   Path(lowerCAmelCase_				).mkdir(exist_ok=lowerCAmelCase_				)
   print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''				)
   model.save_pretrained(lowerCAmelCase_				)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
   image_processor.save_pretrained(lowerCAmelCase_				)

   if push_to_hub:
      snake_case_            =       {
          "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..."				)
      snake_case_            =       model_mapping[yolos_name]
      image_processor.push_to_hub(lowerCAmelCase_		,    organization="hustvl"				)
      model.push_to_hub(lowerCAmelCase_		,    organization="hustvl"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						: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.'''
   )

   SCREAMING_SNAKE_CASE						:int			=  parser.parse_args()
   convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							DistilBertTokenizer
  _SCREAMING_SNAKE_CASE		     =							DistilBertTokenizerFast
  _SCREAMING_SNAKE_CASE		     =							True



  @slow
  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       DistilBertTokenizer.from_pretrained("distilbert-base-uncased"	)

     snake_case_            =       tokenizer.encode("sequence builders"     ,					add_special_tokens=_lowerCAmelCase	)
     snake_case_            =       tokenizer.encode("multi-sequence build"     ,					add_special_tokens=_lowerCAmelCase	)

     snake_case_            =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase	)
     snake_case_            =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase     ,					_lowerCAmelCase	)

     assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
     assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
         tokenizer.sep_token_id
     ]




