



'''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()
A__    :   int           =					logging.get_logger(__name__)





def   a_   (		_UpperCAmelCase    :      str				)    ->  YolosConfig:
      __snake_case :   List[str]													=    YolosConfig()

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

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

      return config





def   a_   (		_UpperCAmelCase    :      dict ,_UpperCAmelCase    :      YolosConfig ,_UpperCAmelCase    :      bool = False				)    ->  List[str]:
      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 :   int													=    state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''				)
            __snake_case :   Optional[int]													=    state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''				)
            # next, add query, keys and values (in that order) to the state dict
            __snake_case :   str													=    in_proj_weight[: config.hidden_size, :]
            __snake_case :   Union[str, Any]													=    in_proj_bias[: config.hidden_size]
            __snake_case :   int													=    in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            __snake_case :   Optional[int]													=    in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            __snake_case :   int													=    in_proj_weight[-config.hidden_size :, :]
            __snake_case :   Any													=    in_proj_bias[-config.hidden_size :]





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

      return name





def   a_   (		_UpperCAmelCase    :      dict ,_UpperCAmelCase    :      YolosForObjectDetection				)    ->  dict:
      for key in orig_state_dict.copy().keys():
            __snake_case :   str													=    orig_state_dict.pop(_UpperCAmelCase				)

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

      return orig_state_dict





def   a_   (		)    ->  torch.Tensor:
      __snake_case :   List[str]													=    'http://images.cocodataset.org/val2017/000000039769.jpg'
      __snake_case :   Optional[Any]													=    Image.open(requests.get(_UpperCAmelCase ,stream=_UpperCAmelCase				).raw				)
      return im





@torch.no_grad()
def   a_   (		_UpperCAmelCase    :      str ,_UpperCAmelCase    :      str ,_UpperCAmelCase    :      str ,_UpperCAmelCase    :      bool = False				)    ->  List[Any]:
      __snake_case :   Any													=    get_yolos_config(_UpperCAmelCase				)

      # load original state_dict
      __snake_case :   List[Any]													=    torch.load(_UpperCAmelCase ,map_location='cpu'				)['model']

      # load 🤗 model
      __snake_case :   List[Any]													=    YolosForObjectDetection(_UpperCAmelCase				)
      model.eval()
      __snake_case :   Optional[Any]													=    convert_state_dict(_UpperCAmelCase ,_UpperCAmelCase				)
      model.load_state_dict(_UpperCAmelCase				)

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

      __snake_case							,     __snake_case :   List[str]													=    None, None
      if yolos_name == "yolos_ti":
            __snake_case :   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]]				)
            __snake_case :   Tuple													=    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 :   Optional[int]													=    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 :   Dict													=    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 :   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]]				)
            __snake_case :   Dict													=    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 :   Dict													=    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 :   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":
            __snake_case :   str													=    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 :   List[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] ,_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:
            __snake_case :   List[str]													=    {
                '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 :   Any													=    model_mapping[yolos_name]
            image_processor.push_to_hub(_UpperCAmelCase ,organization='hustvl'				)
            model.push_to_hub(_UpperCAmelCase ,organization='hustvl'				)


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

    A__    :   Optional[int]           =					parser.parse_args()
    convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''tokenizer''']
 A__				= '''CLIPImageProcessor'''
 A__				= ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
 def __init__(   self						:							Any      ,     __a						:							str=None      ,     __a						:							List[Any]=None      ,     **__a						:							List[str]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'      ,     __a      ,     )
             __snake_case :   List[str]													=    kwargs.pop('feature_extractor'  )

       __snake_case :   List[Any]													=    image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(__a      ,     __a  )
 def __call__(   self						:							List[Any]      ,     __a						:							Optional[int]=None      ,     __a						:							Optional[int]=None      ,     __a						:							Union[str, Any]=None      ,     **__a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''

       if text is None and images is None:
             raise ValueError('You have to specify either text or images. Both cannot be none.'  )

       if text is not None:
             __snake_case :   Any													=    self.tokenizer(__a      ,     return_tensors=__a      ,     **__a  )

       if images is not None:
             __snake_case :   str													=    self.image_processor(__a      ,     return_tensors=__a      ,     **__a  )

       if text is not None and images is not None:
             __snake_case :   Optional[Any]													=    image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**__a  )      ,     tensor_type=__a  )
 def A_   (   self						:							List[Any]      ,     *__a						:							Dict      ,     **__a						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.tokenizer.batch_decode(*__a      ,     **__a  )
 def A_   (   self						:							str      ,     *__a						:							Tuple      ,     **__a						:							List[str]  )      ->   int:







       '''simple docstring'''
       return self.tokenizer.decode(*__a      ,     **__a  )
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.tokenizer.model_input_names
       __snake_case :   Tuple													=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
 @property
 def A_   (   self						:							Any  )      ->   Any:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,     __a      ,     )
       return self.image_processor_class





 @property
 def A_   (   self						:							Optional[int]  )      ->   Optional[Any]:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,     __a      ,     )
       return self.image_processor



