





from ....utils import logging


UpperCAmelCase_  							=				logging.get_logger(__name__)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=None,	__magic_name__=2048							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   config.__dict__
       UpperCamelCase__							:       Any      =   modal_hidden_size
       if num_labels:
           UpperCamelCase__							:       Union[str, Any]      =   num_labels








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()
UpperCAmelCase_  							=				logging.get_logger(__name__)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			YolosConfig:
    UpperCamelCase__							:       str      =   YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        UpperCamelCase__							:       Dict      =   192
        UpperCamelCase__							:       Dict      =   768
        UpperCamelCase__							:       Optional[Any]      =   12
        UpperCamelCase__							:       List[Any]      =   3
        UpperCamelCase__							:       Optional[int]      =   [800, 1333]
        UpperCamelCase__							:       Tuple      =   False
    elif yolos_name == "yolos_s_dWr":
        UpperCamelCase__							:       int      =   330
        UpperCamelCase__							:       Tuple      =   14
        UpperCamelCase__							:       str      =   6
        UpperCamelCase__							:       Optional[int]      =   1320
    elif "yolos_s" in yolos_name:
        UpperCamelCase__							:       Optional[int]      =   384
        UpperCamelCase__							:       Any      =   1536
        UpperCamelCase__							:       Union[str, Any]      =   12
        UpperCamelCase__							:       int      =   6
    elif "yolos_b" in yolos_name:
        UpperCamelCase__							:       Dict      =   [800, 1344]

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

    return config




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




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

    return name




def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict	,     __UpperCAmelCase:    YolosForObjectDetection   )   ->			dict:
    for key in orig_state_dict.copy().keys():
        UpperCamelCase__							:       Union[str, Any]      =   orig_state_dict.pop(__UpperCAmelCase   )

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

    return orig_state_dict




def 	lowerCAmelCase_					(						)   ->			torch.Tensor:
    UpperCamelCase__							:       str      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Dict      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )
    return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    bool = False   )   ->			Optional[int]:
    UpperCamelCase__							:       List[str]      =   get_yolos_config(__UpperCAmelCase   )

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

    # load 🤗 model
    UpperCamelCase__							:       List[Any]      =   YolosForObjectDetection(__UpperCAmelCase   )
    model.eval()
    UpperCamelCase__							:       Dict      =   convert_state_dict(__UpperCAmelCase	,     __UpperCAmelCase   )
    model.load_state_dict(__UpperCAmelCase   )

    # Check outputs on an image, prepared by YolosImageProcessor
    UpperCamelCase__							:       List[Any]      =   800 if yolos_name != '''yolos_ti''' else 512
    UpperCamelCase__							:       int      =   YolosImageProcessor(format='''coco_detection'''	,     size=__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   image_processor(images=prepare_img()	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Dict      =   model(**__UpperCAmelCase   )
    UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   outputs.logits, outputs.pred_boxes

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


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

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

