


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



     """simple docstring"""






     snake_case					=    YolosConfig()

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

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

     return config
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_ = False    ):



     """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       UpperCAmelCase__						(UpperCamelCase_    ):



     """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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     for key in orig_state_dict.copy().keys():
          snake_case					=    orig_state_dict.pop(UpperCamelCase_    )

          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       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     snake_case					=    Image.open(requests.get(UpperCamelCase_							,stream=UpperCamelCase_    ).raw    )
     return im
@torch.no_grad()
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_ = False    ):



     """simple docstring"""






     snake_case					=    get_yolos_config(UpperCamelCase_    )

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

     # load 🤗 model
     snake_case					=    YolosForObjectDetection(UpperCamelCase_    )
     model.eval()
     snake_case					=    convert_state_dict(UpperCamelCase_							,UpperCamelCase_    )
     model.load_state_dict(UpperCamelCase_    )

     # Check outputs on an image, prepared by YolosImageProcessor
     snake_case					=    8_00 if yolos_name != '''yolos_ti''' else 5_12
     snake_case					=    YolosImageProcessor(format='''coco_detection'''							,size=UpperCamelCase_    )
     snake_case					=    image_processor(images=prepare_img()							,return_tensors='''pt'''    )
     snake_case					=    model(**UpperCamelCase_    )
     snake_case			,  snake_case					=    outputs.logits, outputs.pred_boxes

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


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			str      				=   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      :			List[str]      				=   parser.parse_args()
 convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Any      				=   {
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'detr'
    __magic_name__        =		['past_key_values']
    __magic_name__        =		{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }
    def __init__(	self      , __snake_case=True      , __snake_case=None      , __snake_case=3      , __snake_case=1_0_0      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=True      , __snake_case="relu"      , __snake_case=2_5_6      , __snake_case=0.1      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.02      , __snake_case=1.0      , __snake_case=False      , __snake_case="sine"      , __snake_case="resnet50"      , __snake_case=True      , __snake_case=False      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=1      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=0.1      , **__snake_case      , ):
         if backbone_config is not None and use_timm_backbone:
              raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''   )

         if not use_timm_backbone:
              if backbone_config is None:
                   logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''   )
                   snake_case					=    CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
              elif isinstance(__snake_case      , __snake_case   ):
                   snake_case					=    backbone_config.get('''model_type'''   )
                   snake_case					=    CONFIG_MAPPING[backbone_model_type]
                   snake_case					=    config_class.from_dict(__snake_case   )
              # set timm attributes to None
              snake_case			,  snake_case			,  snake_case					=    None, None, None

         snake_case					=    use_timm_backbone
         snake_case					=    backbone_config
         snake_case					=    num_channels
         snake_case					=    num_queries
         snake_case					=    d_model
         snake_case					=    encoder_ffn_dim
         snake_case					=    encoder_layers
         snake_case					=    encoder_attention_heads
         snake_case					=    decoder_ffn_dim
         snake_case					=    decoder_layers
         snake_case					=    decoder_attention_heads
         snake_case					=    dropout
         snake_case					=    attention_dropout
         snake_case					=    activation_dropout
         snake_case					=    activation_function
         snake_case					=    init_std
         snake_case					=    init_xavier_std
         snake_case					=    encoder_layerdrop
         snake_case					=    decoder_layerdrop
         snake_case					=    encoder_layers
         snake_case					=    auxiliary_loss
         snake_case					=    position_embedding_type
         snake_case					=    backbone
         snake_case					=    use_pretrained_backbone
         snake_case					=    dilation
         # Hungarian matcher
         snake_case					=    class_cost
         snake_case					=    bbox_cost
         snake_case					=    giou_cost
         # Loss coefficients
         snake_case					=    mask_loss_coefficient
         snake_case					=    dice_loss_coefficient
         snake_case					=    bbox_loss_coefficient
         snake_case					=    giou_loss_coefficient
         snake_case					=    eos_coefficient
         super().__init__(is_encoder_decoder=__snake_case      , **__snake_case   )
    @property
    def 		a_      (	self   ):
         return self.encoder_attention_heads
    @property
    def 		a_      (	self   ):
         return self.d_model
    @classmethod
    def 		a_      (	cls      , __snake_case      , **__snake_case   ):
         return cls(backbone_config=__snake_case      , **__snake_case   )




    def 		a_      (	self   ):
         snake_case					=    copy.deepcopy(self.__dict__   )
         if output["backbone_config"] is not None:
              snake_case					=    self.backbone_config.to_dict()
         snake_case					=    self.__class__.model_type
         return output






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]   )
    @property
    def 		a_      (	self   ):
         return 1E-5




    @property
    def 		a_      (	self   ):
         return 1_2




