



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_   (  _a    :		str  ):



    '''simple docstring'''




    UpperCAmelCase_			:    str   						= YolosConfig()

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

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

    return config







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







def 	lowerCamelCase_   (  _a    :		str  ):



    '''simple docstring'''




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

    return name







def 	lowerCamelCase_   (  _a    :		dict					,						_a    :		YolosForObjectDetection  ):



    '''simple docstring'''




    for key in orig_state_dict.copy().keys():
        UpperCAmelCase_			:    Dict   						= orig_state_dict.pop(__SCREAMING_SNAKE_CASE  )

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

    return orig_state_dict







def 	lowerCamelCase_   (  ):



    '''simple docstring'''




    UpperCAmelCase_			:    List[str]   						= 'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCAmelCase_			:    Dict   						= Image.open(requests.get(__SCREAMING_SNAKE_CASE					,						stream=__SCREAMING_SNAKE_CASE  ).raw  )
    return im







@torch.no_grad()
def 	lowerCamelCase_   (  _a    :		str					,						_a    :		str					,						_a    :		str					,						_a    :		bool = False  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Tuple   						= get_yolos_config(__SCREAMING_SNAKE_CASE  )

    # load original state_dict
    UpperCAmelCase_			:    int   						= torch.load(__SCREAMING_SNAKE_CASE					,						map_location="""cpu"""  )['model']

    # load 🤗 model
    UpperCAmelCase_			:    Tuple   						= YolosForObjectDetection(__SCREAMING_SNAKE_CASE  )
    model.eval()
    UpperCAmelCase_			:    int   						= convert_state_dict(__SCREAMING_SNAKE_CASE					,						__SCREAMING_SNAKE_CASE  )
    model.load_state_dict(__SCREAMING_SNAKE_CASE  )

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

    UpperCAmelCase_			:    List[str]   						= None, None
    if yolos_name == "yolos_ti":
        UpperCAmelCase_			:    List[str]   						= 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]]  )
        UpperCAmelCase_			:    Optional[int]   						= 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":
        UpperCAmelCase_			:    Union[str, Any]   						= 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]]  )
        UpperCAmelCase_			:    List[str]   						= 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":
        UpperCAmelCase_			:    List[str]   						= 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]]  )
        UpperCAmelCase_			:    Union[str, Any]   						= 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":
        UpperCAmelCase_			:    List[str]   						= 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]]  )
        UpperCAmelCase_			:    Dict   						= 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":
        UpperCAmelCase_			:    Union[str, Any]   						= 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]]  )
        UpperCAmelCase_			:    int   						= 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]					,						__SCREAMING_SNAKE_CASE					,						atol=1E-4  )
    assert torch.allclose(pred_boxes[0, :3, :3]					,						__SCREAMING_SNAKE_CASE					,						atol=1E-4  )

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

    if push_to_hub:
        UpperCAmelCase_			:    Optional[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_			:    Any   						= model_mapping[yolos_name]
        image_processor.push_to_hub(__SCREAMING_SNAKE_CASE					,						organization="""hustvl"""  )
        model.push_to_hub(__SCREAMING_SNAKE_CASE					,						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)

"""simple docstring"""







__SCREAMING_SNAKE_CASE    	=[
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager

