





'''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()
lowercase_					= logging.get_logger(__name__)





def lowerCamelCase						(     __lowerCamelCase :    str    )						->YolosConfig:
    _SCREAMING_SNAKE_CASE									=			YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        _SCREAMING_SNAKE_CASE									=			192
        _SCREAMING_SNAKE_CASE									=			768
        _SCREAMING_SNAKE_CASE									=			12
        _SCREAMING_SNAKE_CASE									=			3
        _SCREAMING_SNAKE_CASE									=			[800, 1333]
        _SCREAMING_SNAKE_CASE									=			False
    elif yolos_name == "yolos_s_dWr":
        _SCREAMING_SNAKE_CASE									=			330
        _SCREAMING_SNAKE_CASE									=			14
        _SCREAMING_SNAKE_CASE									=			6
        _SCREAMING_SNAKE_CASE									=			1320
    elif "yolos_s" in yolos_name:
        _SCREAMING_SNAKE_CASE									=			384
        _SCREAMING_SNAKE_CASE									=			1536
        _SCREAMING_SNAKE_CASE									=			12
        _SCREAMING_SNAKE_CASE									=			6
    elif "yolos_b" in yolos_name:
        _SCREAMING_SNAKE_CASE									=			[800, 1344]

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

    return config





def lowerCamelCase						(     __lowerCamelCase :    dict					,       __lowerCamelCase :    YolosConfig					,       __lowerCamelCase :    bool = False    )						->Any:
    for i in range(config.num_hidden_layers    ):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        _SCREAMING_SNAKE_CASE									=			state_dict.pop(F'blocks.{i}.attn.qkv.weight'    )
        _SCREAMING_SNAKE_CASE									=			state_dict.pop(F'blocks.{i}.attn.qkv.bias'    )
        # next, add query, keys and values (in that order) to the state dict
        _SCREAMING_SNAKE_CASE									=			in_proj_weight[: config.hidden_size, :]
        _SCREAMING_SNAKE_CASE									=			in_proj_bias[: config.hidden_size]
        _SCREAMING_SNAKE_CASE									=			in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        _SCREAMING_SNAKE_CASE									=			in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        _SCREAMING_SNAKE_CASE									=			in_proj_weight[-config.hidden_size :, :]
        _SCREAMING_SNAKE_CASE									=			in_proj_bias[-config.hidden_size :]





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

    return name





def lowerCamelCase						(     __lowerCamelCase :    dict					,       __lowerCamelCase :    YolosForObjectDetection    )						->dict:
    for key in orig_state_dict.copy().keys():
        _SCREAMING_SNAKE_CASE									=			orig_state_dict.pop(__lowerCamelCase    )

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

    return orig_state_dict





def lowerCamelCase						(     )						->torch.Tensor:
    _SCREAMING_SNAKE_CASE									=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _SCREAMING_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:
    _SCREAMING_SNAKE_CASE									=			get_yolos_config(__lowerCamelCase    )

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

    # load 🤗 model
    _SCREAMING_SNAKE_CASE									=			YolosForObjectDetection(__lowerCamelCase    )
    model.eval()
    _SCREAMING_SNAKE_CASE									=			convert_state_dict(__lowerCamelCase					,       __lowerCamelCase    )
    model.load_state_dict(__lowerCamelCase    )

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

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


if __name__ == "__main__":
      lowercase_					= 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."""
      )

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










'''simple docstring'''

from typing import List

from .keymap import KEYMAP, get_character





def lowerCamelCase						(     __lowerCamelCase :    str    )						->Optional[int]:

    def decorator(__lowerCamelCase :    int    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       """handle_key"""					,       []    )
        handle += [key]
        setattr(__lowerCamelCase					,       """handle_key"""					,       __lowerCamelCase    )
        return func

    return decorator





def lowerCamelCase						(     *__lowerCamelCase :    List[str]    )						->Dict:

    def decorator(__lowerCamelCase :    int    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       """handle_key"""					,       []    )
        handle += keys
        setattr(__lowerCamelCase					,       """handle_key"""					,       __lowerCamelCase    )
        return func

    return decorator


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __new__(		cls ,		A ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			super().__new__(cls ,		A ,		A ,		A       )
        if not hasattr(A ,		"""key_handler"""       ):
            setattr(A ,		"""key_handler""" ,		{}       )
        setattr(A ,		"""handle_input""" ,		KeyHandler.handle_input       )

        for value in attrs.values():
            _SCREAMING_SNAKE_CASE									=			getattr(A ,		"""handle_key""" ,		[]       )
            for key in handled_keys:
                _SCREAMING_SNAKE_CASE									=			value
        return new_cls





    @staticmethod
    def 			snake_case_(		cls       )		->   str:
        _SCREAMING_SNAKE_CASE									=			get_character()
        if char != KEYMAP["undefined"]:
            _SCREAMING_SNAKE_CASE									=			ord(A       )
        _SCREAMING_SNAKE_CASE									=			cls.key_handler.get(A       )
        if handler:
            _SCREAMING_SNAKE_CASE									=			char
            return handler(cls       )
        else:
            return None





def lowerCamelCase						(     cls :    Any    )						->Dict:
    return KeyHandler(cls.__name__					,       cls.__bases__					,       cls.__dict__.copy()    )



