



"""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()
_lowerCAmelCase    :Optional[Any]     =					logging.get_logger(__name__)





def  lowerCamelCase_						(UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  Any											=    YolosConfig()

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

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

    return config





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





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

    return name





def  lowerCamelCase_						(UpperCamelCase__		:		dict     ,       UpperCamelCase__		:		YolosForObjectDetection							):
    for key in orig_state_dict.copy().keys():
        _UpperCAmelCase							:  List[str]											=    orig_state_dict.pop(UpperCamelCase__							)

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

    return orig_state_dict





def  lowerCamelCase_						():
    _UpperCAmelCase							:  str											=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _UpperCAmelCase							:  Optional[Any]											=    Image.open(requests.get(UpperCamelCase__     ,       stream=UpperCamelCase__							).raw							)
    return im





@torch.no_grad()
def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		bool = False							):
    _UpperCAmelCase							:  Any											=    get_yolos_config(UpperCamelCase__							)

    # load original state_dict
    _UpperCAmelCase							:  Dict											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']

    # load 🤗 model
    _UpperCAmelCase							:  str											=    YolosForObjectDetection(UpperCamelCase__							)
    model.eval()
    _UpperCAmelCase							:  Union[str, Any]											=    convert_state_dict(UpperCamelCase__     ,       UpperCamelCase__							)
    model.load_state_dict(UpperCamelCase__							)

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

    _UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    None, None
    if yolos_name == "yolos_ti":
        _UpperCAmelCase							:  str											=    torch.tensor(
            [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]							)
        _UpperCAmelCase							:  Any											=    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							:  Union[str, Any]											=    torch.tensor(
            [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]							)
        _UpperCAmelCase							:  int											=    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							:  Optional[int]											=    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							:  Dict											=    torch.tensor(
            [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]							)
        _UpperCAmelCase							:  Optional[int]											=    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							:  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...'''							)
        _UpperCAmelCase							:  Optional[Any]											=    model_mapping[yolos_name]
        image_processor.push_to_hub(UpperCamelCase__     ,       organization='''hustvl'''							)
        model.push_to_hub(UpperCamelCase__     ,       organization='''hustvl'''							)


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

  _lowerCAmelCase    :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 re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_lowerCAmelCase    :str     =					object()

# For specifying empty leaf dict `{}`
_lowerCAmelCase    :str     =					object()





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  Dict											=    tuple((re.compile(x + '''$'''							) for x in qs)							)
    for i in range(len(UpperCamelCase__							) - len(UpperCamelCase__							) + 1							):
        _UpperCAmelCase							:  str											=    [x.match(UpperCamelCase__							) for x, y in zip(UpperCamelCase__     ,       ks[i:]							)]
        if matches and all(UpperCamelCase__							):
            return True
    return False





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]							):
    def replace(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Tuple							):
        for rule, replacement in rules:
            if _match(UpperCamelCase__     ,       UpperCamelCase__							):
                return replacement
        return val

    return replace





def  lowerCamelCase_						():
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        (("transformer", "wte", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("attention", "out_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("mlp", "c_fc", "bias"), P('''mp'''							)),
        (("mlp", "c_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("mlp", "c_proj", "bias"), None),
        # layer norms
        ((r"ln_\d+", "bias"), None),
        ((r"\d+", r"ln_\d+", "scale"), None),
        (("ln_f", "bias"), None),
        (("ln_f", "scale"), None),
    ]





def  lowerCamelCase_						(UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[str]											=    _get_partition_rules()
    _UpperCAmelCase							:  List[str]											=    _replacement_rules(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    {k: _unmatched for k in flatten_dict(UpperCamelCase__							)}
    _UpperCAmelCase							:  int											=    {k: replace(UpperCamelCase__     ,       UpperCamelCase__							) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(UpperCamelCase__							)							)


