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__	   =						logging.get_logger(__name__)



def      __lowercase				(      _UpperCAmelCase			)      ->     YolosConfig:






  '''simple docstring'''

  __lowercase					=		YolosConfig()

  # size of the architecture
  if "yolos_ti" in yolos_name:
    __lowercase					=		192
    __lowercase					=		768
    __lowercase					=		12
    __lowercase					=		3
    __lowercase					=		[800, 1_333]
    __lowercase					=		False
  elif yolos_name == "yolos_s_dWr":
    __lowercase					=		330
    __lowercase					=		14
    __lowercase					=		6
    __lowercase					=		1_320
  elif "yolos_s" in yolos_name:
    __lowercase					=		384
    __lowercase					=		1_536
    __lowercase					=		12
    __lowercase					=		6
  elif "yolos_b" in yolos_name:
    __lowercase					=		[800, 1_344]

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

  return config



def      __lowercase				(      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = False			)      ->     Optional[int]:






  '''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)
    __lowercase					=		state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''			)
    __lowercase					=		state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''			)
    # next, add query, keys and values (in that order) to the state dict
    __lowercase					=		in_proj_weight[: config.hidden_size, :]
    __lowercase					=		in_proj_bias[: config.hidden_size]
    __lowercase					=		in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    __lowercase					=		in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    __lowercase					=		in_proj_weight[-config.hidden_size :, :]
    __lowercase					=		in_proj_bias[-config.hidden_size :]



def      __lowercase				(      _UpperCAmelCase			)      ->     str:






  '''simple docstring'''

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

  return name



def      __lowercase				(      _UpperCAmelCase			, _UpperCAmelCase			)      ->     dict:






  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    __lowercase					=		orig_state_dict.pop(__lowerCAmelCase			)

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

  return orig_state_dict



def      __lowercase				(      )      ->     torch.Tensor:






  '''simple docstring'''

  __lowercase					=		"http://images.cocodataset.org/val2017/000000039769.jpg"
  __lowercase					=		Image.open(requests.get(__lowerCAmelCase			, stream=__lowerCAmelCase			).raw			)
  return im



@torch.no_grad()
def      __lowercase				(      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = False			)      ->     str:






  '''simple docstring'''

  __lowercase					=		get_yolos_config(__lowerCAmelCase			)

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

  # load 🤗 model
  __lowercase					=		YolosForObjectDetection(__lowerCAmelCase			)
  model.eval()
  __lowercase					=		convert_state_dict(__lowerCAmelCase			, __lowerCAmelCase			)
  model.load_state_dict(__lowerCAmelCase			)

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

  __lowercase					,     __lowercase					=		None, None
  if yolos_name == "yolos_ti":
    __lowercase					=		torch.tensor(
        [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]]			)
    __lowercase					=		torch.tensor(
        [[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]]			)
  elif yolos_name == "yolos_s_200_pre":
    __lowercase					=		torch.tensor(
        [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]			)
    __lowercase					=		torch.tensor(
        [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]			)
  elif yolos_name == "yolos_s_300_pre":
    __lowercase					=		torch.tensor(
        [[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]]			)
    __lowercase					=		torch.tensor(
        [[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]]			)
  elif yolos_name == "yolos_s_dWr":
    __lowercase					=		torch.tensor(
        [[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]]			)
    __lowercase					=		torch.tensor(
        [[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]]			)
  elif yolos_name == "yolos_base":
    __lowercase					=		torch.tensor(
        [[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]]			)
    __lowercase					=		torch.tensor(
        [[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]]			)
  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:
    __lowercase					=		{
        "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..."			)
    __lowercase					=		model_mapping[yolos_name]
    image_processor.push_to_hub(__lowerCAmelCase			, organization="hustvl"			)
    model.push_to_hub(__lowerCAmelCase			, organization="hustvl"			)


if __name__ == "__main__":
     lowerCAmelCase__	   =						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__	   =						parser.parse_args()
     convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''




from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)

