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()
a_							=      logging.get_logger(__name__)




def       __lowerCAmelCase      (						A_   : str					)    ->  YolosConfig:
  __UpperCAmelCase						    =       YolosConfig()

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

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

  return config




def       __lowerCAmelCase      (						A_   : dict						,	A_   : YolosConfig						,	A_   : bool = False					)    ->  str:
  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						    =       state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''					)
    __UpperCAmelCase						    =       state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''					)
    # next, add query, keys and values (in that order) to the state dict
    __UpperCAmelCase						    =       in_proj_weight[: config.hidden_size, :]
    __UpperCAmelCase						    =       in_proj_bias[: config.hidden_size]
    __UpperCAmelCase						    =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    __UpperCAmelCase						    =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    __UpperCAmelCase						    =       in_proj_weight[-config.hidden_size :, :]
    __UpperCAmelCase						    =       in_proj_bias[-config.hidden_size :]




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

  return name




def       __lowerCAmelCase      (						A_   : dict						,	A_   : YolosForObjectDetection					)    ->  dict:
  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase						    =       orig_state_dict.pop(A_					)

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

  return orig_state_dict




def       __lowerCAmelCase      (						)    ->  torch.Tensor:
  __UpperCAmelCase						    =       "http://images.cocodataset.org/val2017/000000039769.jpg"
  __UpperCAmelCase						    =       Image.open(requests.get(A_						,	stream=A_					).raw					)
  return im




@torch.no_grad()
def       __lowerCAmelCase      (						A_   : str						,	A_   : str						,	A_   : str						,	A_   : bool = False					)    ->  List[str]:
  __UpperCAmelCase						    =       get_yolos_config(A_					)

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

  # load 🤗 model
  __UpperCAmelCase						    =       YolosForObjectDetection(A_					)
  model.eval()
  __UpperCAmelCase						    =       convert_state_dict(A_						,	A_					)
  model.load_state_dict(A_					)

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

  __UpperCAmelCase    ,		__UpperCAmelCase						    =       None, None
  if yolos_name == "yolos_ti":
    __UpperCAmelCase						    =       torch.tensor(
        [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]					)
  elif yolos_name == "yolos_s_200_pre":
    __UpperCAmelCase						    =       torch.tensor(
        [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]					)
  elif yolos_name == "yolos_s_300_pre":
    __UpperCAmelCase						    =       torch.tensor(
        [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]					)
  elif yolos_name == "yolos_s_dWr":
    __UpperCAmelCase						    =       torch.tensor(
        [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]					)
  elif yolos_name == "yolos_base":
    __UpperCAmelCase						    =       torch.tensor(
        [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]					)
    __UpperCAmelCase						    =       torch.tensor(
        [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]					)
  else:
    raise ValueError(F'''Unknown yolos_name: {yolos_name}'''					)

  assert torch.allclose(logits[0, :3, :3]						,	A_						,	atol=1e-4					)
  assert torch.allclose(pred_boxes[0, :3, :3]						,	A_						,	atol=1e-4					)

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

  if push_to_hub:
    __UpperCAmelCase						    =       {
        "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						    =       model_mapping[yolos_name]
    image_processor.push_to_hub(A_						,	organization="hustvl"					)
    model.push_to_hub(A_						,	organization="hustvl"					)


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

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




from abc import ABC, abstractmethod
from argparse import ArgumentParser


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  @staticmethod
  @abstractmethod
  def 	_UpperCAmelCase				(				__lowerCAmelCase:			ArgumentParser   ) -> Tuple:




    '''simple docstring'''




    raise NotImplementedError()






  @abstractmethod
  def 	_UpperCAmelCase				(				self:			List[str]   ) -> List[Any]:




    '''simple docstring'''




    raise NotImplementedError()



