

"""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										=		logging.get_logger(__name__)





def 	lowercase      (   __UpperCamelCase						) ->      YolosConfig:
 __magic_name__     =      YolosConfig()

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

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

 return config





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





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

 return name





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      dict:
 for key in orig_state_dict.copy().keys():
  __magic_name__     =      orig_state_dict.pop(__UpperCamelCase						)

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

 return orig_state_dict





def 	lowercase      (   ) ->      torch.Tensor:
 __magic_name__     =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __magic_name__     =      Image.open(requests.get(__UpperCamelCase      ,   stream=__UpperCamelCase						).raw						)
 return im





@torch.no_grad()
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase = False						) ->      int:
 __magic_name__     =      get_yolos_config(__UpperCamelCase						)

 # load original state_dict
 __magic_name__     =      torch.load(__UpperCamelCase      ,   map_location='''cpu'''						)['''model''']

 # load 🤗 model
 __magic_name__     =      YolosForObjectDetection(__UpperCamelCase						)
 model.eval()
 __magic_name__     =      convert_state_dict(__UpperCamelCase      ,   __UpperCamelCase						)
 model.load_state_dict(__UpperCamelCase						)

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

 __magic_name__   ,						__magic_name__     =      None, None
 if yolos_name == "yolos_ti":
  __magic_name__     =      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]]						)
  __magic_name__     =      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":
  __magic_name__     =      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]]						)
  __magic_name__     =      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":
  __magic_name__     =      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]]						)
  __magic_name__     =      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":
  __magic_name__     =      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]]						)
  __magic_name__     =      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":
  __magic_name__     =      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]]						)
  __magic_name__     =      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]      ,   __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:
  __magic_name__     =      {
      '''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...'''						)
  __magic_name__     =      model_mapping[yolos_name]
  image_processor.push_to_hub(__UpperCamelCase      ,   organization='''hustvl'''						)
  model.push_to_hub(__UpperCamelCase      ,   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 typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase										=		logging.get_logger(__name__)
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       ['''pixel_values''']







     def __init__(      self ,						UpperCamelCase_ = True ,						UpperCamelCase_ = 32 ,						UpperCamelCase_=PILImageResampling.BILINEAR ,						UpperCamelCase_ = True ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize
      __magic_name__     =      do_rescale
      __magic_name__     =      size_divisor
      __magic_name__     =      resample
      super().__init__(**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      __magic_name__   ,						__magic_name__     =      get_image_size(UpperCamelCase_       )
      # Rounds the height and width down to the closest multiple of size_divisor
      __magic_name__     =      height // size_divisor * size_divisor
      __magic_name__     =      width // size_divisor * size_divisor
      __magic_name__     =      resize(UpperCamelCase_ ,						(new_h, new_w) ,						resample=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )
      return image







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      return rescale(image=UpperCamelCase_ ,						scale=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_=None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = ChannelDimension.FIRST ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize if do_resize is not None else self.do_resize
      __magic_name__     =      do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__     =      size_divisor if size_divisor is not None else self.size_divisor
      __magic_name__     =      resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
       raise ValueError('''size_divisor is required for resizing'''       )

      __magic_name__     =      make_list_of_images(UpperCamelCase_       )

      if not valid_images(UpperCamelCase_       ):
       raise ValueError('''Invalid image(s)'''       )

      # All transformations expect numpy arrays.
      __magic_name__     =      [to_numpy_array(UpperCamelCase_       ) for img in images]

      if do_resize:
       __magic_name__     =      [self.resize(UpperCamelCase_ ,						size_divisor=UpperCamelCase_ ,						resample=UpperCamelCase_       ) for image in images]

      if do_rescale:
       __magic_name__     =      [self.rescale(UpperCamelCase_ ,						scale=1 / 255       ) for image in images]

      __magic_name__     =      [to_channel_dimension_format(UpperCamelCase_ ,						UpperCamelCase_       ) for image in images]

      __magic_name__     =      {'''pixel_values''': images}
      return BatchFeature(data=UpperCamelCase_ ,						tensor_type=UpperCamelCase_       )

