




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			:	Any							 =     logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> YolosConfig:



       """simple docstring"""

       A__   				=					YolosConfig()

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

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

       return config







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_ = False		)     -> Tuple:



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







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

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

       return name







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> dict:



       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              A__   				=					orig_state_dict.pop(lowercase_		)

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

       return orig_state_dict







def SCREAMING_SNAKE_CASE       ( )     -> torch.Tensor:



       """simple docstring"""

       A__   				=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__   				=					Image.open(requests.get(lowercase_				,       stream=lowercase_		).raw		)
       return im







@torch.no_grad()
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_ = False		)     -> int:



       """simple docstring"""

       A__   				=					get_yolos_config(lowercase_		)

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

       # load 🤗 model
       A__   				=					YolosForObjectDetection(lowercase_		)
       model.eval()
       A__   				=					convert_state_dict(lowercase_				,       lowercase_		)
       model.load_state_dict(lowercase_		)

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

       A__				,							A__   				=					None, None
       if yolos_name == "yolos_ti":
              A__   				=					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]]		)
              A__   				=					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":
              A__   				=					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]]		)
              A__   				=					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":
              A__   				=					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]]		)
              A__   				=					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":
              A__   				=					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]]		)
              A__   				=					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":
              A__   				=					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]]		)
              A__   				=					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]				,       lowercase_				,       atol=1E-4		)
       assert torch.allclose(pred_boxes[0, :3, :3]				,       lowercase_				,       atol=1E-4		)

       Path(lowercase_		).mkdir(exist_ok=lowercase_		)
       print(f"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""		)
       model.save_pretrained(lowercase_		)
       print(f"""Saving image processor to {pytorch_dump_folder_path}"""		)
       image_processor.save_pretrained(lowercase_		)

       if push_to_hub:
              A__   				=					{
                  '''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...'''		)
              A__   				=					model_mapping[yolos_name]
              image_processor.push_to_hub(lowercase_				,       organization='''hustvl'''		)
              model.push_to_hub(lowercase_				,       organization='''hustvl'''		)


if __name__ == "__main__":
  _lowerCamelCase			:	List[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			:	List[str]							 =     parser.parse_args()
  convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


_lowerCamelCase			:	Union[str, Any]							 =     {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase			:	Optional[Any]							 =     ["""DPTFeatureExtractor"""]
  _lowerCamelCase			:	int							 =     ["""DPTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase			:	Optional[int]							 =     [
      """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPTForDepthEstimation""",
      """DPTForSemanticSegmentation""",
      """DPTModel""",
      """DPTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_dpt import DPTFeatureExtractor
    from .image_processing_dpt import DPTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpt import (
        DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPTForDepthEstimation,
        DPTForSemanticSegmentation,
        DPTModel,
        DPTPreTrainedModel,
    )


else:
  import sys

  _lowerCamelCase			:	Any							 =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


