

'''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 :      List[Any]					 =     logging.get_logger(__name__)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			YolosConfig:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =YolosConfig()

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

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

     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     dict							, _UpperCamelCase						:     YolosConfig							, _UpperCamelCase						:     bool = False				)      ->			Union[str, Any]:
     """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)
          _SCREAMING_SNAKE_CASE							    =state_dict.pop(f"blocks.{i}.attn.qkv.weight"				)
          _SCREAMING_SNAKE_CASE							    =state_dict.pop(f"blocks.{i}.attn.qkv.bias"				)
          # next, add query, keys and values (in that order) to the state dict
          _SCREAMING_SNAKE_CASE							    =in_proj_weight[: config.hidden_size, :]
          _SCREAMING_SNAKE_CASE							    =in_proj_bias[: config.hidden_size]
          _SCREAMING_SNAKE_CASE							    =in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _SCREAMING_SNAKE_CASE							    =in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          _SCREAMING_SNAKE_CASE							    =in_proj_weight[-config.hidden_size :, :]
          _SCREAMING_SNAKE_CASE							    =in_proj_bias[-config.hidden_size :]


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			str:
     """simple docstring"""




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

     return name


def        _lowerCAmelCase					(						_UpperCamelCase						:     dict							, _UpperCamelCase						:     YolosForObjectDetection				)      ->			dict:
     """simple docstring"""




     for key in orig_state_dict.copy().keys():
          _SCREAMING_SNAKE_CASE							    =orig_state_dict.pop(_UpperCamelCase				)

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

     return orig_state_dict


def        _lowerCAmelCase					(						)      ->			torch.Tensor:
     """simple docstring"""




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


@torch.no_grad()
def        _lowerCAmelCase					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     str							, _UpperCamelCase						:     str							, _UpperCamelCase						:     bool = False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =get_yolos_config(_UpperCamelCase				)

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

     # load 🤗 model
     _SCREAMING_SNAKE_CASE							    =YolosForObjectDetection(_UpperCamelCase				)
     model.eval()
     _SCREAMING_SNAKE_CASE							    =convert_state_dict(_UpperCamelCase							, _UpperCamelCase				)
     model.load_state_dict(_UpperCamelCase				)

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

     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =None, None
     if yolos_name == "yolos_ti":
          _SCREAMING_SNAKE_CASE							    =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]]				)
          _SCREAMING_SNAKE_CASE							    =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":
          _SCREAMING_SNAKE_CASE							    =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]]				)
          _SCREAMING_SNAKE_CASE							    =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":
          _SCREAMING_SNAKE_CASE							    =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]]				)
          _SCREAMING_SNAKE_CASE							    =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":
          _SCREAMING_SNAKE_CASE							    =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]]				)
          _SCREAMING_SNAKE_CASE							    =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":
          _SCREAMING_SNAKE_CASE							    =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]]				)
          _SCREAMING_SNAKE_CASE							    =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]							, _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:
          _SCREAMING_SNAKE_CASE							    ={
              '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...'				)
          _SCREAMING_SNAKE_CASE							    =model_mapping[yolos_name]
          image_processor.push_to_hub(_UpperCamelCase							, organization='hustvl'				)
          model.push_to_hub(_UpperCamelCase							, organization='hustvl'				)


if __name__ == "__main__":
     lowerCamelCase :      Optional[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'''
from typing import TYPE_CHECKING

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


lowerCamelCase :      int					 =     {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     ["GLPNFeatureExtractor"]
     lowerCamelCase :      Optional[int]					 =     ["GLPNImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GLPNForDepthEstimation",
         "GLPNLayer",
         "GLPNModel",
         "GLPNPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_glpn import GLPNFeatureExtractor
          from .image_processing_glpn import GLPNImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_glpn import (
              GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
              GLPNForDepthEstimation,
              GLPNLayer,
              GLPNModel,
              GLPNPreTrainedModel,
          )


else:
     import sys

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