"""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()
__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				str													=	YolosConfig()

     # size of the architecture
     if "yolos_ti" in yolos_name:
          lowercase_	:				int													=	1_92
          lowercase_	:				List[str]													=	7_68
          lowercase_	:				Optional[Any]													=	12
          lowercase_	:				Tuple													=	3
          lowercase_	:				Union[str, Any]													=	[8_00, 13_33]
          lowercase_	:				List[str]													=	False
     elif yolos_name == "yolos_s_dWr":
          lowercase_	:				Optional[int]													=	3_30
          lowercase_	:				Tuple													=	14
          lowercase_	:				Union[str, Any]													=	6
          lowercase_	:				str													=	13_20
     elif "yolos_s" in yolos_name:
          lowercase_	:				Tuple													=	3_84
          lowercase_	:				Optional[Any]													=	15_36
          lowercase_	:				List[Any]													=	12
          lowercase_	:				List[Any]													=	6
     elif "yolos_b" in yolos_name:
          lowercase_	:				Dict													=	[8_00, 13_44]

     lowercase_	:				Optional[Any]													=	91
     lowercase_	:				Union[str, Any]													=	'huggingface/label-files'
     lowercase_	:				Union[str, Any]													=	'coco-detection-id2label.json'
     lowercase_	:				str													=	json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			repo_type='dataset'      )     ,			'r'      )      )
     lowercase_	:				Tuple													=	{int(__SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}
     lowercase_	:				str													=	idalabel
     lowercase_	:				int													=	{v: k for k, v in idalabel.items()}

     return config



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     dict     ,			__SCREAMING_SNAKE_CASE							:     YolosConfig     ,			__SCREAMING_SNAKE_CASE							:     bool = False      ):
     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_	:				Optional[Any]													=	state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''      )
          lowercase_	:				List[str]													=	state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''      )
          # next, add query, keys and values (in that order) to the state dict
          lowercase_	:				Any													=	in_proj_weight[: config.hidden_size, :]
          lowercase_	:				Optional[Any]													=	in_proj_bias[: config.hidden_size]
          lowercase_	:				List[Any]													=	in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          lowercase_	:				Any													=	in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          lowercase_	:				int													=	in_proj_weight[-config.hidden_size :, :]
          lowercase_	:				Union[str, Any]													=	in_proj_bias[-config.hidden_size :]



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str      ):
     if "backbone" in name:
          lowercase_	:				str													=	name.replace('backbone'     ,			'vit'      )
     if "cls_token" in name:
          lowercase_	:				Any													=	name.replace('cls_token'     ,			'embeddings.cls_token'      )
     if "det_token" in name:
          lowercase_	:				Tuple													=	name.replace('det_token'     ,			'embeddings.detection_tokens'      )
     if "mid_pos_embed" in name:
          lowercase_	:				int													=	name.replace('mid_pos_embed'     ,			'encoder.mid_position_embeddings'      )
     if "pos_embed" in name:
          lowercase_	:				Optional[Any]													=	name.replace('pos_embed'     ,			'embeddings.position_embeddings'      )
     if "patch_embed.proj" in name:
          lowercase_	:				Union[str, Any]													=	name.replace('patch_embed.proj'     ,			'embeddings.patch_embeddings.projection'      )
     if "blocks" in name:
          lowercase_	:				Any													=	name.replace('blocks'     ,			'encoder.layer'      )
     if "attn.proj" in name:
          lowercase_	:				Union[str, Any]													=	name.replace('attn.proj'     ,			'attention.output.dense'      )
     if "attn" in name:
          lowercase_	:				str													=	name.replace('attn'     ,			'attention.self'      )
     if "norm1" in name:
          lowercase_	:				str													=	name.replace('norm1'     ,			'layernorm_before'      )
     if "norm2" in name:
          lowercase_	:				str													=	name.replace('norm2'     ,			'layernorm_after'      )
     if "mlp.fc1" in name:
          lowercase_	:				List[str]													=	name.replace('mlp.fc1'     ,			'intermediate.dense'      )
     if "mlp.fc2" in name:
          lowercase_	:				Tuple													=	name.replace('mlp.fc2'     ,			'output.dense'      )
     if "class_embed" in name:
          lowercase_	:				Optional[Any]													=	name.replace('class_embed'     ,			'class_labels_classifier'      )
     if "bbox_embed" in name:
          lowercase_	:				List[Any]													=	name.replace('bbox_embed'     ,			'bbox_predictor'      )
     if "vit.norm" in name:
          lowercase_	:				Tuple													=	name.replace('vit.norm'     ,			'vit.layernorm'      )

     return name



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     dict     ,			__SCREAMING_SNAKE_CASE							:     YolosForObjectDetection      ):
     for key in orig_state_dict.copy().keys():
          lowercase_	:				Dict													=	orig_state_dict.pop(__SCREAMING_SNAKE_CASE      )

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

     return orig_state_dict



def 			lowercase__(       ):
     lowercase_	:				List[str]													=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase_	:				Dict													=	Image.open(requests.get(__SCREAMING_SNAKE_CASE     ,			stream=__SCREAMING_SNAKE_CASE      ).raw      )
     return im



@torch.no_grad()
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     bool = False      ):
     lowercase_	:				Tuple													=	get_yolos_config(__SCREAMING_SNAKE_CASE      )

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

     # load 🤗 model
     lowercase_	:				Tuple													=	YolosForObjectDetection(__SCREAMING_SNAKE_CASE      )
     model.eval()
     lowercase_	:				int													=	convert_state_dict(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
     model.load_state_dict(__SCREAMING_SNAKE_CASE      )

     # Check outputs on an image, prepared by YolosImageProcessor
     lowercase_	:				Tuple													=	8_00 if yolos_name != 'yolos_ti' else 5_12
     lowercase_	:				str													=	YolosImageProcessor(format='coco_detection'     ,			size=__SCREAMING_SNAKE_CASE      )
     lowercase_	:				int													=	image_processor(images=prepare_img()     ,			return_tensors='pt'      )
     lowercase_	:				Dict													=	model(**__SCREAMING_SNAKE_CASE      )
     lowercase_							,  lowercase_	:				Union[str, Any]													=	outputs.logits, outputs.pred_boxes

     lowercase_							,  lowercase_	:				List[str]													=	None, None
     if yolos_name == "yolos_ti":
          lowercase_	:				List[str]													=	torch.tensor(
              [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]      )
          lowercase_	:				Optional[int]													=	torch.tensor(
              [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]      )
     elif yolos_name == "yolos_s_200_pre":
          lowercase_	:				Union[str, Any]													=	torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]      )
          lowercase_	:				List[str]													=	torch.tensor(
              [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]      )
     elif yolos_name == "yolos_s_300_pre":
          lowercase_	:				List[str]													=	torch.tensor(
              [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]      )
          lowercase_	:				Union[str, Any]													=	torch.tensor(
              [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]      )
     elif yolos_name == "yolos_s_dWr":
          lowercase_	:				List[str]													=	torch.tensor(
              [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]      )
          lowercase_	:				Dict													=	torch.tensor(
              [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]      )
     elif yolos_name == "yolos_base":
          lowercase_	:				Union[str, Any]													=	torch.tensor(
              [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]      )
          lowercase_	:				int													=	torch.tensor(
              [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]      )
     else:
          raise ValueError(F'''Unknown yolos_name: {yolos_name}'''      )

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

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

     if push_to_hub:
          lowercase_	:				Optional[Any]													=	{
              '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_	:				Any													=	model_mapping[yolos_name]
          image_processor.push_to_hub(__SCREAMING_SNAKE_CASE     ,			organization='hustvl'      )
          model.push_to_hub(__SCREAMING_SNAKE_CASE     ,			organization='hustvl'      )


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

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


"""simple docstring"""







import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__SCREAMING_SNAKE_CASE    	="python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
       pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
       pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
       if pkg in deps:
              if pkg == "tokenizers":
                     # must be loaded here, or else tqdm check may fail
                     from .utils import is_tokenizers_available

                     if not is_tokenizers_available():
                            continue  # not required, check version only if installed

              require_version_core(deps[pkg])
       else:
              raise ValueError(F"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None      ):
     require_version(deps[pkg]     ,			__SCREAMING_SNAKE_CASE      )

