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




def 							lowerCamelCase	(      UpperCAmelCase__       :					str )			->      YolosConfig:
							lowercase_					:			int										=       YolosConfig()

							# size of the architecture
							if "yolos_ti" in yolos_name:
														lowercase_					:			Optional[Any]										=       192
														lowercase_					:			Any										=       768
														lowercase_					:			Tuple										=       12
														lowercase_					:			Optional[Any]										=       3
														lowercase_					:			List[Any]										=       [800, 1333]
														lowercase_					:			Optional[int]										=       False
							elif yolos_name == "yolos_s_dWr":
														lowercase_					:			int										=       330
														lowercase_					:			Union[str, Any]										=       14
														lowercase_					:			Dict										=       6
														lowercase_					:			List[str]										=       1320
							elif "yolos_s" in yolos_name:
														lowercase_					:			Optional[Any]										=       384
														lowercase_					:			Optional[int]										=       1536
														lowercase_					:			Optional[int]										=       12
														lowercase_					:			Dict										=       6
							elif "yolos_b" in yolos_name:
														lowercase_					:			List[str]										=       [800, 1344]

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

							return config




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




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

							return name




def 							lowerCamelCase	(      UpperCAmelCase__       :					dict      ,      UpperCAmelCase__       :					YolosForObjectDetection )			->      dict:
							for key in orig_state_dict.copy().keys():
														lowercase_					:			Optional[int]										=       orig_state_dict.pop(UpperCAmelCase__ )

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

							return orig_state_dict




def 							lowerCamelCase	(      )			->      torch.Tensor:
							lowercase_					:			Tuple										=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowercase_					:			List[str]										=       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]:
							lowercase_					:			Optional[Any]										=       get_yolos_config(UpperCAmelCase__ )

							# load original state_dict
							lowercase_					:			Optional[int]										=       torch.load(UpperCAmelCase__      ,      map_location="""cpu""" )["""model"""]

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

							# Check outputs on an image, prepared by YolosImageProcessor
							lowercase_					:			Tuple										=       800 if yolos_name != """yolos_ti""" else 512
							lowercase_					:			List[str]										=       YolosImageProcessor(format="""coco_detection"""      ,      size=UpperCAmelCase__ )
							lowercase_					:			Any										=       image_processor(images=prepare_img()      ,      return_tensors="""pt""" )
							lowercase_					:			List[str]										=       model(**UpperCAmelCase__ )
							lowercase_						,						lowercase_					:			int										=       outputs.logits, outputs.pred_boxes

							lowercase_						,						lowercase_					:			Optional[int]										=       None, None
							if yolos_name == "yolos_ti":
														lowercase_					:			Optional[int]										=       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_					:			Tuple										=       torch.tensor(
														    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] )
														lowercase_					:			Optional[Any]										=       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_					:			Optional[Any]										=       torch.tensor(
														    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]] )
														lowercase_					:			Optional[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_					:			Dict										=       torch.tensor(
														    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]] )
														lowercase_					:			int										=       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_					:			List[str]										=       torch.tensor(
														    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]] )
														lowercase_					:			Any										=       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]      ,      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:
														lowercase_					:			int										=       {
														    """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_					:			str										=       model_mapping[yolos_name]
														image_processor.push_to_hub(UpperCAmelCase__      ,      organization="""hustvl""" )
														model.push_to_hub(UpperCAmelCase__      ,      organization="""hustvl""" )


if __name__ == "__main__":
						_lowercase						: Tuple						=						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."
						)

						_lowercase						: Dict						=						parser.parse_args()
						convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




_lowercase						: str						=						tuple[float, float, float]
_lowercase						: List[Any]						=						tuple[float, float, float]




def 							lowerCamelCase	(      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad )			->      Vectorad:
							lowercase_					:			List[str]										=       end_pointa[0] - end_pointa[0]
							lowercase_					:			Union[str, Any]										=       end_pointa[1] - end_pointa[1]
							lowercase_					:			List[Any]										=       end_pointa[2] - end_pointa[2]
							return (x, y, z)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Vectorad      ,      UpperCAmelCase__       :					Vectorad )			->      Vectorad:
							lowercase_					:			List[Any]										=       ab[1] * ac[2] - ab[2] * ac[1]  # *i
							lowercase_					:			Union[str, Any]										=       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
							lowercase_					:			List[str]										=       ab[0] * ac[1] - ab[1] * ac[0]  # *k
							return (x, y, z)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Vectorad      ,      UpperCAmelCase__       :					int )			->      bool:
							return tuple(round(UpperCAmelCase__      ,      UpperCAmelCase__ ) for x in vector ) == (0, 0, 0)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					int = 10 )			->      bool:
							lowercase_					:			Dict										=       create_vector(UpperCAmelCase__      ,      UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       create_vector(UpperCAmelCase__      ,      UpperCAmelCase__ )
							return is_zero_vector(get_ad_vectors_cross(UpperCAmelCase__      ,      UpperCAmelCase__ )      ,      UpperCAmelCase__ )





