


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



def 			lowerCAmelCase (		lowerCAmelCase_ )-> YolosConfig:
				lowerCAmelCase_ :      int    				=				YolosConfig()

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

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

				return config



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ = False )-> int:


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



def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:


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

				return name



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> dict:


				for key in orig_state_dict.copy().keys():
								lowerCAmelCase_ :      str    				=				orig_state_dict.pop(__lowerCAmelCase )

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

				return orig_state_dict



def 			lowerCAmelCase (		)-> torch.Tensor:
				lowerCAmelCase_ :      str    				=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowerCAmelCase_ :      Tuple    				=				Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase ).raw )
				return im



@torch.no_grad()
def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ = False )-> Optional[Any]:
				lowerCAmelCase_ :      Union[str, Any]    				=				get_yolos_config(__lowerCAmelCase )

				# load original state_dict
				lowerCAmelCase_ :      Union[str, Any]    				=				torch.load(__lowerCAmelCase ,   map_location='''cpu''' )['''model''']

				# load 🤗 model
				lowerCAmelCase_ :      Optional[int]    				=				YolosForObjectDetection(__lowerCAmelCase )
				model.eval()
				lowerCAmelCase_ :      List[Any]    				=				convert_state_dict(__lowerCAmelCase ,   __lowerCAmelCase )
				model.load_state_dict(__lowerCAmelCase )

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

				lowerCAmelCase_				,  lowerCAmelCase_ :      Optional[int]    				=				None, None
				if yolos_name == "yolos_ti":
								lowerCAmelCase_ :      Optional[Any]    				=				torch.tensor(
								    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]] )
								lowerCAmelCase_ :      Optional[Any]    				=				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":
								lowerCAmelCase_ :      Dict    				=				torch.tensor(
								    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] )
								lowerCAmelCase_ :      Union[str, 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":
								lowerCAmelCase_ :      Optional[int]    				=				torch.tensor(
								    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]] )
								lowerCAmelCase_ :      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":
								lowerCAmelCase_ :      Any    				=				torch.tensor(
								    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]] )
								lowerCAmelCase_ :      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":
								lowerCAmelCase_ :      Tuple    				=				torch.tensor(
								    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]] )
								lowerCAmelCase_ :      Optional[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] ,   __lowerCAmelCase ,   atol=1e-4 )
				assert torch.allclose(pred_boxes[0, :3, :3] ,   __lowerCAmelCase ,   atol=1e-4 )

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

				if push_to_hub:
								lowerCAmelCase_ :      str    				=				{
								    '''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...''' )
								lowerCAmelCase_ :      Union[str, Any]    				=				model_mapping[yolos_name]
								image_processor.push_to_hub(__lowerCAmelCase ,   organization='''hustvl''' )
								model.push_to_hub(__lowerCAmelCase ,   organization='''hustvl''' )


if __name__ == "__main__":
						_UpperCAmelCase			:  Union[str, 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."""
						)

						_UpperCAmelCase			:  Optional[int]     =parser.parse_args()
						convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)



from __future__ import annotations

from math import pi



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> dict[str, float]:

				if (inductance, frequency, reactance).count(0 ) != 1:
								raise ValueError('''One and only one argument must be 0''' )
				if inductance < 0:
								raise ValueError('''Inductance cannot be negative''' )
				if frequency < 0:
								raise ValueError('''Frequency cannot be negative''' )
				if reactance < 0:
								raise ValueError('''Inductive reactance cannot be negative''' )
				if inductance == 0:
								return {"inductance": reactance / (2 * pi * frequency)}
				elif frequency == 0:
								return {"frequency": reactance / (2 * pi * inductance)}
				elif reactance == 0:
								return {"reactance": 2 * pi * frequency * inductance}
				else:
								raise ValueError('''Exactly one argument must be 0''' )


if __name__ == "__main__":
						import doctest

						doctest.testmod()