





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

def      __A   (      a_    :str)		->		YolosConfig:
				__a			:    Tuple      							=	YolosConfig()

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

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

				return config

def      __A   (      a_    :dict ,							a_    :YolosConfig ,							a_    :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)
								__a			:    Union[str, Any]      							=	state_dict.pop(F"""blocks.{i}.attn.qkv.weight""")
								__a			:    Tuple      							=	state_dict.pop(F"""blocks.{i}.attn.qkv.bias""")
								# next, add query, keys and values (in that order) to the state dict
								__a			:    str      							=	in_proj_weight[: config.hidden_size, :]
								__a			:    int      							=	in_proj_bias[: config.hidden_size]
								__a			:    List[str]      							=	in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__a			:    Dict      							=	in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__a			:    str      							=	in_proj_weight[-config.hidden_size :, :]
								__a			:    List[str]      							=	in_proj_bias[-config.hidden_size :]

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

				return name

def      __A   (      a_    :dict ,							a_    :YolosForObjectDetection)		->		dict:
				for key in orig_state_dict.copy().keys():
								__a			:    Dict      							=	orig_state_dict.pop(a_)

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

				return orig_state_dict

def      __A   (      )		->		torch.Tensor:
				__a			:    Optional[Any]      							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__a			:    Optional[int]      							=	Image.open(requests.get(a_ ,							stream=a_).raw)
				return im

@torch.no_grad()
def      __A   (      a_    :str ,							a_    :str ,							a_    :str ,							a_    :bool = False)		->		Union[str, Any]:
				__a			:    List[Any]      							=	get_yolos_config(a_)

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

				# load 🤗 model
				__a			:    str      							=	YolosForObjectDetection(a_)
				model.eval()
				__a			:    Optional[Any]      							=	convert_state_dict(a_ ,							a_)
				model.load_state_dict(a_)

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

				__a							,    __a			:    Dict      							=	None, None
				if yolos_name == "yolos_ti":
								__a			:    Dict      							=	torch.tensor(
								    [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]])
								__a			:    str      							=	torch.tensor(
								    [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]])
				elif yolos_name == "yolos_s_200_pre":
								__a			:    Optional[int]      							=	torch.tensor(
								    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]])
								__a			:    str      							=	torch.tensor(
								    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]])
				elif yolos_name == "yolos_s_300_pre":
								__a			:    Tuple      							=	torch.tensor(
								    [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]])
								__a			:    Union[str, Any]      							=	torch.tensor(
								    [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]])
				elif yolos_name == "yolos_s_dWr":
								__a			:    str      							=	torch.tensor(
								    [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]])
								__a			:    Union[str, Any]      							=	torch.tensor(
								    [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]])
				elif yolos_name == "yolos_base":
								__a			:    Any      							=	torch.tensor(
								    [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]])
								__a			:    int      							=	torch.tensor(
								    [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]])
				else:
								raise ValueError(F"""Unknown yolos_name: {yolos_name}""")

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

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

				if push_to_hub:
								__a			:    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...''')
								__a			:    Dict      							=	model_mapping[yolos_name]
								image_processor.push_to_hub(a_ ,							organization='''hustvl''')
								model.push_to_hub(a_ ,							organization='''hustvl''')


if __name__ == "__main__":
		A			      =	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.'''
		)

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




'''simple docstring'''





def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> tuple[float, float]:

  # Check if the input is valid
  if not len(_lowerCAmelCase			) == len(_lowerCAmelCase			) == 3:
    raise ValueError('Please enter a valid equation.'			)
  if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
    raise ValueError('Both a & b of two equations can\'t be zero.'			)

  # Extract the coefficients
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Union[str, Any]       			=					equationa
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Any       			=					equationa

  # Calculate the determinants of the matrices
  __lowerCamelCase			:			Union[str, Any]       			=					aa * ba - aa * ba
  __lowerCamelCase			:			Dict       			=					ca * ba - ca * ba
  __lowerCamelCase			:			List[str]       			=					aa * ca - aa * ca

  # Check if the system of linear equations has a solution (using Cramer's rule)
  if determinant == 0:
    if determinant_x == determinant_y == 0:
      raise ValueError('Infinite solutions. (Consistent system)'			)
    else:
      raise ValueError('No solution. (Inconsistent system)'			)
  else:
    if determinant_x == determinant_y == 0:
      # Trivial solution (Inconsistent system)
      return (0.0, 0.0)
    else:
      __lowerCamelCase			:			Tuple       			=					determinant_x / determinant
      __lowerCamelCase			:			Union[str, Any]       			=					determinant_y / determinant
      # Non-Trivial Solution (Consistent system)
      return (x, y)






