


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()
_snake_case	:						Any         =							logging.get_logger(__name__)







def 							lowerCAmelCase_	(   __lowerCamelCase      ):
							__snake_case						:     List[str]									=  YolosConfig()

							# size of the architecture
							if "yolos_ti" in yolos_name:
														__snake_case						:     int									=  1_9_2
														__snake_case						:     List[Any]									=  7_6_8
														__snake_case						:     Union[str, Any]									=  1_2
														__snake_case						:     Optional[Any]									=  3
														__snake_case						:     Any									=  [8_0_0, 1_3_3_3]
														__snake_case						:     List[str]									=  False
							elif yolos_name == "yolos_s_dWr":
														__snake_case						:     List[str]									=  3_3_0
														__snake_case						:     Dict									=  1_4
														__snake_case						:     List[str]									=  6
														__snake_case						:     Union[str, Any]									=  1_3_2_0
							elif "yolos_s" in yolos_name:
														__snake_case						:     Union[str, Any]									=  3_8_4
														__snake_case						:     Dict									=  1_5_3_6
														__snake_case						:     int									=  1_2
														__snake_case						:     Optional[Any]									=  6
							elif "yolos_b" in yolos_name:
														__snake_case						:     Union[str, Any]									=  [8_0_0, 1_3_4_4]

							__snake_case						:     Optional[int]									=  9_1
							__snake_case						:     Any									=  "huggingface/label-files"
							__snake_case						:     Optional[int]									=  "coco-detection-id2label.json"
							__snake_case						:     Optional[Any]									=  json.load(open(hf_hub_download(__lowerCamelCase				,      __lowerCamelCase				,      repo_type="dataset"      )				,      "r"      )      )
							__snake_case						:     Dict									=  {int(__lowerCamelCase      ): v for k, v in idalabel.items()}
							__snake_case						:     str									=  idalabel
							__snake_case						:     int									=  {v: k for k, v in idalabel.items()}

							return config







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







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

							return name







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase      ):
							for key in orig_state_dict.copy().keys():
														__snake_case						:     str									=  orig_state_dict.pop(__lowerCamelCase      )

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

							return orig_state_dict







def 							lowerCAmelCase_	(   ):
							__snake_case						:     Optional[int]									=  "http://images.cocodataset.org/val2017/000000039769.jpg"
							__snake_case						:     List[str]									=  Image.open(requests.get(__lowerCamelCase				,      stream=__lowerCamelCase      ).raw      )
							return im







@torch.no_grad()
def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase = False      ):
							__snake_case						:     str									=  get_yolos_config(__lowerCamelCase      )

							# load original state_dict
							__snake_case						:     int									=  torch.load(__lowerCamelCase				,      map_location="cpu"      )["model"]

							# load 🤗 model
							__snake_case						:     str									=  YolosForObjectDetection(__lowerCamelCase      )
							model.eval()
							__snake_case						:     Dict									=  convert_state_dict(__lowerCamelCase				,      __lowerCamelCase      )
							model.load_state_dict(__lowerCamelCase      )

							# Check outputs on an image, prepared by YolosImageProcessor
							__snake_case						:     Dict									=  8_0_0 if yolos_name != "yolos_ti" else 5_1_2
							__snake_case						:     str									=  YolosImageProcessor(format="coco_detection"				,      size=__lowerCamelCase      )
							__snake_case						:     List[Any]									=  image_processor(images=prepare_img()				,      return_tensors="pt"      )
							__snake_case						:     Optional[Any]									=  model(**__lowerCamelCase      )
							__snake_case					,     __snake_case						:     Tuple									=  outputs.logits, outputs.pred_boxes

							__snake_case					,     __snake_case						:     Tuple									=  None, None
							if yolos_name == "yolos_ti":
														__snake_case						:     List[str]									=  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]]      )
														__snake_case						:     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":
														__snake_case						:     List[str]									=  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]]      )
														__snake_case						:     Union[str, Any]									=  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":
														__snake_case						:     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]]      )
														__snake_case						:     Dict									=  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":
														__snake_case						:     Optional[int]									=  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]]      )
														__snake_case						:     Dict									=  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":
														__snake_case						:     List[str]									=  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]]      )
														__snake_case						:     Optional[Any]									=  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]				,      __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:
														__snake_case						:     Dict									=  {
														    "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..."      )
														__snake_case						:     Optional[Any]									=  model_mapping[yolos_name]
														image_processor.push_to_hub(__lowerCamelCase				,      organization="hustvl"      )
														model.push_to_hub(__lowerCamelCase				,      organization="hustvl"      )


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

			_snake_case	:						str         =							parser.parse_args()
			convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






import requests
from bsa import BeautifulSoup

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str = "AAPL"				)  ->     str:
   _A      =       F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
   _A      =       BeautifulSoup(requests.get(_snake_case				).text	,   '''html.parser'''				)
   _A      =       '''My(6px) Pos(r) smartphone_Mt(6px)'''
   return soup.find('''div'''	,   class_=class_				).find('''span'''				).text


if __name__ == "__main__":
   for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
      print(f'Current {symbol:<4} stock price is {stock_price(symbol):>8}')



