


'''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()
__snake_case				 =	logging.get_logger(__name__)


def      a (     __a  )    ->      YolosConfig:



					'''simple docstring'''






					UpperCamelCase__  :Optional[int]				   =					YolosConfig()

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

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

					return config


def      a (     __a					,   __a					,   __a = False  )    ->      str:



					'''simple docstring'''






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


def      a (     __a  )    ->      str:



					'''simple docstring'''






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

					return name


def      a (     __a					,   __a  )    ->      dict:



					'''simple docstring'''






					for key in orig_state_dict.copy().keys():
										UpperCamelCase__  :List[Any]				   =					orig_state_dict.pop(snake_case_  )

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

					return orig_state_dict


def      a (     )    ->      torch.Tensor:



					'''simple docstring'''






					UpperCamelCase__  :Optional[Any]				   =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					UpperCamelCase__  :Dict				   =					Image.open(requests.get(snake_case_					,   stream=snake_case_  ).raw  )
					return im


@torch.no_grad()
def      a (     __a					,   __a					,   __a					,   __a = False  )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					get_yolos_config(snake_case_  )

					# load original state_dict
					UpperCamelCase__  :Tuple				   =					torch.load(snake_case_					,   map_location='''cpu'''  )['''model''']

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

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

					UpperCamelCase__  :List[Any]				   =					None, None
					if yolos_name == "yolos_ti":
										UpperCamelCase__  :List[Any]				   =					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]]  )
										UpperCamelCase__  :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":
										UpperCamelCase__  :Optional[Any]				   =					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]]  )
										UpperCamelCase__  :List[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":
										UpperCamelCase__  :Optional[Any]				   =					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]]  )
										UpperCamelCase__  :Optional[int]				   =					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":
										UpperCamelCase__  :Union[str, Any]				   =					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]]  )
										UpperCamelCase__  :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":
										UpperCamelCase__  :int				   =					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]]  )
										UpperCamelCase__  :List[str]				   =					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]					,   snake_case_					,   atol=1e-4  )
					assert torch.allclose(pred_boxes[0, :3, :3]					,   snake_case_					,   atol=1e-4  )

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

					if push_to_hub:
										UpperCamelCase__  :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...'''  )
										UpperCamelCase__  :List[Any]				   =					model_mapping[yolos_name]
										image_processor.push_to_hub(snake_case_					,   organization='''hustvl'''  )
										model.push_to_hub(snake_case_					,   organization='''hustvl'''  )


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

		__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 requests

a_					=     """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""







def 						__lowercase	(  snake_case_    :    str		)  ->None:



    '''simple docstring'''







    __A       :		str							=	requests.get(_NEWS_API + bbc_news_api_key		).json()
    # each article in the list is a dict
    for i, article in enumerate(bbc_news_page['''articles''']							,1		):
        print(F"""{i}.) {article['title']}"""		)


if __name__ == "__main__":
       fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")




