




"""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__     						=						logging.get_logger(__name__)







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Tuple    =    YolosConfig()

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

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

				return config







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







def      _snake_case			(				lowercase__		):
				if "backbone" in name:
								_lowerCamelCase     :			Any    =    name.replace('backbone'							,    'vit'		)
				if "cls_token" in name:
								_lowerCamelCase     :			int    =    name.replace('cls_token'							,    'embeddings.cls_token'		)
				if "det_token" in name:
								_lowerCamelCase     :			int    =    name.replace('det_token'							,    'embeddings.detection_tokens'		)
				if "mid_pos_embed" in name:
								_lowerCamelCase     :			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     :			str    =    name.replace('patch_embed.proj'							,    'embeddings.patch_embeddings.projection'		)
				if "blocks" in name:
								_lowerCamelCase     :			Union[str, Any]    =    name.replace('blocks'							,    'encoder.layer'		)
				if "attn.proj" in name:
								_lowerCamelCase     :			str    =    name.replace('attn.proj'							,    'attention.output.dense'		)
				if "attn" in name:
								_lowerCamelCase     :			List[Any]    =    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     :			Union[str, Any]    =    name.replace('mlp.fc1'							,    'intermediate.dense'		)
				if "mlp.fc2" in name:
								_lowerCamelCase     :			Optional[Any]    =    name.replace('mlp.fc2'							,    'output.dense'		)
				if "class_embed" in name:
								_lowerCamelCase     :			List[str]    =    name.replace('class_embed'							,    'class_labels_classifier'		)
				if "bbox_embed" in name:
								_lowerCamelCase     :			Dict    =    name.replace('bbox_embed'							,    'bbox_predictor'		)
				if "vit.norm" in name:
								_lowerCamelCase     :			List[Any]    =    name.replace('vit.norm'							,    'vit.layernorm'		)

				return name







def      _snake_case			(				lowercase__							,    lowercase__		):
				for key in orig_state_dict.copy().keys():
								_lowerCamelCase     :			Dict    =    orig_state_dict.pop(lowercase__		)

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

				return orig_state_dict







def      _snake_case			(				):
				_lowerCamelCase     :			Union[str, Any]    =    'http://images.cocodataset.org/val2017/000000039769.jpg'
				_lowerCamelCase     :			Dict    =    Image.open(requests.get(lowercase__							,    stream=lowercase__		).raw		)
				return im







@torch.no_grad()
def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = False		):
				_lowerCamelCase     :			str    =    get_yolos_config(lowercase__		)

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

				# load 🤗 model
				_lowerCamelCase     :			List[Any]    =    YolosForObjectDetection(lowercase__		)
				model.eval()
				_lowerCamelCase     :			str    =    convert_state_dict(lowercase__							,    lowercase__		)
				model.load_state_dict(lowercase__		)

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

				_lowerCamelCase, _lowerCamelCase     :			str    =    None, None
				if yolos_name == "yolos_ti":
								_lowerCamelCase     :			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]]		)
								_lowerCamelCase     :			int    =    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":
								_lowerCamelCase     :			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]]		)
								_lowerCamelCase     :			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":
								_lowerCamelCase     :			Optional[int]    =    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]]		)
								_lowerCamelCase     :			List[str]    =    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":
								_lowerCamelCase     :			Dict    =    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]]		)
								_lowerCamelCase     :			List[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":
								_lowerCamelCase     :			Optional[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]]		)
								_lowerCamelCase     :			Union[str, 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]							,    lowercase__							,    atol=1E-4		)
				assert torch.allclose(pred_boxes[0, :3, :3]							,    lowercase__							,    atol=1E-4		)

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

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


if __name__ == "__main__":
			lowercase__     						=						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__     						=						parser.parse_args()
			convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""






def      _snake_case			(				lowercase__		):
				stooge(lowercase__							,    0							,    len(lowercase__		) - 1		)
				return arr







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				if i >= h:
								return

				# If first element is smaller than the last then swap them
				if arr[i] > arr[h]:
								_lowerCamelCase, _lowerCamelCase     :			Optional[Any]    =    arr[h], arr[i]

				# If there are more than 2 elements in the array
				if h - i + 1 > 2:
								_lowerCamelCase     :			Union[str, Any]    =    (int)((h - i + 1) / 3		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)

								# Recursively sort last 2/3 elements
								stooge(lowercase__							,    i + t							,    (lowercase__)		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)


if __name__ == "__main__":
			lowercase__     						=						input("""Enter numbers separated by a comma:\n""").strip()
			lowercase__     						=						[int(item) for item in user_input.split(""",""")]
			print(stooge_sort(unsorted))