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


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							YolosConfig()

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

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

	return config


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase = False			):






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


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





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

	return name


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	for key in orig_state_dict.copy().keys():
		_lowercase :    str							=							orig_state_dict.pop(__UpperCAmelCase			)

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

	return orig_state_dict


def      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    Any							=							'http://images.cocodataset.org/val2017/000000039769.jpg'
	_lowercase :    Any							=							Image.open(requests.get(__UpperCAmelCase     ,stream=__UpperCAmelCase			).raw			)
	return im


@torch.no_grad()
def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase = False			):






	"""simple docstring"""





	_lowercase :    Optional[Any]							=							get_yolos_config(__UpperCAmelCase			)

	# load original state_dict
	_lowercase :    List[Any]							=							torch.load(__UpperCAmelCase     ,map_location='cpu'			)['model']

	# load 🤗 model
	_lowercase :    str							=							YolosForObjectDetection(__UpperCAmelCase			)
	model.eval()
	_lowercase :    Any							=							convert_state_dict(__UpperCAmelCase     ,__UpperCAmelCase			)
	model.load_state_dict(__UpperCAmelCase			)

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

	_lowercase :    Any							=							None, None
	if yolos_name == "yolos_ti":
		_lowercase :    Optional[int]							=							torch.tensor(
		    [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_7_6_9, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]]			)
		_lowercase :    Dict							=							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":
		_lowercase :    Optional[Any]							=							torch.tensor(
		    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]			)
		_lowercase :    Tuple							=							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":
		_lowercase :    Optional[Any]							=							torch.tensor(
		    [[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]]			)
		_lowercase :    Optional[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":
		_lowercase :    int							=							torch.tensor(
		    [[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]]			)
		_lowercase :    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":
		_lowercase :    Dict							=							torch.tensor(
		    [[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]]			)
		_lowercase :    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]     ,__UpperCAmelCase     ,atol=1E-4			)
	assert torch.allclose(pred_boxes[0, :3, :3]     ,__UpperCAmelCase     ,atol=1E-4			)

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

	if push_to_hub:
		_lowercase :    Tuple							=							{
		    '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...'			)
		_lowercase :    str							=							model_mapping[yolos_name]
		image_processor.push_to_hub(__UpperCAmelCase     ,organization='hustvl'			)
		model.push_to_hub(__UpperCAmelCase     ,organization='hustvl'			)


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

						SCREAMING_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"""







def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	stooge(__UpperCAmelCase     ,0     ,len(__UpperCAmelCase			) - 1			)
	return arr


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		_lowercase				,      _lowercase :    List[str]							=							arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		_lowercase :    Optional[int]							=							(int)((h - i + 1) / 3			)

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

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

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


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



