






'''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()
lowerCamelCase_				: Union[str, Any]    	=							logging.get_logger(__name__)







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = YolosConfig()

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

						UpperCamelCase__         = 91
						UpperCamelCase__         = 'huggingface/label-files'
						UpperCamelCase__         = 'coco-detection-id2label.json'
						UpperCamelCase__         = json.load(open(hf_hub_download(_A						,      _A						,      repo_type="""dataset"""					)						,      """r"""					)					)
						UpperCamelCase__         = {int(_A					): v for k, v in idalabel.items()}
						UpperCamelCase__         = idalabel
						UpperCamelCase__         = {v: k for k, v in idalabel.items()}

						return config







def 	__magic_name__( _A						,      _A						,      _A = 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)
												UpperCamelCase__         = state_dict.pop(f"blocks.{i}.attn.qkv.weight"					)
												UpperCamelCase__         = state_dict.pop(f"blocks.{i}.attn.qkv.bias"					)
												# next, add query, keys and values (in that order) to the state dict
												UpperCamelCase__         = in_proj_weight[: config.hidden_size, :]
												UpperCamelCase__         = in_proj_bias[: config.hidden_size]
												UpperCamelCase__         = in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												UpperCamelCase__         = in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												UpperCamelCase__         = in_proj_weight[-config.hidden_size :, :]
												UpperCamelCase__         = in_proj_bias[-config.hidden_size :]







def 	__magic_name__( _A					):
						'''simple docstring'''





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

						return name







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						for key in orig_state_dict.copy().keys():
												UpperCamelCase__         = orig_state_dict.pop(_A					)

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

						return orig_state_dict







def 	__magic_name__( ):
						'''simple docstring'''





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







@torch.no_grad()
def 	__magic_name__( _A						,      _A						,      _A						,      _A = False					):
						'''simple docstring'''





						UpperCamelCase__         = get_yolos_config(_A					)

						# load original state_dict
						UpperCamelCase__         = torch.load(_A						,      map_location="""cpu"""					)['model']

						# load 🤗 model
						UpperCamelCase__         = YolosForObjectDetection(_A					)
						model.eval()
						UpperCamelCase__         = convert_state_dict(_A						,      _A					)
						model.load_state_dict(_A					)

						# Check outputs on an image, prepared by YolosImageProcessor
						UpperCamelCase__         = 800 if yolos_name != 'yolos_ti' else 512
						UpperCamelCase__         = YolosImageProcessor(format="""coco_detection"""						,      size=_A					)
						UpperCamelCase__         = image_processor(images=prepare_img()						,      return_tensors="""pt"""					)
						UpperCamelCase__         = model(**_A					)
						UpperCamelCase__         = outputs.logits, outputs.pred_boxes

						UpperCamelCase__         = None, None
						if yolos_name == "yolos_ti":
												UpperCamelCase__         = 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__         = 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__         = 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__         = 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__         = 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__         = 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__         = 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__         = 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__         = 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__         = 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:
												UpperCamelCase__         = {
												    '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__         = model_mapping[yolos_name]
												image_processor.push_to_hub(_A						,      organization="""hustvl"""					)
												model.push_to_hub(_A						,      organization="""hustvl"""					)


if __name__ == "__main__":
					lowerCamelCase_				: List[str]    	=							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.'''
					)

					lowerCamelCase_				: Dict    	=							parser.parse_args()
					convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''


from collections import defaultdict







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = 1
						UpperCamelCase__         = True
						for v in tree[start]:
												if v not in visited:
																		ret += dfs(_A					)
						if ret % 2 == 0:
												cuts.append(_A					)
						return ret







def 	__magic_name__( ):
						'''simple docstring'''





						dfs(1					)


if __name__ == "__main__":
					lowerCamelCase_	,     lowerCamelCase_				: int    	=							10, 9
					lowerCamelCase_				: int    	=							defaultdict(list)
					lowerCamelCase_				: dict[int, bool]    	=							{}
					lowerCamelCase_				: list[int]    	=							[]
					lowerCamelCase_				: Union[str, Any]    	=							0
					lowerCamelCase_				: Tuple    	=							[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
					for u, v in edges:
										tree[u].append(v)
										tree[v].append(u)
					even_tree()
					print(len(cuts) - 1)
