






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()
__UpperCAmelCase								=						logging.get_logger(__name__)




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[Any]            =						YolosConfig()

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

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

					return config




def      lowercase__      (     __snake_case    :							dict     ,       __snake_case    :							YolosConfig     ,       __snake_case    :							bool = 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_    :      Optional[Any]            =						state_dict.pop(F"blocks.{i}.attn.qkv.weight"  )
										UpperCAmelCase_    :      Tuple            =						state_dict.pop(F"blocks.{i}.attn.qkv.bias"  )
										# next, add query, keys and values (in that order) to the state dict
										UpperCAmelCase_    :      Tuple            =						in_proj_weight[: config.hidden_size, :]
										UpperCAmelCase_    :      Optional[int]            =						in_proj_bias[: config.hidden_size]
										UpperCAmelCase_    :      Tuple            =						in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										UpperCAmelCase_    :      Optional[Any]            =						in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										UpperCAmelCase_    :      List[str]            =						in_proj_weight[-config.hidden_size :, :]
										UpperCAmelCase_    :      str            =						in_proj_bias[-config.hidden_size :]




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

					return name




def      lowercase__      (     __snake_case    :							dict     ,       __snake_case    :							YolosForObjectDetection  ):


					'''simple docstring'''


					for key in orig_state_dict.copy().keys():
										UpperCAmelCase_    :      List[str]            =						orig_state_dict.pop(__snake_case  )

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

					return orig_state_dict




def      lowercase__      (     ):


					'''simple docstring'''


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




@torch.no_grad()
def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							str     ,       __snake_case    :							str     ,       __snake_case    :							bool = False  ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						get_yolos_config(__snake_case  )

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

					# load 🤗 model
					UpperCAmelCase_    :      Optional[Any]            =						YolosForObjectDetection(__snake_case  )
					model.eval()
					UpperCAmelCase_    :      List[str]            =						convert_state_dict(__snake_case     ,       __snake_case  )
					model.load_state_dict(__snake_case  )

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

					UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						None, None
					if yolos_name == "yolos_ti":
										UpperCAmelCase_    :      Optional[Any]            =						torch.tensor(
										    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]  )
										UpperCAmelCase_    :      Union[str, Any]            =						torch.tensor(
										    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]  )
					elif yolos_name == "yolos_s_200_pre":
										UpperCAmelCase_    :      Any            =						torch.tensor(
										    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]  )
										UpperCAmelCase_    :      Dict            =						torch.tensor(
										    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]  )
					elif yolos_name == "yolos_s_300_pre":
										UpperCAmelCase_    :      str            =						torch.tensor(
										    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]  )
										UpperCAmelCase_    :      Optional[Any]            =						torch.tensor(
										    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]  )
					elif yolos_name == "yolos_s_dWr":
										UpperCAmelCase_    :      int            =						torch.tensor(
										    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]  )
										UpperCAmelCase_    :      Union[str, Any]            =						torch.tensor(
										    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]  )
					elif yolos_name == "yolos_base":
										UpperCAmelCase_    :      Optional[int]            =						torch.tensor(
										    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]  )
										UpperCAmelCase_    :      str            =						torch.tensor(
										    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]  )
					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_    :      List[str]            =						{
										    '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_    :      str            =						model_mapping[yolos_name]
										image_processor.push_to_hub(__snake_case     ,       organization='hustvl'  )
										model.push_to_hub(__snake_case     ,       organization='hustvl'  )


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

		__UpperCAmelCase								=						parser.parse_args()
		convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)














def      lowercase__      (     __snake_case    :							Dict  ):


					'''simple docstring'''


					if not head:
										return True
					# split the list to two parts
					UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						head.next, head
					while fast and fast.next:
										UpperCAmelCase_    :      str            =						fast.next.next
										UpperCAmelCase_    :      Union[str, Any]            =						slow.next
					UpperCAmelCase_    :      int            =						slow.next
					UpperCAmelCase_    :      List[Any]            =						None  # Don't forget here! But forget still works!
					# reverse the second part
					UpperCAmelCase_    :      Tuple            =						None
					while second:
										UpperCAmelCase_    :      int            =						second.next
										UpperCAmelCase_    :      Any            =						node
										UpperCAmelCase_    :      Optional[Any]            =						second
										UpperCAmelCase_    :      Tuple            =						nxt
					# compare two parts
					# second part has the same or one less node
					while node:
										if node.val != head.val:
															return False
										UpperCAmelCase_    :      Optional[Any]            =						node.next
										UpperCAmelCase_    :      Dict            =						head.next
					return True




def      lowercase__      (     __snake_case    :							Union[str, Any]  ):


					'''simple docstring'''


					if not head or not head.next:
										return True

					# 1. Get the midpoint (slow)
					UpperCAmelCase_    :      Any            =						head
					while fast and fast.next:
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						fast.next.next, slow.next

					# 2. Push the second half into the stack
					UpperCAmelCase_    :      List[str]            =						[slow.val]
					while slow.next:
										UpperCAmelCase_    :      List[str]            =						slow.next
										stack.append(slow.val  )

					# 3. Comparison
					while stack:
										if stack.pop() != cur.val:
															return False
										UpperCAmelCase_    :      int            =						cur.next

					return True






def      lowercase__      (     __snake_case    :							Dict  ):


					'''simple docstring'''


					if not head or not head.next:
										return True
					UpperCAmelCase_    :      Tuple            =						{}
					UpperCAmelCase_    :      int            =						0
					while head:
										if head.val in d:
															d[head.val].append(__snake_case  )
										else:
															UpperCAmelCase_    :      List[Any]            =						[pos]
										UpperCAmelCase_    :      Any            =						head.next
										pos += 1
					UpperCAmelCase_    :      Dict            =						pos - 1
					UpperCAmelCase_    :      Optional[int]            =						0
					for v in d.values():
										if len(__snake_case  ) % 2 != 0:
															middle += 1
										else:
															UpperCAmelCase_    :      int            =						0
															for i in range(0     ,       len(__snake_case  )  ):
																				if v[i] + v[len(__snake_case  ) - 1 - step] != checksum:
																									return False
																				step += 1
										if middle > 1:
															return False
					return True






