

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			:				List[Any]   =     logging.get_logger(__name__)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					YolosConfig()

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

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

							return config






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






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

							return name






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							for key in orig_state_dict.copy().keys():
														A      :					str      		=					orig_state_dict.pop(__UpperCamelCase		)

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

							return orig_state_dict






def        snake_case__		(    ):
							A      :					Optional[Any]      		=					"http://images.cocodataset.org/val2017/000000039769.jpg"
							A      :					Any      		=					Image.open(requests.get(__UpperCamelCase     ,				stream=__UpperCamelCase		).raw		)
							return im






@torch.no_grad()
def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_ = False		):
							A      :					List[str]      		=					get_yolos_config(__UpperCamelCase		)

							# load original state_dict
							A      :					int      		=					torch.load(__UpperCamelCase     ,				map_location='''cpu'''		)["model"]

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

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

							A      :					Union[str, Any]      		=					None, None
							if yolos_name == "yolos_ti":
														A      :					Any      		=					torch.tensor(
														    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9_769, -17.7691], [-42.3281, -20.7200, -30.6294]]		)
														A      :					str      		=					torch.tensor(
														    [[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]]		)
							elif yolos_name == "yolos_s_200_pre":
														A      :					Dict      		=					torch.tensor(
														    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]		)
														A      :					List[str]      		=					torch.tensor(
														    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]		)
							elif yolos_name == "yolos_s_300_pre":
														A      :					Union[str, Any]      		=					torch.tensor(
														    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]		)
														A      :					Dict      		=					torch.tensor(
														    [[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]]		)
							elif yolos_name == "yolos_s_dWr":
														A      :					Union[str, Any]      		=					torch.tensor(
														    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]		)
														A      :					str      		=					torch.tensor(
														    [[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]]		)
							elif yolos_name == "yolos_base":
														A      :					Any      		=					torch.tensor(
														    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]		)
														A      :					Optional[Any]      		=					torch.tensor(
														    [[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]]		)
							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:
														A      :					List[Any]      		=					{
														    "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...'''		)
														A      :					str      		=					model_mapping[yolos_name]
														image_processor.push_to_hub(__UpperCamelCase     ,				organization='''hustvl'''		)
														model.push_to_hub(__UpperCamelCase     ,				organization='''hustvl'''		)


if __name__ == "__main__":
		lowercase			:				Optional[Any]   =     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			:				Any   =     parser.parse_args()
		convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











from __future__ import annotations

import queue


class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= data
											a__	:							Tuple      						= None
											a__	:							Optional[Any]      						= None






def     SCREAMING_SNAKE_CASE(      )	->						TreeNode:
				print("\n********Press N to stop entering at any point of time********\n" )
				a__	:							Tuple      						= input("Enter the value of the root node: " ).strip().lower()
				a__	:							queue.Queue      						= queue.Queue()
				a__	:							str      						= TreeNode(int(__UpperCamelCase ) )
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							str      						= q.get()
								a__	:							Optional[Any]      						= F'Enter the left node of {node_found.data}: '
								a__	:							List[Any]      						= input(__UpperCamelCase ).strip().lower() or "n"
								if check == "n":
												return tree_node
								a__	:							Optional[int]      						= TreeNode(int(__UpperCamelCase ) )
								a__	:							List[str]      						= left_node
								q.put(__UpperCamelCase )
								a__	:							Dict      						= F'Enter the right node of {node_found.data}: '
								a__	:							List[str]      						= input(__UpperCamelCase ).strip().lower() or "n"
								if check == "n":
												return tree_node
								a__	:							str      						= TreeNode(int(__UpperCamelCase ) )
								a__	:							Any      						= right_node
								q.put(__UpperCamelCase )
				raise






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				print(node.data	,				end="," )
				pre_order(node.left )
				pre_order(node.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				in_order(node.left )
				print(node.data	,				end="," )
				in_order(node.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				post_order(node.left )
				post_order(node.right )
				print(node.data	,				end="," )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							queue.Queue      						= queue.Queue()
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							Tuple      						= q.get()
								print(node_dequeued.data	,				end="," )
								if node_dequeued.left:
												q.put(node_dequeued.left )
								if node_dequeued.right:
												q.put(node_dequeued.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							queue.Queue      						= queue.Queue()
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							int      						= []
								while not q.empty():
												a__	:							Union[str, Any]      						= q.get()
												print(node_dequeued.data	,				end="," )
												if node_dequeued.left:
																list_.append(node_dequeued.left )
												if node_dequeued.right:
																list_.append(node_dequeued.right )
								print()
								for node in list_:
												q.put(__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							list[TreeNode]      						= []
				a__	:							Optional[int]      						= node
				while n or stack:
								while n:  # start from root node, find its left child
												print(n.data	,				end="," )
												stack.append(__UpperCamelCase )
												a__	:							int      						= n.left
								# end of while means current node doesn't have left child
								a__	:							Dict      						= stack.pop()
								# start to traverse its right child
								a__	:							str      						= n.right






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							list[TreeNode]      						= []
				a__	:							Dict      						= node
				while n or stack:
								while n:
												stack.append(__UpperCamelCase )
												a__	:							List[Any]      						= n.left
								a__	:							str      						= stack.pop()
								print(n.data	,				end="," )
								a__	:							Any      						= n.right






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__						,   a__	:							List[str]      						= [], []
				a__	:							List[Any]      						= node
				stacka.append(__UpperCamelCase )
				while stacka:  # to find the reversed order of post order, store it in stack2
								a__	:							Optional[Any]      						= stacka.pop()
								if n.left:
												stacka.append(n.left )
								if n.right:
												stacka.append(n.right )
								stacka.append(__UpperCamelCase )
				while stacka:  # pop up from stack2 will be the post order
								print(stacka.pop().data	,				end="," )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase = ""	,				__UpperCamelCase=50	,				__UpperCamelCase="*" )	->						str:
				if not s:
								return "\n" + width * char
				a__						,   a__	:							int      						= divmod(width - len(__UpperCamelCase ) - 2	,				2 )
				return F'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(prompt("""Binary Tree Traversals"""))

						lowerCamelCase		       =      build_tree()
						print(prompt("""Pre Order Traversal"""))
						pre_order(node)
						print(prompt() + """\n""")

						print(prompt("""In Order Traversal"""))
						in_order(node)
						print(prompt() + """\n""")

						print(prompt("""Post Order Traversal"""))
						post_order(node)
						print(prompt() + """\n""")

						print(prompt("""Level Order Traversal"""))
						level_order(node)
						print(prompt() + """\n""")

						print(prompt("""Actual Level Order Traversal"""))
						level_order_actual(node)
						print("""*""" * 50 + """\n""")

						print(prompt("""Pre Order Traversal - Iteration Version"""))
						pre_order_iter(node)
						print(prompt() + """\n""")

						print(prompt("""In Order Traversal - Iteration Version"""))
						in_order_iter(node)
						print(prompt() + """\n""")

						print(prompt("""Post Order Traversal - Iteration Version"""))
						post_order_iter(node)
						print(prompt())


