






from __future__ import annotations

import queue




class 				__A     :





				"""simple docstring"""







				def __init__( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=data
									__UpperCamelCase      :					List[str]       		=None
									__UpperCamelCase      :					Union[str, Any]       		=None






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






def 							A     (       a_					)     ->						None:
					if not isinstance(a_							,a_					) or not node:
										return
					print(node.data							,end=','					)
					pre_order(node.left					)
					pre_order(node.right					)






def 							A     (       a_					)     ->						None:
					if not isinstance(a_							,a_					) or not node:
										return
					in_order(node.left					)
					print(node.data							,end=','					)
					in_order(node.right					)






def 							A     (       a_					)     ->						None:
					if not isinstance(a_							,a_					) or not node:
										return
					post_order(node.left					)
					post_order(node.right					)
					print(node.data							,end=','					)






def 							A     (       a_					)     ->						None:
					if not isinstance(a_							,a_					) or not node:
										return
					__UpperCamelCase      :					queue.Queue       		=queue.Queue()
					q.put(a_					)
					while not q.empty():
										__UpperCamelCase      :					str       		=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 							A     (       a_					)     ->						None:
					if not isinstance(a_							,a_					) or not node:
										return
					__UpperCamelCase      :					queue.Queue       		=queue.Queue()
					q.put(a_					)
					while not q.empty():
										__UpperCamelCase      :					Dict       		=[]
										while not q.empty():
															__UpperCamelCase      :					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(a_					)






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






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






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






def 							A     (       a_ = ""							,a_=50							,a_="*"					)     ->						str:
					if not s:
										return "\n" + width * char
					__UpperCamelCase		,				__UpperCamelCase      :					Optional[Any]       		=divmod(width - len(a_					) - 2							,2					)
					return F'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
							import doctest

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

							A_       :TreeNode       =						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())





"""simple docstring"""





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__						:   int			 = logging.get_logger(__name__)


def    _snake_case  (  _snake_case :	Union[str, Any]	):
      lowerCAmelCase    :		Dict				=      OrderedDict()
      for key, value in state_dict.items():
            if key.startswith('''module.encoder'''	):
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''module.encoder'''       ,      '''glpn.encoder'''	)
            if key.startswith('''module.decoder'''	):
                  lowerCAmelCase    :		str				=      key.replace('''module.decoder'''       ,      '''decoder.stages'''	)
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  lowerCAmelCase    :		Union[str, Any]				=      key[key.find('''patch_embed'''	) + len('''patch_embed'''	)]
                  lowerCAmelCase    :		str				=      key.replace(f'''patch_embed{idx}'''       ,      f'''patch_embeddings.{int(_snake_case	)-1}'''	)
            if "norm" in key:
                  lowerCAmelCase    :		str				=      key.replace('''norm'''       ,      '''layer_norm'''	)
            if "glpn.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  lowerCAmelCase    :		Optional[int]				=      key[key.find('''glpn.encoder.layer_norm'''	) + len('''glpn.encoder.layer_norm'''	)]
                  lowerCAmelCase    :		List[str]				=      key.replace(f'''layer_norm{idx}'''       ,      f'''layer_norm.{int(_snake_case	)-1}'''	)
            if "layer_norm1" in key:
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''layer_norm1'''       ,      '''layer_norm_1'''	)
            if "layer_norm2" in key:
                  lowerCAmelCase    :		Any				=      key.replace('''layer_norm2'''       ,      '''layer_norm_2'''	)
            if "block" in key:
                  # replace for example block1 by block.0
                  lowerCAmelCase    :		Tuple				=      key[key.find('''block'''	) + len('''block'''	)]
                  lowerCAmelCase    :		Tuple				=      key.replace(f'''block{idx}'''       ,      f'''block.{int(_snake_case	)-1}'''	)
            if "attn.q" in key:
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''attn.q'''       ,      '''attention.self.query'''	)
            if "attn.proj" in key:
                  lowerCAmelCase    :		Dict				=      key.replace('''attn.proj'''       ,      '''attention.output.dense'''	)
            if "attn" in key:
                  lowerCAmelCase    :		List[str]				=      key.replace('''attn'''       ,      '''attention.self'''	)
            if "fc1" in key:
                  lowerCAmelCase    :		List[Any]				=      key.replace('''fc1'''       ,      '''dense1'''	)
            if "fc2" in key:
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''fc2'''       ,      '''dense2'''	)
            if "linear_pred" in key:
                  lowerCAmelCase    :		List[Any]				=      key.replace('''linear_pred'''       ,      '''classifier'''	)
            if "linear_fuse" in key:
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''linear_fuse.conv'''       ,      '''linear_fuse'''	)
                  lowerCAmelCase    :		int				=      key.replace('''linear_fuse.bn'''       ,      '''batch_norm'''	)
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  lowerCAmelCase    :		Optional[Any]				=      key[key.find('''linear_c'''	) + len('''linear_c'''	)]
                  lowerCAmelCase    :		int				=      key.replace(f'''linear_c{idx}'''       ,      f'''linear_c.{int(_snake_case	)-1}'''	)
            if "bot_conv" in key:
                  lowerCAmelCase    :		str				=      key.replace('''bot_conv'''       ,      '''0.convolution'''	)
            if "skip_conv1" in key:
                  lowerCAmelCase    :		int				=      key.replace('''skip_conv1'''       ,      '''1.convolution'''	)
            if "skip_conv2" in key:
                  lowerCAmelCase    :		str				=      key.replace('''skip_conv2'''       ,      '''2.convolution'''	)
            if "fusion1" in key:
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''fusion1'''       ,      '''1.fusion'''	)
            if "fusion2" in key:
                  lowerCAmelCase    :		Any				=      key.replace('''fusion2'''       ,      '''2.fusion'''	)
            if "fusion3" in key:
                  lowerCAmelCase    :		List[Any]				=      key.replace('''fusion3'''       ,      '''3.fusion'''	)
            if "fusion" in key and "conv" in key:
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''conv'''       ,      '''convolutional_layer'''	)
            if key.startswith('''module.last_layer_depth'''	):
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''module.last_layer_depth'''       ,      '''head.head'''	)
            lowerCAmelCase    :		Union[str, Any]				=      value

      return new_state_dict


def    _snake_case  (  _snake_case :	Optional[Any]       ,      _snake_case :	str	):
      # for each of the encoder blocks:
      for i in range(config.num_encoder_blocks	):
            for j in range(config.depths[i]	):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  lowerCAmelCase    :		int				=      state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''	)
                  lowerCAmelCase    :		Optional[int]				=      state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''	)
                  # next, add keys and values (in that order) to the state dict
                  lowerCAmelCase    :		str				=      kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCAmelCase    :		Union[str, Any]				=      kv_bias[: config.hidden_sizes[i]]
                  lowerCAmelCase    :		Dict				=      kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCAmelCase    :		List[str]				=      kv_bias[config.hidden_sizes[i] :]


def    _snake_case  (  ):
      lowerCAmelCase    :		int				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase    :		str				=      Image.open(requests.get(_snake_case       ,      stream=_snake_case	).raw	)

      return image


@torch.no_grad()
def    _snake_case  (  _snake_case :	Dict       ,      _snake_case :	Dict       ,      _snake_case :	Union[str, Any]=False       ,      _snake_case :	List[str]=None	):
      lowerCAmelCase    :		Optional[int]				=      GLPNConfig(hidden_sizes=[64, 128, 320, 512]       ,      decoder_hidden_size=64       ,      depths=[3, 8, 27, 3]	)

      # load image processor (only resize + rescale)
      lowerCAmelCase    :		Union[str, Any]				=      GLPNImageProcessor()

      # prepare image
      lowerCAmelCase    :		Tuple				=      prepare_img()
      lowerCAmelCase    :		Dict				=      image_processor(images=_snake_case       ,      return_tensors='''pt'''	).pixel_values

      logger.info('''Converting model...'''	)

      # load original state dict
      lowerCAmelCase    :		List[str]				=      torch.load(_snake_case       ,      map_location=torch.device('''cpu'''	)	)

      # rename keys
      lowerCAmelCase    :		Tuple				=      rename_keys(_snake_case	)

      # key and value matrices need special treatment
      read_in_k_v(_snake_case       ,      _snake_case	)

      # create HuggingFace model and load state dict
      lowerCAmelCase    :		str				=      GLPNForDepthEstimation(_snake_case	)
      model.load_state_dict(_snake_case	)
      model.eval()

      # forward pass
      lowerCAmelCase    :		Union[str, Any]				=      model(_snake_case	)
      lowerCAmelCase    :		int				=      outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCAmelCase    :		str				=      torch.tensor(
                      [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]	)
            elif "kitti" in model_name:
                  lowerCAmelCase    :		str				=      torch.tensor(
                      [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]	)
            else:
                  raise ValueError(f'''Unknown model name: {model_name}'''	)

            lowerCAmelCase    :		List[Any]				=      torch.Size([1, 480, 640]	)

            assert predicted_depth.shape == expected_shape
            assert torch.allclose(predicted_depth[0, :3, :3]       ,      _snake_case       ,      atol=1E-4	)
            print('''Looks ok!'''	)

      # finally, push to hub if required
      if push_to_hub:
            logger.info('''Pushing model and image processor to the hub...'''	)
            model.push_to_hub(
                repo_path_or_name=Path(_snake_case       ,      _snake_case	)       ,      organization='''nielsr'''       ,      commit_message='''Add model'''       ,      use_temp_dir=_snake_case       ,      )
            image_processor.push_to_hub(
                repo_path_or_name=Path(_snake_case       ,      _snake_case	)       ,      organization='''nielsr'''       ,      commit_message='''Add image processor'''       ,      use_temp_dir=_snake_case       ,      )


if __name__ == "__main__":
 snake_case__						:   Tuple			 = argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''',
     default=None,
     type=str,
     help='''Path to the original PyTorch checkpoint (.pth file).''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
 )
 parser.add_argument(
     '''--model_name''',
     default='''glpn-kitti''',
     type=str,
     help='''Name of the model in case you\'re pushing to the hub.''',
 )
 snake_case__						:   List[str]			 = parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


