



'''simple docstring'''







from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass




@dataclass
class 				__lowerCAmelCase:
			__snake_case  :		int
			__snake_case  :		TreeNode | None		=      None
			__snake_case  :		TreeNode | None		=      None







def  SCREAMING_SNAKE_CASE__ (				):
		return TreeNode(3 ,		TreeNode(9     ) ,		TreeNode(20 ,		TreeNode(15     ) ,		TreeNode(7     )     )     )







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):

		def depth_first_search(
		    SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ) -> None:
				if not root:
						return

				if depth == len(SCREAMING_SNAKE_CASE     ):
						right_view.append(root.val     )

				depth_first_search(root.right ,		depth + 1 ,		SCREAMING_SNAKE_CASE     )
				depth_first_search(root.left ,		depth + 1 ,		SCREAMING_SNAKE_CASE     )

		SCREAMING_SNAKE_CASE_						:list										=      []
		if not root:
				return right_view

		depth_first_search(SCREAMING_SNAKE_CASE ,		0 ,		SCREAMING_SNAKE_CASE     )
		return right_view







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):

		def depth_first_search(
		    SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ) -> None:
				if not root:
						return

				if depth == len(SCREAMING_SNAKE_CASE     ):
						left_view.append(root.val     )

				depth_first_search(root.left ,		depth + 1 ,		SCREAMING_SNAKE_CASE     )
				depth_first_search(root.right ,		depth + 1 ,		SCREAMING_SNAKE_CASE     )

		SCREAMING_SNAKE_CASE_						:list										=      []
		if not root:
				return left_view

		depth_first_search(SCREAMING_SNAKE_CASE ,		0 ,		SCREAMING_SNAKE_CASE     )
		return left_view







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):

		def breadth_first_search(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ) -> None:
				SCREAMING_SNAKE_CASE_						:List[str]										=      [(root, 0)]
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      defaultdict(SCREAMING_SNAKE_CASE     )

				while queue:
						SCREAMING_SNAKE_CASE_						:int										=      queue.pop(0     )
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Tuple										=      first

						lookup[hd].append(node.val     )

						if node.left:
								queue.append((node.left, hd - 1)     )
						if node.right:
								queue.append((node.right, hd + 1)     )

				for pair in sorted(lookup.items() ,		key=lambda SCREAMING_SNAKE_CASE     : each[0]     ):
						top_view.append(pair[1][0]     )

		SCREAMING_SNAKE_CASE_						:list										=      []
		if not root:
				return top_view

		breadth_first_search(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		return top_view







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		from collections import defaultdict

		def breadth_first_search(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ) -> None:
				SCREAMING_SNAKE_CASE_						:int										=      [(root, 0)]
				SCREAMING_SNAKE_CASE_						:Tuple										=      defaultdict(SCREAMING_SNAKE_CASE     )

				while queue:
						SCREAMING_SNAKE_CASE_						:Optional[Any]										=      queue.pop(0     )
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[int]										=      first
						lookup[hd].append(node.val     )

						if node.left:
								queue.append((node.left, hd - 1)     )
						if node.right:
								queue.append((node.right, hd + 1)     )

				for pair in sorted(lookup.items() ,		key=lambda SCREAMING_SNAKE_CASE     : each[0]     ):
						bottom_view.append(pair[1][-1]     )

		SCREAMING_SNAKE_CASE_						:list										=      []
		if not root:
				return bottom_view

		breadth_first_search(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		return bottom_view


if __name__ == "__main__":
							import doctest

							doctest.testmod()





import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

			# Load weights from numpy
			load_tf_weights_in_imagegpt(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )

			# Save pytorch-model
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F"Save PyTorch model to {pytorch_weights_dump_path}"       )
			torch.save(model.state_dict()				,  _UpperCAmelCase       )
			print(F"Save configuration file to {pytorch_config_dump_path}"       )
			with open(_UpperCAmelCase				,  'w'				,  encoding='utf-8'       ) as f:
						f.write(config.to_json_string()       )


if __name__ == "__main__":
							UpperCAmelCase__  =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--imagegpt_checkpoint_path",
							    default=None,
							    type=str,
							    required=True,
							    help="Path to the TensorFlow checkpoint path.",
							)
							parser.add_argument(
							    "--model_size",
							    default=None,
							    type=str,
							    required=True,
							    help="Size of the model (can be either 'small', 'medium' or 'large').",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
