"""simple docstring"""


class   UpperCAmelCase_	:
    def __init__(	self			:       int   ,						_lowercase			:       int		)						->    None:
       _lowercase							     =       data
       _lowercase							     =       None
       _lowercase							     =       None


def   __UpperCAmelCase     (	_snake_case		:       BinaryTreeNode | None,				_snake_case		:       int       ):
   if node is None:
      _lowercase							     =       BinaryTreeNode(_UpperCAmelCase       )
      return node

   # binary search tree is not empty,
   # so we will insert it into the tree
   # if new_value is less than value of data in node,
   #  add it to left subtree and proceed recursively
   if new_value < node.data:
      _lowercase							     =       insert(node.left_child,				_UpperCAmelCase       )
   else:
      # if new_value is greater than value of data in node,
      #  add it to right subtree and proceed recursively
      _lowercase							     =       insert(node.right_child,				_UpperCAmelCase       )
   return node


def   __UpperCAmelCase     (	_snake_case		:       None | BinaryTreeNode       ):  # if node is None,return
   if node:
      _lowercase							     =       inorder(node.left_child       )
      _lowercase							     =       [*inorder_array, node.data]
      _lowercase							     =       inorder_array + inorder(node.right_child       )
   else:
      _lowercase							     =       []
   return inorder_array


def   __UpperCAmelCase     (	):
   _lowercase							     =       insert(_UpperCAmelCase,				1_5       )
   insert(_UpperCAmelCase,				1_0       )
   insert(_UpperCAmelCase,				2_5       )
   insert(_UpperCAmelCase,				6       )
   insert(_UpperCAmelCase,				1_4       )
   insert(_UpperCAmelCase,				2_0       )
   insert(_UpperCAmelCase,				6_0       )
   return root


def   __UpperCAmelCase     (	):
   _lowercase							     =       make_tree()
   print("Printing values of binary search tree in Inorder Traversal."       )
   inorder(_UpperCAmelCase       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




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
							)
