






import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=0      )->    Optional[int]:





						"""simple docstring"""



						if name is None:
												_UpperCAmelCase							   =     None
						else:
												_UpperCAmelCase							   =     "." * max(0					,						spaces - 2      ) + "# {:" + str(50 - spaces      ) + "s}"
												_UpperCAmelCase							   =     fmt.format(_UpperCAmelCase      )

						# Print and recurse (if needed).
						if isinstance(_UpperCAmelCase					,						_UpperCAmelCase      ):
												if msg is not None:
																		print(_UpperCAmelCase      )
												for k in val.keys():
																		recursive_print(_UpperCAmelCase					,						val[k]					,						spaces + 2      )
						elif isinstance(_UpperCAmelCase					,						torch.Tensor      ):
												print(_UpperCAmelCase					,						':'					,						val.size()      )
						else:
												print(_UpperCAmelCase					,						':'					,						_UpperCAmelCase      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    Any:





						"""simple docstring"""



						_UpperCAmelCase							   =     param.size()
						# other versions store [num_heads * num_splits * hidden_size, :]
						_UpperCAmelCase							   =     (num_heads, num_splits, hidden_size) + input_shape[1:]
						_UpperCAmelCase							   =     param.view(*_UpperCAmelCase      )
						_UpperCAmelCase							   =     param.transpose(0					,						1      ).contiguous()
						_UpperCAmelCase							   =     param.view(*_UpperCAmelCase      )
						return param


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     config.n_positions
						_UpperCAmelCase							   =     config.n_layer
						_UpperCAmelCase							   =     config.vocab_size
						_UpperCAmelCase							   =     config.n_head
						_UpperCAmelCase							   =     config.n_embd // config.n_head

						_UpperCAmelCase							   =     sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
						_UpperCAmelCase							   =     {
						    "transformer.wte.weight": word_embeddings,
						    "transformer.wpe.weight": sd_megatron["model.language_model.embedding.position_embeddings.weight"],
						    "transformer.ln_f.weight": sd_megatron["model.language_model.encoder.final_layernorm.weight"],
						    "transformer.ln_f.bias": sd_megatron["model.language_model.encoder.final_layernorm.bias"],
						}

						_UpperCAmelCase							   =     "model.language_model.encoder.layers."
						for i in range(_UpperCAmelCase      ):
												_UpperCAmelCase							   =     torch.tril(torch.ones((n_positions, n_positions)					,						dtype=torch.bool      )      )
												_UpperCAmelCase							   =     causal_mask.view(1					,						1					,						_UpperCAmelCase					,						_UpperCAmelCase      )
												_UpperCAmelCase							   =     causal_mask
												_UpperCAmelCase							   =     torch.tensor(-1E4					,						dtype=torch.bfloataa      )

												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.input_layernorm.weight"""]
												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.input_layernorm.bias"""]

												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.self_attention.query_key_value.weight"""]
												_UpperCAmelCase							   =     fix_query_key_value_ordering(_UpperCAmelCase					,						3					,						_UpperCAmelCase					,						_UpperCAmelCase      )
												_UpperCAmelCase							   =     vala.transpose(0					,						1      ).contiguous()

												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.self_attention.query_key_value.bias"""]
												_UpperCAmelCase							   =     fix_query_key_value_ordering(_UpperCAmelCase					,						3					,						_UpperCAmelCase					,						_UpperCAmelCase      )
												_UpperCAmelCase							   =     vala

												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.self_attention.dense.weight"""].transpose(
												    0					,						1      )
												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.self_attention.dense.bias"""]
												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.post_attention_layernorm.weight"""]
												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.post_attention_layernorm.bias"""]
												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0					,						1      )
												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
												    0					,						1      )
												_UpperCAmelCase							   =     sd_megatron[F"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

						# For LM head, transformers' wants the matrix to weight embeddings.
						_UpperCAmelCase							   =     word_embeddings

						return sd_hf


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    List[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     64_000
						_UpperCAmelCase							   =     config_megatron["encoder_seq_length"]
						_UpperCAmelCase							   =     config_megatron["hidden_size"]
						_UpperCAmelCase							   =     config_megatron["num_layers"]
						_UpperCAmelCase							   =     config_megatron["num_attention_heads"]
						_UpperCAmelCase							   =     config_megatron["ffn_hidden_size"]
						_UpperCAmelCase							   =     "gelu"
						_UpperCAmelCase							   =     0.1
						_UpperCAmelCase							   =     0.1
						_UpperCAmelCase							   =     0.1
						_UpperCAmelCase							   =     config_megatron["layernorm_epsilon"]  # 1e-5
						_UpperCAmelCase							   =     config_megatron["init_method_std"]  # 0.02
						_UpperCAmelCase							   =     config_megatron["apply_query_key_layer_scaling"]  # True
						_UpperCAmelCase							   =     True
						_UpperCAmelCase							   =     True

						# This identifies the 6.7B (7B) model which uses a different tokenizer
						if config_megatron["hidden_size"] == 4_096:
												_UpperCAmelCase							   =     1  # <|endoftext|>
												_UpperCAmelCase							   =     1  # <|endoftext|>
												_UpperCAmelCase							   =     0  # <unk>
						else:
												_UpperCAmelCase							   =     2  # <s>
												_UpperCAmelCase							   =     3  # <|endoftext|>
												_UpperCAmelCase							   =     0  # <pad>

						return config_hf


def __A       ( __lowerCAmelCase      )->    List[Any]:





						"""simple docstring"""



						print(_UpperCAmelCase      )

						_UpperCAmelCase							   =     args.checkpoint_path
						_UpperCAmelCase							   =     args.save_path
						if isfile(_UpperCAmelCase      ):
												raise FileNotFoundError(F"""ERROR! could not find file {checkpoint_path}"""      )

						# Load the model.
						_UpperCAmelCase							   =     torch.load(_UpperCAmelCase					,						map_location='cpu'      )

						# Load the config.
						_UpperCAmelCase							   =     checkpoint["hyper_parameters"]["cfg"]
						_UpperCAmelCase							   =     GPTaConfig()
						_UpperCAmelCase							   =     copy_config(config_hf=_UpperCAmelCase					,						config_megatron=_UpperCAmelCase      )
						_UpperCAmelCase							   =     ["GPT2LMHeadModel"]

						_UpperCAmelCase							   =     checkpoint["state_dict"]

						# Convert.
						print('Converting'      )
						_UpperCAmelCase							   =     convert_megatron_checkpoint(_UpperCAmelCase					,						_UpperCAmelCase      )

						# Print the structure of converted state dict.
						if args.print_checkpoint_structure:
												recursive_print(_UpperCAmelCase					,						_UpperCAmelCase      )

						_UpperCAmelCase							   =     "GPTSw3Tokenizer"

						# Store the config to file.
						print('Saving config'      )
						config_hf.save_pretrained(_UpperCAmelCase      )

						# Store the state_dict to file.
						_UpperCAmelCase							   =     os.path.join(_UpperCAmelCase					,						'pytorch_model.bin'      )
						print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""      )
						torch.save(_UpperCAmelCase					,						_UpperCAmelCase      )


if __name__ == "__main__":
		_a       =    argparse.ArgumentParser()
		parser.add_argument(
		    '''--checkpoint_path''',
		    type=str,
		    required=True,
		    help='''e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000''',
		)
		parser.add_argument('''--save_path''', type=str, required=True, help='''e.g. /home/user/gpt-sw3/hf''')
		parser.add_argument('''--print-checkpoint-structure''', action='''store_true''')
		_a       =    parser.parse_args()
		main(_args)







'''simple docstring'''

from __future__ import annotations

lowerCAmelCase__      :   Tuple      	=			{1: 1}


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		if n in COLLATZ_SEQUENCE_LENGTHS:
				return COLLATZ_SEQUENCE_LENGTHS[n]
		__UpperCAmelCase	:							int     =     n // 2 if n % 2 == 0 else 3 * n + 1
		__UpperCAmelCase	:							str     =     collatz_sequence_length(_UpperCAmelCase  ) + 1
		__UpperCAmelCase	:							Union[str, Any]     =     sequence_length
		return sequence_length


def 						__UpperCamelCase						(			_UpperCAmelCase = 1000000  ):
		__UpperCAmelCase	:							Union[str, Any]     =     max((collatz_sequence_length(_UpperCAmelCase  ), i) for i in range(1,       _UpperCAmelCase  )  )
		return result[1]


if __name__ == "__main__":
			print(solution(int(input().strip())))




