






"""simple docstring"""



import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[str]						,   __snake_case     :    Dict						,   __snake_case     :    str=0       ):





  '''simple docstring'''





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

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





  '''simple docstring'''





  lowercase				       =			param.size()
  # other versions store [num_heads * num_splits * hidden_size, :]
  lowercase				       =			(num_heads, num_splits, hidden_size) + input_shape[1:]
  lowercase				       =			param.view(*__snake_case       )
  lowercase				       =			param.transpose(0						,   1       ).contiguous()
  lowercase				       =			param.view(*__snake_case       )
  return param
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    int       ):





  '''simple docstring'''





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

  lowercase				       =			sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
  lowercase				       =			{
      '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'],
  }

  lowercase				       =			'model.language_model.encoder.layers.'
  for i in range(__snake_case       ):
    lowercase				       =			torch.tril(torch.ones((n_positions, n_positions)						,   dtype=torch.bool       )       )
    lowercase				       =			causal_mask.view(1						,   1						,   __snake_case						,   __snake_case       )
    lowercase				       =			causal_mask
    lowercase				       =			torch.tensor(-1e4						,   dtype=torch.bfloataa       )

    lowercase				       =			sd_megatron[f'{pf}{i}.input_layernorm.weight']
    lowercase				       =			sd_megatron[f'{pf}{i}.input_layernorm.bias']

    lowercase				       =			sd_megatron[f'{pf}{i}.self_attention.query_key_value.weight']
    lowercase				       =			fix_query_key_value_ordering(__snake_case						,   3						,   __snake_case						,   __snake_case       )
    lowercase				       =			vala.transpose(0						,   1       ).contiguous()

    lowercase				       =			sd_megatron[f'{pf}{i}.self_attention.query_key_value.bias']
    lowercase				       =			fix_query_key_value_ordering(__snake_case						,   3						,   __snake_case						,   __snake_case       )
    lowercase				       =			vala

    lowercase				       =			sd_megatron[f'{pf}{i}.self_attention.dense.weight'].transpose(
        0						,   1       )
    lowercase				       =			sd_megatron[f'{pf}{i}.self_attention.dense.bias']
    lowercase				       =			sd_megatron[f'{pf}{i}.post_attention_layernorm.weight']
    lowercase				       =			sd_megatron[f'{pf}{i}.post_attention_layernorm.bias']
    lowercase				       =			sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0						,   1       )
    lowercase				       =			sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.bias']
    lowercase				       =			sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
        0						,   1       )
    lowercase				       =			sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.bias']

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

  return sd_hf
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    int       ):





  '''simple docstring'''





  lowercase				       =			6_40_00
  lowercase				       =			config_megatron['encoder_seq_length']
  lowercase				       =			config_megatron['hidden_size']
  lowercase				       =			config_megatron['num_layers']
  lowercase				       =			config_megatron['num_attention_heads']
  lowercase				       =			config_megatron['ffn_hidden_size']
  lowercase				       =			'gelu'
  lowercase				       =			0.1
  lowercase				       =			0.1
  lowercase				       =			0.1
  lowercase				       =			config_megatron['layernorm_epsilon']  # 1e-5
  lowercase				       =			config_megatron['init_method_std']  # 0.02
  lowercase				       =			config_megatron['apply_query_key_layer_scaling']  # True
  lowercase				       =			True
  lowercase				       =			True

  # This identifies the 6.7B (7B) model which uses a different tokenizer
  if config_megatron["hidden_size"] == 40_96:
    lowercase				       =			1  # <|endoftext|>
    lowercase				       =			1  # <|endoftext|>
    lowercase				       =			0  # <unk>
  else:
    lowercase				       =			2  # <s>
    lowercase				       =			3  # <|endoftext|>
    lowercase				       =			0  # <pad>

  return config_hf
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any       ):





  '''simple docstring'''





  print(__snake_case       )

  lowercase				       =			args.checkpoint_path
  lowercase				       =			args.save_path
  if isfile(__snake_case       ):
    raise FileNotFoundError(f'ERROR! could not find file {checkpoint_path}'       )

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

  # Load the config.
  lowercase				       =			checkpoint['hyper_parameters']['cfg']
  lowercase				       =			GPTaConfig()
  lowercase				       =			copy_config(config_hf=__snake_case						,   config_megatron=__snake_case       )
  lowercase				       =			['GPT2LMHeadModel']

  lowercase				       =			checkpoint['state_dict']

  # Convert.
  print('Converting'       )
  lowercase				       =			convert_megatron_checkpoint(__snake_case						,   __snake_case       )

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

  lowercase				       =			'GPTSw3Tokenizer'

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

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


if __name__ == "__main__":
    _UpperCamelCase						:    List[Any]		=						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')
    _UpperCamelCase						:    Optional[Any]		=						parser.parse_args()
    main(_args)






'''simple docstring'''




import os





def 				a_							(    )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   Dict           =       os.path.dirname(os.path.realpath(lowercase     )     )
				__lowerCamelCase		:   Any           =       os.path.join(lowercase				,    "triangle.txt"     )

				with open(lowercase     ) as in_file:
								__lowerCamelCase		:   List[str]           =       [[int(lowercase     ) for i in line.split()] for line in in_file]

				while len(lowercase     ) != 1:
								__lowerCamelCase		:   List[str]           =       triangle.pop()
								__lowerCamelCase		:   Union[str, Any]           =       triangle[-1]
								for j in range(len(lowercase     ) - 1     ):
												curr_row[j] += max(last_row[j]				,    last_row[j + 1]     )
				return triangle[0][0]


if __name__ == "__main__":
				print(solution())





