






"""simple docstring"""


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase=0			):




  """simple docstring"""







  if name is None:
    __A						      =			None
  else:
    __A						      =			'''.''' * max(0		,	spaces - 2			) + '''# {:''' + str(5_0 - spaces			) + '''s}'''
    __A						      =			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  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







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



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







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

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

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

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

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

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

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

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

  return sd_hf



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







  __A						      =			6_4_0_0_0
  __A						      =			config_megatron['''encoder_seq_length''']
  __A						      =			config_megatron['''hidden_size''']
  __A						      =			config_megatron['''num_layers''']
  __A						      =			config_megatron['''num_attention_heads''']
  __A						      =			config_megatron['''ffn_hidden_size''']
  __A						      =			'''gelu'''
  __A						      =			0.1
  __A						      =			0.1
  __A						      =			0.1
  __A						      =			config_megatron['''layernorm_epsilon''']  # 1e-5
  __A						      =			config_megatron['''init_method_std''']  # 0.02
  __A						      =			config_megatron['''apply_query_key_layer_scaling''']  # True
  __A						      =			True
  __A						      =			True

  # This identifies the 6.7B (7B) model which uses a different tokenizer
  if config_megatron["hidden_size"] == 4_0_9_6:
    __A						      =			1  # <|endoftext|>
    __A						      =			1  # <|endoftext|>
    __A						      =			0  # <unk>
  else:
    __A						      =			2  # <s>
    __A						      =			3  # <|endoftext|>
    __A						      =			0  # <pad>

  return config_hf



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  print(__UpperCamelCase			)

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

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

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

  __A						      =			checkpoint['''state_dict''']

  # Convert.
  print('''Converting'''			)
  __A						      =			convert_megatron_checkpoint(__UpperCamelCase		,	__UpperCamelCase			)

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

  __A						      =			'''GPTSw3Tokenizer'''

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

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


if __name__ == "__main__":
       lowercase_    					=					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')
       lowercase_    					=					parser.parse_args()
       main(_args)












"""simple docstring"""


import itertools
from pathlib import Path



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  __A						      =			[tuple(__UpperCamelCase			) for login in logins]

  __A						      =			{char for login in split_logins for char in login}

  for permutation in itertools.permutations(__UpperCamelCase			):
    __A						      =			True
    for login in logins:
      if not (
          permutation.index(login[0]			)
          < permutation.index(login[1]			)
          < permutation.index(login[2]			)
      ):
        __A						      =			False
        break

    if satisfied:
      return int(''''''.join(__UpperCamelCase			)			)

  raise Exception('''Unable to find the secret passcode'''			)



def  lowerCAmelCase    (   __UpperCamelCase = "keylog.txt"			):




  """simple docstring"""







  __A						      =			Path(__UpperCamelCase			).parent.joinpath(__UpperCamelCase			).read_text().splitlines()

  return find_secret_passcode(__UpperCamelCase			)


if __name__ == "__main__":
       print(F'''{solution() = }''')




