


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:str						,  lowerCAmelCase_							:Tuple=0							)->List[str]:
   '''simple docstring'''






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

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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Any							)->Optional[int]:
   '''simple docstring'''






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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:int							)->Union[str, Any]:
   '''simple docstring'''






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

   snake_case_         =   sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
   snake_case_         =   {
       "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"],
   }

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

      snake_case_         =   sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
      snake_case_         =   sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

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

      snake_case_         =   sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
      snake_case_         =   fix_query_key_value_ordering(lowerCAmelCase_						,  3						,  lowerCAmelCase_						,  lowerCAmelCase_							)
      snake_case_         =   vala

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

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

   return sd_hf



def      _lowerCAmelCase  (							lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Union[str, Any]							)->Union[str, Any]:
   '''simple docstring'''






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

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

   return config_hf



def      _lowerCAmelCase  (							lowerCAmelCase_							:Any							)->Union[str, Any]:
   '''simple docstring'''






   print(lowerCAmelCase_							)

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

   # Load the model.
   snake_case_         =   torch.load(lowerCAmelCase_						,  map_location="cpu"							)

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

   snake_case_         =   checkpoint["state_dict"]

   # Convert.
   print("Converting"							)
   snake_case_         =   convert_megatron_checkpoint(lowerCAmelCase_						,  lowerCAmelCase_							)

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

   snake_case_         =   "GPTSw3Tokenizer"

   # Store the config to file.
   print("Saving config"							)
   config_hf.save_pretrained(lowerCAmelCase_							)

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE		:Union[str, 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''')
   SCREAMING_SNAKE_CASE		:Dict											=       parser.parse_args()
   main(_args)








from .prime_check import Test

Test()




