
"""simple docstring"""





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase=0    ):
   # Format the message.
   if name is None:
      UpperCAmelCase_   :   str     	=       None
   else:
      UpperCAmelCase_   :   Tuple     	=       "." * max(0,						spaces - 2    ) + "# {:" + str(50 - spaces    ) + "s}"
      UpperCAmelCase_   :   Optional[int]     	=       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       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
   # for compatibility with later versions of NVIDIA Megatron-LM.
   # The inverse operation is performed inside Megatron-LM to read checkpoints:
   # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
   # If param is the weight tensor of the self-attention block, the returned tensor
   # will have to be transposed one more time to be read by HuggingFace GPT2.
   UpperCAmelCase_   :   Any     	=       param.size()
   # other versions store [num_heads * num_splits * hidden_size, :]
   UpperCAmelCase_   :   List[Any]     	=       (num_heads, num_splits, hidden_size) + input_shape[1:]
   UpperCAmelCase_   :   List[Any]     	=       param.view(*__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       param.transpose(0,						1    ).contiguous()
   UpperCAmelCase_   :   Union[str, Any]     	=       param.view(*__lowerCamelCase    )
   return param







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Dict     	=       config.n_positions
   UpperCAmelCase_   :   Optional[int]     	=       config.n_layer
   UpperCAmelCase_   :   Any     	=       config.vocab_size
   UpperCAmelCase_   :   Optional[Any]     	=       config.n_head
   UpperCAmelCase_   :   Optional[int]     	=       config.n_embd // config.n_head

   UpperCAmelCase_   :   Union[str, Any]     	=       sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
   UpperCAmelCase_   :   Union[str, Any]     	=       {
       "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_   :   Union[str, Any]     	=       "model.language_model.encoder.layers."
   for i in range(__lowerCamelCase    ):
      UpperCAmelCase_   :   Tuple     	=       torch.tril(torch.ones((n_positions, n_positions),						dtype=torch.bool    )    )
      UpperCAmelCase_   :   Optional[int]     	=       causal_mask.view(1,						1,						__lowerCamelCase,						__lowerCamelCase    )
      UpperCAmelCase_   :   Any     	=       causal_mask
      UpperCAmelCase_   :   List[Any]     	=       torch.tensor(-1E4,						dtype=torch.bfloataa    )

      UpperCAmelCase_   :   List[str]     	=       sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
      UpperCAmelCase_   :   Union[str, Any]     	=       sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

      UpperCAmelCase_   :   List[Any]     	=       sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
      UpperCAmelCase_   :   List[Any]     	=       fix_query_key_value_ordering(__lowerCamelCase,						3,						__lowerCamelCase,						__lowerCamelCase    )
      UpperCAmelCase_   :   Optional[Any]     	=       vala.transpose(0,						1    ).contiguous()

      UpperCAmelCase_   :   Any     	=       sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
      UpperCAmelCase_   :   Dict     	=       fix_query_key_value_ordering(__lowerCamelCase,						3,						__lowerCamelCase,						__lowerCamelCase    )
      UpperCAmelCase_   :   List[str]     	=       vala

      UpperCAmelCase_   :   List[Any]     	=       sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
          0,						1    )
      UpperCAmelCase_   :   Union[str, Any]     	=       sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
      UpperCAmelCase_   :   str     	=       sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
      UpperCAmelCase_   :   Union[str, Any]     	=       sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
      UpperCAmelCase_   :   List[Any]     	=       sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0,						1    )
      UpperCAmelCase_   :   Any     	=       sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
      UpperCAmelCase_   :   int     	=       sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
          0,						1    )
      UpperCAmelCase_   :   Optional[Any]     	=       sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

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

   return sd_hf







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   int     	=       6_4000
   UpperCAmelCase_   :   List[Any]     	=       config_megatron["encoder_seq_length"]
   UpperCAmelCase_   :   Dict     	=       config_megatron["hidden_size"]
   UpperCAmelCase_   :   List[Any]     	=       config_megatron["num_layers"]
   UpperCAmelCase_   :   Optional[Any]     	=       config_megatron["num_attention_heads"]
   UpperCAmelCase_   :   Optional[Any]     	=       config_megatron["ffn_hidden_size"]
   UpperCAmelCase_   :   List[str]     	=       "gelu"
   UpperCAmelCase_   :   Tuple     	=       0.1
   UpperCAmelCase_   :   Any     	=       0.1
   UpperCAmelCase_   :   Dict     	=       0.1
   UpperCAmelCase_   :   Optional[Any]     	=       config_megatron["layernorm_epsilon"]  # 1e-5
   UpperCAmelCase_   :   Dict     	=       config_megatron["init_method_std"]  # 0.02
   UpperCAmelCase_   :   Optional[int]     	=       config_megatron["apply_query_key_layer_scaling"]  # True
   UpperCAmelCase_   :   List[Any]     	=       True
   UpperCAmelCase_   :   str     	=       True

   # This identifies the 6.7B (7B) model which uses a different tokenizer
   if config_megatron["hidden_size"] == 4096:
      UpperCAmelCase_   :   Optional[int]     	=       1  # <|endoftext|>
      UpperCAmelCase_   :   Optional[Any]     	=       1  # <|endoftext|>
      UpperCAmelCase_   :   List[Any]     	=       0  # <unk>
   else:
      UpperCAmelCase_   :   str     	=       2  # <s>
      UpperCAmelCase_   :   List[str]     	=       3  # <|endoftext|>
      UpperCAmelCase_   :   List[Any]     	=       0  # <pad>

   return config_hf







def       __a	(						__lowerCamelCase    ):
   print(__lowerCamelCase    )

   UpperCAmelCase_   :   Optional[int]     	=       args.checkpoint_path
   UpperCAmelCase_   :   Dict     	=       args.save_path
   if isfile(__lowerCamelCase    ):
      raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""    )

   # Load the model.
   UpperCAmelCase_   :   List[Any]     	=       torch.load(__lowerCamelCase,						map_location="cpu"    )

   # Load the config.
   UpperCAmelCase_   :   List[str]     	=       checkpoint["hyper_parameters"]["cfg"]
   UpperCAmelCase_   :   str     	=       GPTaConfig()
   UpperCAmelCase_   :   Tuple     	=       copy_config(config_hf=__lowerCamelCase,						config_megatron=__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       ["GPT2LMHeadModel"]

   UpperCAmelCase_   :   int     	=       checkpoint["state_dict"]

   # Convert.
   print("Converting"    )
   UpperCAmelCase_   :   Optional[Any]     	=       convert_megatron_checkpoint(__lowerCamelCase,						__lowerCamelCase    )

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

   UpperCAmelCase_   :   Tuple     	=       "GPTSw3Tokenizer"

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

   # Store the state_dict to file.
   UpperCAmelCase_   :   Tuple     	=       os.path.join(__lowerCamelCase,						"pytorch_model.bin"    )
   print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
   torch.save(__lowerCamelCase,						__lowerCamelCase    )


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 ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Dict				       =					c_float




      @property
      def 							lowerCamelCase     (					self						):
             return sizeof(self.value						)






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     int			,						a_  :     int = 0			)   -> int:
       # < 0 is possible if using a dynamic axis
       if dimension <= 0:
              UpperCAmelCase__	:   List[Any]												=	fixed_dimension

       dimension -= num_token_to_add
       return dimension






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     ParameterFormat			)   -> int:
       return num_parameters * dtype.size






def  lowerCAmelCase__		(					a_  :     str			)   -> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

       try:
              return AutoProcessor.from_pretrained(a_			)
       except (ValueError, OSError, KeyError):
              UpperCAmelCase__							,							UpperCAmelCase__	:   Dict												=	None, None
              try:
                     UpperCAmelCase__	:   Union[str, Any]												=	AutoTokenizer.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass
              try:
                     UpperCAmelCase__	:   List[str]												=	AutoFeatureExtractor.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass

              if tokenizer is not None and feature_extractor is not None:
                     raise ValueError(
                         f"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""			)
              elif tokenizer is None and feature_extractor is None:
                     return None
              elif tokenizer is not None:
                     return tokenizer
              else:
                     return feature_extractor