





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def  __UpperCamelCase    (						snake_case							,			snake_case							,			snake_case=0      )    ->		Any:


   '''simple docstring'''







   if name is None:
      __A    =  None
   else:
      __A    =  """.""" * max(0							,			spaces - 2      ) + """# {:""" + str(5_0 - spaces      ) + """s}"""
      __A    =  fmt.format(_a      )

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


   '''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(*_a      )
   __A    =  param.transpose(0							,			1      ).contiguous()
   __A    =  param.view(*_a      )
   return param
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		Any:


   '''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(_a      ):
      __A    =  torch.tril(torch.ones((n_positions, n_positions)							,			dtype=torch.bool      )      )
      __A    =  causal_mask.view(1							,			1							,			_a							,			_a      )
      __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(_a							,			3							,			_a							,			_a      )
      __A    =  vala.transpose(0							,			1      ).contiguous()

      __A    =  sd_megatron[F"{pf}{i}.self_attention.query_key_value.bias"]
      __A    =  fix_query_key_value_ordering(_a							,			3							,			_a							,			_a      )
      __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  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		str:


   '''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  __UpperCamelCase    (						snake_case      )    ->		int:


   '''simple docstring'''







   print(_a      )

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

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

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

   __A    =  checkpoint["""state_dict"""]

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

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

   __A    =  """GPTSw3Tokenizer"""

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

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


if __name__ == "__main__":
    _UpperCamelCase       :				Tuple           =			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[int]           =			parser.parse_args()
    main(_args)








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_       : List[Any]							=    {
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : str							=    ["""RetriBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Optional[Any]							=    [
          """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """RetriBertModel""",
          """RetriBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
      from .tokenization_retribert import RetriBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_retribert_fast import RetriBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_retribert import (
                RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RetriBertModel,
                RetriBertPreTrainedModel,
            )

else:
      import sys

      UpperCAmelCase_       : Tuple							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
