
import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		str    ,  a_			:		List[str]=0				)     ->       List[str]:
   # Format the message.
   if name is None:
      _UpperCAmelCase   	=					None
   else:
      _UpperCAmelCase   	=					"." * max(0    ,  spaces - 2				) + "# {:" + str(50 - spaces				) + "s}"
      _UpperCAmelCase   	=					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		(		a_			:		List[Any]    ,  a_			:		str    ,  a_			:		Dict    ,  a_			:		List[Any]				)     ->       Dict:
   # 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   	=					param.size()
   # other versions store [num_heads * num_splits * hidden_size, :]
   _UpperCAmelCase   	=					(num_heads, num_splits, hidden_size) + input_shape[1:]
   _UpperCAmelCase   	=					param.view(*a_				)
   _UpperCAmelCase   	=					param.transpose(0    ,  1				).contiguous()
   _UpperCAmelCase   	=					param.view(*a_				)
   return param
def 			__UpperCAmelCase		(		a_			:		List[str]    ,  a_			:		List[Any]				)     ->       Optional[int]:
   _UpperCAmelCase   	=					config.n_positions
   _UpperCAmelCase   	=					config.n_layer
   _UpperCAmelCase   	=					config.vocab_size
   _UpperCAmelCase   	=					config.n_head
   _UpperCAmelCase   	=					config.n_embd // config.n_head

   _UpperCAmelCase   	=					sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
   _UpperCAmelCase   	=					{
       "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   	=					"model.language_model.encoder.layers."
   for i in range(a_				):
      _UpperCAmelCase   	=					torch.tril(torch.ones((n_positions, n_positions)    ,  dtype=torch.bool				)				)
      _UpperCAmelCase   	=					causal_mask.view(1    ,  1    ,  a_    ,  a_				)
      _UpperCAmelCase   	=					causal_mask
      _UpperCAmelCase   	=					torch.tensor(-1e4    ,  dtype=torch.bfloataa				)

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

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

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

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

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

   return sd_hf
def 			__UpperCAmelCase		(		a_			:		Optional[int]    ,  a_			:		Any				)     ->       Union[str, Any]:
   _UpperCAmelCase   	=					6_40_00
   _UpperCAmelCase   	=					config_megatron["encoder_seq_length"]
   _UpperCAmelCase   	=					config_megatron["hidden_size"]
   _UpperCAmelCase   	=					config_megatron["num_layers"]
   _UpperCAmelCase   	=					config_megatron["num_attention_heads"]
   _UpperCAmelCase   	=					config_megatron["ffn_hidden_size"]
   _UpperCAmelCase   	=					"gelu"
   _UpperCAmelCase   	=					0.1
   _UpperCAmelCase   	=					0.1
   _UpperCAmelCase   	=					0.1
   _UpperCAmelCase   	=					config_megatron["layernorm_epsilon"]  # 1e-5
   _UpperCAmelCase   	=					config_megatron["init_method_std"]  # 0.02
   _UpperCAmelCase   	=					config_megatron["apply_query_key_layer_scaling"]  # True
   _UpperCAmelCase   	=					True
   _UpperCAmelCase   	=					True

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

   return config_hf
def 			__UpperCAmelCase		(		a_			:		Optional[Any]				)     ->       List[Any]:
   print(a_				)

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

   # Load the model.
   _UpperCAmelCase   	=					torch.load(a_    ,  map_location="cpu"				)

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

   _UpperCAmelCase   	=					checkpoint["state_dict"]

   # Convert.
   print("Converting"				)
   _UpperCAmelCase   	=					convert_megatron_checkpoint(a_    ,  a_				)

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

   _UpperCAmelCase   	=					"GPTSw3Tokenizer"

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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_     				=   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_     				=   parser.parse_args()
  main(_args)



"""simple docstring"""







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM
def 			__UpperCAmelCase		(		a_			:		Any				)     ->       List[str]:
   if "model" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("model."    ,  ""				)
   if "norm1" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "attention.output.LayerNorm"				)
   if "norm2" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "output.LayerNorm"				)
   if "norm" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("norm"    ,  "LayerNorm"				)
   if "transformer" in orig_key:
      _UpperCAmelCase   	=					orig_key.split("."				)[0].split("_"				)[-1]
      _UpperCAmelCase   	=					orig_key.replace(F'''transformer_{layer_num}'''    ,  F'''encoder.layer.{layer_num}'''				)
   if "mha.attn" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mha.attn"    ,  "attention.self"				)
   if "mha" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mha"    ,  "attention"				)
   if "W_q" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("W_q"    ,  "self.query"				)
   if "W_k" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("W_k"    ,  "self.key"				)
   if "W_v" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("W_v"    ,  "self.value"				)
   if "ff.0" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("ff.0"    ,  "intermediate.dense"				)
   if "ff.2" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("ff.2"    ,  "output.dense"				)
   if "ff" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("ff"    ,  "output.dense"				)
   if "mlm_class" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mlm.mlm_class"    ,  "cls.predictions.decoder"				)
   if "mlm" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mlm"    ,  "cls.predictions.transform"				)
   if "backbone.backbone.encoders" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("backbone.backbone.encoders"    ,  "encoder.layer"				)
   if "cls" not in orig_key:
      _UpperCAmelCase   	=					"mra." + orig_key

   return orig_key
def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		Any				)     ->       List[str]:
   for key in orig_state_dict.copy().keys():
      _UpperCAmelCase   	=					orig_state_dict.pop(a_				)

      if ("pooler" in key) or ("sen_class" in key):
         continue
      else:
         _UpperCAmelCase   	=					val

   _UpperCAmelCase   	=					orig_state_dict["cls.predictions.decoder.bias"]
   _UpperCAmelCase   	=					torch.arange(a_				).expand((1, -1)				) + 2

   return orig_state_dict
def 			__UpperCAmelCase		(		a_			:		int    ,  a_			:		Tuple    ,  a_			:		List[str]				)     ->       Any:
   _UpperCAmelCase   	=					torch.load(a_    ,  map_location="cpu"				)["model_state_dict"]
   _UpperCAmelCase   	=					MraConfig.from_json_file(a_				)
   _UpperCAmelCase   	=					MraForMaskedLM(a_				)

   _UpperCAmelCase   	=					convert_checkpoint_helper(config.max_position_embeddings    ,  a_				)

   print(model.load_state_dict(a_				)				)
   model.eval()
   model.save_pretrained(a_				)

   print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_     				=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help='The json file for Mra model config.',
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
  convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

