




"""simple docstring"""



import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_=0		)		->	Tuple:
 # Format the message.
 if name is None:
  A__ =       None
 else:
  A__ =       "." * max(0    ,				spaces - 2		) + "# {:" + str(50 - spaces		) + "s}"
  A__ =       fmt.format(lowercase_		)

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







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	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 BERT.
 A__ =       param.size()
 if checkpoint_version == 1.0:
  # version 1.0 stores [num_heads * hidden_size * num_splits, :]
  A__ =       (num_heads, hidden_size, num_splits) + input_shape[1:]
  A__ =       param.view(*lowercase_		)
  A__ =       param.transpose(0    ,				2		)
  A__ =       param.transpose(1    ,				2		).contiguous()
 elif checkpoint_version >= 2.0:
  # other versions store [num_heads * num_splits * hidden_size, :]
  A__ =       (num_heads, num_splits, hidden_size) + input_shape[1:]
  A__ =       param.view(*lowercase_		)
  A__ =       param.transpose(0    ,				1		).contiguous()
 A__ =       param.view(*lowercase_		)
 return param







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
 # The converted output model.
 A__ =       {}

 # old versions did not store training args
 A__ =       input_state_dict.get("args"    ,				lowercase_		)
 if ds_args is not None:
  # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
  # from pprint import pprint
  # pprint(vars(ds_args))

  A__ =       ds_args.tokenizer_type
  A__ =       ds_args.padded_vocab_size
  A__ =       ds_args.max_position_embeddings
  A__ =       ds_args.hidden_size
  A__ =       ds_args.num_layers
  A__ =       ds_args.num_attention_heads
  A__ =       ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
  # pprint(config)

 # The number of heads.
 A__ =       config.num_attention_heads
 # The hidden_size per head.
 A__ =       config.hidden_size // heads
 # Megatron-LM checkpoint version
 if "checkpoint_version" in input_state_dict.keys():
  A__ =       input_state_dict["checkpoint_version"]
 else:
  A__ =       0.0

 # The model.
 A__ =       input_state_dict["model"]
 # The language model.
 A__ =       model["language_model"]
 # The embeddings.
 A__ =       lm["embedding"]

 # The word embeddings.
 A__ =       embeddings["word_embeddings"]["weight"]
 # Truncate the embedding table to vocab_size rows.
 A__ =       word_embeddings[: config.vocab_size, :]
 # Store the word embeddings.
 A__ =       word_embeddings

 # The position embeddings.
 A__ =       embeddings["position_embeddings"]["weight"]
 assert pos_embeddings.size(0		) == config.max_position_embeddings and pos_embeddings.size(1		) == config.hidden_size
 # Store the position embeddings.
 A__ =       pos_embeddings

 # The token-type embeddings.
 A__ =       embeddings["tokentype_embeddings"]["weight"]
 # Store the position embeddings.
 A__ =       tokentype_embeddings

 # The transformer.
 A__ =       lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

 # The regex to extract layer names.
 A__ =       re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"		)

 # The simple map of names for "automated" rules.
 A__ =       {
     "attention.dense": ".attention.output.dense.",
     "self_attention.dense": ".attention.output.dense.",
     "mlp.dense_h_to_4h": ".intermediate.dense.",
     "mlp.dense_4h_to_h": ".output.dense.",
 }

 # Keep track of the attention/query/value tensor.
 A__ =       None

 # Extract the layers.
 for key, val in transformer.items():
  # Match the name.
  A__ =       layer_re.match(lowercase_		)

  # Stop if that's not a layer
  if m is None:
   break

  # The index of the layer.
  A__ =       int(m.group(1		)		)
  # The name of the operation.
  A__ =       m.group(2		)
  # Is it a weight or a bias?
  A__ =       m.group(3		)

  # The name of the layer.
  A__ =       f"""bert.encoder.layer.{layer_idx}"""

  # For layernorm(s), simply store the layer norm.
  if op_name.endswith("layernorm"		):
   A__ =       "attention.ln" if op_name.startswith("input"		) else "ln"
   A__ =       val

  # Transpose the QKV matrix.
  elif (
      op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
  ) and weight_or_bias == "weight":
   # Make sure the QKV pointer is nil.
   assert attention_qkv_weight is None, ""

   A__ =       fix_query_key_value_ordering(lowercase_    ,				lowercase_    ,				3    ,				lowercase_    ,				lowercase_		)
   # Store the tensor as we need the bias as well to interleave QKV and biases.
   A__ =       out_val

  # Transpose the bias.
  elif (
      op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
  ) and weight_or_bias == "bias":
   # Make sure we read the weight tensor.
   assert attention_qkv_weight is not None, ""

   # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
   A__ =       attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
   A__ =       attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
   A__ =       attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

   A__ =       fix_query_key_value_ordering(lowercase_    ,				lowercase_    ,				3    ,				lowercase_    ,				lowercase_		)
   # Split the bias.
   A__ =       out_val[0 * config.hidden_size : 1 * config.hidden_size]
   A__ =       out_val[1 * config.hidden_size : 2 * config.hidden_size]
   A__ =       out_val[2 * config.hidden_size : 3 * config.hidden_size]

   # Store.
   A__ =       q
   A__ =       q_bias
   A__ =       k
   A__ =       k_bias
   A__ =       v
   A__ =       v_bias

   # Clear the stored tensor.
   A__ =       None

  # Copy weights and biases as is.
  elif weight_or_bias in ["weight", "bias"]:
   A__ =       megatron_to_transformers[op_name]
   A__ =       val

    # The final layernorm.
 A__ =       transformer["final_layernorm.weight"]
 A__ =       transformer["final_layernorm.bias"]

 # The pooler.
 A__ =       lm["pooler"]

 # Store the matrix and the bias.
 A__ =       pooler["dense.weight"]
 A__ =       pooler["dense.bias"]

 # The LM head from Megatron (for RACE).
 A__ =       model["lm_head"]

 # The transform matrix.
 A__ =       lm_head["dense.weight"]
 A__ =       lm_head["dense.bias"]

 # The transform LN.
 A__ =       lm_head["layernorm.weight"]
 A__ =       lm_head["layernorm.bias"]

 # For the decoder, we replicate the weights.
 A__ =       word_embeddings
 A__ =       lm_head["bias"]

 # The classifier from Megatron (for MLNI).
 A__ =       model["binary_head"]

 # Store the classifier.
 A__ =       binary_head["weight"]
 A__ =       binary_head["bias"]

 # It should be done!
 return output_state_dict







def 						_SCREAMING_SNAKE_CASE     (							)		->	str:
 # Create the argument parser.
 A__ =       argparse.ArgumentParser()
 parser.add_argument("--print-checkpoint-structure"    ,				action="store_true"		)
 parser.add_argument("path_to_checkpoint"    ,				type=lowercase_    ,				help="Path to the ZIP file containing the checkpoint"		)
 parser.add_argument(
     "--config_file"    ,				default=""    ,				type=lowercase_    ,				help="An optional config json file describing the pre-trained model."    ,				)
 A__ =       parser.parse_args()

 # Extract the basename.
 A__ =       os.path.dirname(args.path_to_checkpoint		)

 # Load the model.
 # the .zip is very optional, let's keep it for backward compatibility
 print(f"""Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\""""		)
 if args.path_to_checkpoint.endswith(".zip"		):
  with zipfile.ZipFile(args.path_to_checkpoint    ,				"r"		) as checkpoint:
   with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"		) as pytorch_dict:
    A__ =       torch.load(lowercase_    ,				map_location="cpu"		)
 else:
  A__ =       torch.load(args.path_to_checkpoint    ,				map_location="cpu"		)

 if args.config_file == "":
  # Default config of megatron-bert 345m
  A__ =       MegatronBertConfig()

  # different megatron-bert-*-345m models have different vocab sizes, so override the default
  # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
  A__ =       input_state_dict["model"]["lm_head"]["bias"].numel()
 else:
  A__ =       MegatronBertConfig.from_json_file(args.config_file		)

 # Convert.
 print("Converting"		)
 A__ =       convert_megatron_checkpoint(lowercase_    ,				lowercase_    ,				lowercase_		)

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

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

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


####################################################################################################

if __name__ == "__main__":
      main()

####################################################################################################











"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	str:
 return " ".join(sorted(set(sentence.split()		)		)		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





