



# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig





def 						a__     (						_UpperCamelCase     :	str	,_UpperCamelCase     :	List[Any]	,_UpperCamelCase     :	List[Any]=0							):
   # Format the message.
   if name is None:
      __lowerCamelCase  							=      None
   else:
      __lowerCamelCase  							=      '''.''' * max(0	,spaces - 2							) + '''# {:''' + str(50 - spaces							) + '''s}'''
      __lowerCamelCase  							=      fmt.format(_UpperCamelCase							)

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





def 						a__     (						_UpperCamelCase     :	Optional[int]	,_UpperCamelCase     :	List[Any]	,_UpperCamelCase     :	List[Any]	,_UpperCamelCase     :	Any	,_UpperCamelCase     :	Optional[int]							):
   # 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.
   __lowerCamelCase  							=      param.size()
   if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      __lowerCamelCase  							=      (num_heads, hidden_size, num_splits) + input_shape[1:]
      __lowerCamelCase  							=      param.view(*_UpperCamelCase							)
      __lowerCamelCase  							=      param.transpose(0	,2							)
      __lowerCamelCase  							=      param.transpose(1	,2							).contiguous()
   elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      __lowerCamelCase  							=      (num_heads, num_splits, hidden_size) + input_shape[1:]
      __lowerCamelCase  							=      param.view(*_UpperCamelCase							)
      __lowerCamelCase  							=      param.transpose(0	,1							).contiguous()
   __lowerCamelCase  							=      param.view(*_UpperCamelCase							)
   return param





def 						a__     (						_UpperCamelCase     :	List[Any]	,_UpperCamelCase     :	Optional[Any]	,_UpperCamelCase     :	Union[str, Any]							):
   # The converted output model.
   __lowerCamelCase  							=      {}

   # old versions did not store training args
   __lowerCamelCase  							=      input_state_dict.get('''args'''	,_UpperCamelCase							)
   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))

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

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

   # The model.
   __lowerCamelCase  							=      input_state_dict['''model''']
   # The language model.
   __lowerCamelCase  							=      model['''language_model''']
   # The embeddings.
   __lowerCamelCase  							=      lm['''embedding''']

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

   # The position embeddings.
   __lowerCamelCase  							=      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.
   __lowerCamelCase  							=      pos_embeddings

   # The token-type embeddings.
   __lowerCamelCase  							=      embeddings['''tokentype_embeddings''']['''weight''']
   # Store the position embeddings.
   __lowerCamelCase  							=      tokentype_embeddings

   # The transformer.
   __lowerCamelCase  							=      lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

   # The simple map of names for "automated" rules.
   __lowerCamelCase  							=      {
       '''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.
   __lowerCamelCase  							=      None

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

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

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

      # The name of the layer.
      __lowerCamelCase  							=      F"""bert.encoder.layer.{layer_idx}"""

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

         __lowerCamelCase  							=      fix_query_key_value_ordering(_UpperCamelCase	,_UpperCamelCase	,3	,_UpperCamelCase	,_UpperCamelCase							)
         # Store the tensor as we need the bias as well to interleave QKV and biases.
         __lowerCamelCase  							=      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.
         __lowerCamelCase  							=      attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
         __lowerCamelCase  							=      attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
         __lowerCamelCase  							=      attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

         # Clear the stored tensor.
         __lowerCamelCase  							=      None

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

    # The final layernorm.
   __lowerCamelCase  							=      transformer['''final_layernorm.weight''']
   __lowerCamelCase  							=      transformer['''final_layernorm.bias''']

   # The pooler.
   __lowerCamelCase  							=      lm['''pooler''']

   # Store the matrix and the bias.
   __lowerCamelCase  							=      pooler['''dense.weight''']
   __lowerCamelCase  							=      pooler['''dense.bias''']

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

   # The transform matrix.
   __lowerCamelCase  							=      lm_head['''dense.weight''']
   __lowerCamelCase  							=      lm_head['''dense.bias''']

   # The transform LN.
   __lowerCamelCase  							=      lm_head['''layernorm.weight''']
   __lowerCamelCase  							=      lm_head['''layernorm.bias''']

   # For the decoder, we replicate the weights.
   __lowerCamelCase  							=      word_embeddings
   __lowerCamelCase  							=      lm_head['''bias''']

   # The classifier from Megatron (for MLNI).
   __lowerCamelCase  							=      model['''binary_head''']

   # Store the classifier.
   __lowerCamelCase  							=      binary_head['''weight''']
   __lowerCamelCase  							=      binary_head['''bias''']

   # It should be done!
   return output_state_dict





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

   # Extract the basename.
   __lowerCamelCase  							=      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:
            __lowerCamelCase  							=      torch.load(_UpperCamelCase	,map_location='''cpu'''							)
   else:
      __lowerCamelCase  							=      torch.load(args.path_to_checkpoint	,map_location='''cpu'''							)

   if args.config_file == "":
      # Default config of megatron-bert 345m
      __lowerCamelCase  							=      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
      __lowerCamelCase  							=      input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
   else:
      __lowerCamelCase  							=      MegatronBertConfig.from_json_file(args.config_file							)

   # Convert.
   print('''Converting'''							)
   __lowerCamelCase  							=      convert_megatron_checkpoint(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)

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

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

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


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

if __name__ == "__main__":
  main()

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











def 						a__     (						_UpperCamelCase     :	int							):
   if not isinstance(_UpperCamelCase	,_UpperCamelCase							):
      __lowerCamelCase  							=      (
          '''operation can not be conducted on a object of type '''
          F"""{type(_UpperCamelCase							).__name__}"""
      )
      raise TypeError(_UpperCamelCase							)
   __lowerCamelCase  							=      ''''''
   for _ in range(0	,32							):
      bit_string += str(number % 2							)
      __lowerCamelCase  							=      number >> 1
   return bit_string





def 						a__     (						_UpperCamelCase     :	int							):
   if number < 0:
      raise ValueError('''the value of input must be positive'''							)
   elif isinstance(_UpperCamelCase	,_UpperCamelCase							):
      raise TypeError('''Input value must be a \'int\' type'''							)
   elif isinstance(_UpperCamelCase	,_UpperCamelCase							):
      raise TypeError('''\'<\' not supported between instances of \'str\' and \'int\''''							)
   __lowerCamelCase  							=      0
   # iterator over [1 to 32],since we are dealing with 32 bit integer
   for _ in range(1	,33							):
      # left shift the bits by unity
      __lowerCamelCase  							=      result << 1
      # get the end bit
      __lowerCamelCase  							=      number % 2
      # right shift the bits by unity
      __lowerCamelCase  							=      number >> 1
      # add that bit to our ans
      __lowerCamelCase  							=      result | end_bit
   return get_reverse_bit_string(_UpperCamelCase							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






