




# 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 				UpperCAmelCase__  (							_A      ,      _A      ,      _A=0			):






  """simple docstring"""


  if name is None:
    a_		    =  None
  else:
    a_		    =  '''.''' * max(0      ,      spaces - 2			) + '''# {:''' + str(50 - 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__  (							_A      ,      _A      ,      _A      ,      _A      ,      _A			):






  """simple docstring"""


  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(*_A			)
    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(*_A			)
    a_		    =  param.transpose(0      ,      1			).contiguous()
  a_		    =  param.view(*_A			)
  return param






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  {}

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

    # 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(_A      ,      _A      ,      3      ,      _A      ,      _A			)
      # 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(_A      ,      _A      ,      3      ,      _A      ,      _A			)
      # 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 				UpperCAmelCase__  (							):






  """simple docstring"""


  a_		    =  argparse.ArgumentParser()
  parser.add_argument('''--print-checkpoint-structure'''      ,      action='''store_true'''			)
  parser.add_argument('''path_to_checkpoint'''      ,      type=_A      ,      help='''Path to the ZIP file containing the checkpoint'''			)
  parser.add_argument(
      '''--config_file'''      ,      default=''''''      ,      type=_A      ,      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(_A      ,      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(_A      ,      _A      ,      _A			)

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

  # Store the config to file.
  print('''Saving config'''			)
  config.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__":
  main()

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






import math






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  math.log(2      ,      10			) * number
  a_		    =  round((log_number - int(_A			))      ,      15			)

  return difference






def 				UpperCAmelCase__  (							_A = 678_910			):






  """simple docstring"""


  a_		    =  90
  a_		    =  0

  a_		    =  math.log(1.23      ,      10			)
  a_		    =  math.log(1.24      ,      10			)
  a_		    =  0

  while position < number:
    a_		    =  log_difference(_A			)

    if difference >= upper_limit:
      power_iterator += 93

    elif difference < lower_limit:
      power_iterator += 196

    else:
      a_		    =  power_iterator
      power_iterator += 196
      position += 1

  return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")
