code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  # 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 (	SCREAMING_SNAKE_CASE  :     Optional[Any] , SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     Union[str, Any]=0		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    if name is None:
      a__			:      Any            =None
    else:
      a__			:      str            ="." * max(0 , spaces - 2		) + "# {:" + str(50 - spaces		) + "s}"
      a__			:      Tuple            =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   _A (	SCREAMING_SNAKE_CASE  :     Union[str, Any] , SCREAMING_SNAKE_CASE  :     Union[str, Any] , SCREAMING_SNAKE_CASE  :     Tuple , SCREAMING_SNAKE_CASE  :     Tuple , SCREAMING_SNAKE_CASE  :     Dict		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Any            =param.size()
    if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      a__			:      int            =(num_heads, hidden_size, num_splits) + input_shape[1:]
      a__			:      Any            =param.view(*lowercase__		)
      a__			:      int            =param.transpose(0 , 2		)
      a__			:      Tuple            =param.transpose(1 , 2		).contiguous()
    elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      a__			:      str            =(num_heads, num_splits, hidden_size) + input_shape[1:]
      a__			:      Tuple            =param.view(*lowercase__		)
      a__			:      str            =param.transpose(0 , 1		).contiguous()
    a__			:      List[Any]            =param.view(*lowercase__		)
    return param
  def   _A (	SCREAMING_SNAKE_CASE  :     Dict , SCREAMING_SNAKE_CASE  :     Optional[int] , SCREAMING_SNAKE_CASE  :     Optional[Any]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[int]            ={}
  
    # old versions did not store training args
    a__			:      str            =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__			:      str            =ds_args.tokenizer_type
      a__			:      Tuple            =ds_args.padded_vocab_size
      a__			:      Optional[int]            =ds_args.max_position_embeddings
      a__			:      Tuple            =ds_args.hidden_size
      a__			:      Optional[int]            =ds_args.num_layers
      a__			:      Optional[Any]            =ds_args.num_attention_heads
      a__			:      Any            =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__			:      str            =config.num_attention_heads
    # The hidden_size per head.
    a__			:      Optional[Any]            =config.hidden_size // heads
    # Megatron-LM checkpoint version
    if "checkpoint_version" in input_state_dict.keys():
      a__			:      str            =input_state_dict["checkpoint_version"]
    else:
      a__			:      List[Any]            =0.0
  
    # The model.
    a__			:      Tuple            =input_state_dict["model"]
    # The language model.
    a__			:      int            =model["language_model"]
    # The embeddings.
    a__			:      Optional[int]            =lm["embedding"]
  
    # The word embeddings.
    a__			:      Union[str, Any]            =embeddings["word_embeddings"]["weight"]
    # Truncate the embedding table to vocab_size rows.
    a__			:      Union[str, Any]            =word_embeddings[: config.vocab_size, :]
    # Store the word embeddings.
    a__			:      str            =word_embeddings
  
    # The position embeddings.
    a__			:      int            =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__			:      List[str]            =pos_embeddings
  
    # The token-type embeddings.
    a__			:      Union[str, Any]            =embeddings["tokentype_embeddings"]["weight"]
    # Store the position embeddings.
    a__			:      Optional[Any]            =tokentype_embeddings
  
    # The transformer.
    a__			:      Tuple            =lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]
  
    # The regex to extract layer names.
    a__			:      Tuple            =re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"		)
  
    # The simple map of names for "automated" rules.
    a__			:      Optional[int]            ={
        "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__			:      Optional[Any]            =None
  
    # Extract the layers.
    for key, val in transformer.items():
      # Match the name.
      a__			:      str            =layer_re.match(lowercase__		)
  
      # Stop if that's not a layer
      if m is None:
        break
  
      # The index of the layer.
      a__			:      int            =int(m.group(1		)		)
      # The name of the operation.
      a__			:      Union[str, Any]            =m.group(2		)
      # Is it a weight or a bias?
      a__			:      List[Any]            =m.group(3		)
  
      # The name of the layer.
      a__			:      Union[str, Any]            =f'''bert.encoder.layer.{layer_idx}'''
  
      # For layernorm(s), simply store the layer norm.
      if op_name.endswith("layernorm"		):
        a__			:      Union[str, Any]            ="attention.ln" if op_name.startswith("input"		) else "ln"
        a__			:      List[str]            =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__			:      Optional[int]            =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__			:      Optional[int]            =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__			:      Any            =attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
        a__			:      Any            =attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
        a__			:      Union[str, Any]            =attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]
  
        a__			:      int            =fix_query_key_value_ordering(lowercase__ , lowercase__ , 3 , lowercase__ , lowercase__		)
        # Split the bias.
        a__			:      Optional[int]            =out_val[0 * config.hidden_size : 1 * config.hidden_size]
        a__			:      List[str]            =out_val[1 * config.hidden_size : 2 * config.hidden_size]
        a__			:      Optional[Any]            =out_val[2 * config.hidden_size : 3 * config.hidden_size]
  
        # Store.
        a__			:      Union[str, Any]            =q
        a__			:      Any            =q_bias
        a__			:      int            =k
        a__			:      Optional[int]            =k_bias
        a__			:      Any            =v
        a__			:      Optional[Any]            =v_bias
  
        # Clear the stored tensor.
        a__			:      int            =None
  
      # Copy weights and biases as is.
      elif weight_or_bias in ["weight", "bias"]:
        a__			:      Dict            =megatron_to_transformers[op_name]
        a__			:      List[str]            =val
  
      # The final layernorm.
    a__			:      List[str]            =transformer["final_layernorm.weight"]
    a__			:      Optional[int]            =transformer["final_layernorm.bias"]
  
    # The pooler.
    a__			:      Tuple            =lm["pooler"]
  
    # Store the matrix and the bias.
    a__			:      Optional[Any]            =pooler["dense.weight"]
    a__			:      str            =pooler["dense.bias"]
  
    # The LM head from Megatron (for RACE).
    a__			:      Dict            =model["lm_head"]
  
    # The transform matrix.
    a__			:      Any            =lm_head["dense.weight"]
    a__			:      Optional[int]            =lm_head["dense.bias"]
  
    # The transform LN.
    a__			:      int            =lm_head["layernorm.weight"]
    a__			:      Optional[Any]            =lm_head["layernorm.bias"]
  
    # For the decoder, we replicate the weights.
    a__			:      Any            =word_embeddings
    a__			:      Dict            =lm_head["bias"]
  
    # The classifier from Megatron (for MLNI).
    a__			:      Tuple            =model["binary_head"]
  
    # Store the classifier.
    a__			:      Tuple            =binary_head["weight"]
    a__			:      Dict            =binary_head["bias"]
  
    # It should be done!
    return output_state_dict
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Union[str, Any]            =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__			:      List[str]            =parser.parse_args()
  
    # Extract the basename.
    a__			:      Tuple            =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__			:      List[Any]            =torch.load(lowercase__ , map_location="cpu"		)
    else:
      a__			:      Optional[Any]            =torch.load(args.path_to_checkpoint , map_location="cpu"		)
  
    if args.config_file == "":
      # Default config of megatron-bert 345m
      a__			:      Union[str, Any]            =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__			:      Any            =input_state_dict["model"]["lm_head"]["bias"].numel()
    else:
      a__			:      Dict            =MegatronBertConfig.from_json_file(args.config_file		)
  
    # Convert.
    print("Converting"		)
    a__			:      Dict            =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__			:      Union[str, Any]            =os.path.join(lowercase__ , "pytorch_model.bin"		)
    print(f'''Saving checkpoint to "{output_checkpoint_file}"'''		)
    torch.save(lowercase__ , lowercase__		)
  
  
  ####################################################################################################
  
  if __name__ == "__main__":
       main()
  
  ####################################################################################################
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def   _A (	SCREAMING_SNAKE_CASE  :     int		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		) or number < 0:
      raise ValueError("Input must be a non-negative integer"		)
  
    a__			:      List[str]            =number & ~(number - 1)
    a__			:      Optional[int]            =0
    while intermediate:
      intermediate >>= 1
      index += 1
    return index - 1
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod(verbose=True)
  
  
  
  
  
  
  