code: 
  
  
  
  import argparse
  import os
  import re
  import zipfile
  
  import torch
  
  from transformers import MegatronBertConfig
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case=0							)   -> Dict:
  
  
    if name is None:
      _A           =    None
    else:
      _A           =    '''.''' * max(0						,				spaces - 2							) + '''# {:''' + str(50 - spaces							) + '''s}'''
      _A           =    fmt.format(SCREAMING_SNAKE_CASE__							)
  
    # Print and recurse (if needed).
    if isinstance(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__							):
      if msg is not None:
        print(SCREAMING_SNAKE_CASE__							)
      for k in val.keys():
        recursive_print(SCREAMING_SNAKE_CASE__						,				val[k]						,				spaces + 2							)
    elif isinstance(SCREAMING_SNAKE_CASE__						,				torch.Tensor							):
      print(SCREAMING_SNAKE_CASE__						,				''':'''						,				val.size()							)
    else:
      print(SCREAMING_SNAKE_CASE__						,				''':'''						,				SCREAMING_SNAKE_CASE__							)
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Dict:
    _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(*SCREAMING_SNAKE_CASE__							)
      _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(*SCREAMING_SNAKE_CASE__							)
      _A           =    param.transpose(0						,				1							).contiguous()
    _A           =    param.view(*SCREAMING_SNAKE_CASE__							)
    return param
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> Tuple:
    _A           =    {}
  
    # old versions did not store training args
    _A           =    input_state_dict.get('''args'''						,				SCREAMING_SNAKE_CASE__							)
    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(SCREAMING_SNAKE_CASE__							)
  
      # 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(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				3						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__							)
        # 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(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				3						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__							)
        # 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__ (							)   -> int:
    _A           =    argparse.ArgumentParser()
    parser.add_argument('''--print-checkpoint-structure'''						,				action='''store_true'''							)
    parser.add_argument('''path_to_checkpoint'''						,				type=SCREAMING_SNAKE_CASE__						,				help='''Path to the ZIP file containing the checkpoint'''							)
    parser.add_argument(
        '''--config_file'''						,				default=''''''						,				type=SCREAMING_SNAKE_CASE__						,				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(SCREAMING_SNAKE_CASE__						,				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(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__							)
  
    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
      recursive_print(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__							)
  
    # Store the config to file.
    print('''Saving config'''							)
    config.save_pretrained(SCREAMING_SNAKE_CASE__							)
  
    # Store the state_dict to file.
    _A           =    os.path.join(SCREAMING_SNAKE_CASE__						,				'''pytorch_model.bin'''							)
    print(F'''Saving checkpoint to "{output_checkpoint_file}"'''							)
    torch.save(SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__							)
  
  
  ####################################################################################################
  
  if __name__ == "__main__":
      main()
  
  ####################################################################################################
style_context: 
  
  
  
  import platform
  from argparse import ArgumentParser
  
  import huggingface_hub
  import pandas
  import pyarrow
  
  from datasets import __version__ as version
  from datasets.commands import BaseDatasetsCLICommand
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> Optional[Any]:
    return EnvironmentCommand()
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   @staticmethod
   def lowercase_		(						a					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    parser.add_parser('''env'''	,    help='''Print relevant system environment info.'''					)
     download_parser.set_defaults(func=a					)
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    {
         '''`datasets` version''': version,
         '''Platform''': platform.platform(),
         '''Python version''': platform.python_version(),
         '''Huggingface_hub version''': huggingface_hub.__version__,
         '''PyArrow version''': pyarrow.__version__,
         '''Pandas version''': pandas.__version__,
     }
  
     print('''\nCopy-and-paste the text below in your GitHub issue.\n'''					)
     print(self.format_dict(a					)					)
  
     return info
  
  
  
   @staticmethod
   def lowercase_		(						a					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]					) + "\n"