



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()

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




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"