

# 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_gpt2/convert_megatron_gpt2_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 AutoTokenizer, GPTaConfig



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=0					)							->     str:



  """simple docstring"""






  if name is None:
    _snake_case						=							None
  else:
    _snake_case						=							'''.''' * max(0			,	spaces - 2					) + '''# {:''' + str(50 - spaces					) + '''s}'''
    _snake_case						=							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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Any:



  """simple docstring"""






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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     int:



  """simple docstring"""






  _snake_case						=							{}

  # old versions did not store training args
  _snake_case						=							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))

    _snake_case						=							ds_args.padded_vocab_size
    _snake_case						=							ds_args.max_position_embeddings
    _snake_case						=							ds_args.hidden_size
    _snake_case						=							ds_args.num_layers
    _snake_case						=							ds_args.num_attention_heads
    _snake_case						=							ds_args.ffn_hidden_size
    # pprint(config)

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

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

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

  # The position embeddings.
  _snake_case						=							embeddings['''position_embeddings''']['''weight''']
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  _snake_case						=							pos_embeddings.size(0					)
  if n_positions != config.n_positions:
    raise ValueError(
        F"""pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"""					)
  # Store the position embeddings.
  _snake_case						=							pos_embeddings

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

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

  # The simple map of names for "automated" rules.
  _snake_case						=							{
      '''attention.dense''': '''.attn.c_proj.''',
      '''self_attention.dense''': '''.attn.c_proj.''',
      '''mlp.dense_h_to_4h''': '''.mlp.c_fc.''',
      '''mlp.dense_4h_to_h''': '''.mlp.c_proj.''',
  }

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

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

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

    # The name of the layer.
    _snake_case						=							F"""transformer.h.{layer_idx}"""

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('''layernorm'''					):
      _snake_case						=							'''ln_1''' if op_name.startswith('''input'''					) else '''ln_2'''
      _snake_case						=							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":
      # Insert a tensor of 1x1xDxD bias.
      _snake_case						=							torch.tril(torch.ones((n_positions, n_positions)			,	dtype=torch.floataa					)					).view(
          1			,	1			,	_UpperCamelCase			,	_UpperCamelCase					)
      _snake_case						=							causal_mask

      # Insert a "dummy" tensor for masked_bias.
      _snake_case						=							torch.tensor(-1E4			,	dtype=torch.floataa					)
      _snake_case						=							masked_bias

      _snake_case						=							fix_query_key_value_ordering(_UpperCamelCase			,	_UpperCamelCase			,	3			,	_UpperCamelCase			,	_UpperCamelCase					)
      # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
      _snake_case						=							out_val.transpose(0			,	1					).contiguous()
      # Store.
      _snake_case						=							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":
      _snake_case						=							fix_query_key_value_ordering(_UpperCamelCase			,	_UpperCamelCase			,	3			,	_UpperCamelCase			,	_UpperCamelCase					)
      # Store. No change of shape.
      _snake_case						=							out_val

    # Transpose the weights.
    elif weight_or_bias == "weight":
      _snake_case						=							megatron_to_transformers[op_name]
      _snake_case						=							val.transpose(0			,	1					)

    # Copy the bias.
    elif weight_or_bias == "bias":
      _snake_case						=							megatron_to_transformers[op_name]
      _snake_case						=							val

    # DEBUG.
  assert config.n_layer == layer_idx + 1

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

  # For LM head, transformers' wants the matrix to weight embeddings.
  _snake_case						=							word_embeddings

  # It should be done!
  return output_state_dict



def 				snake_case_()							->     List[str]:



  """simple docstring"""






  _snake_case						=							argparse.ArgumentParser()
  parser.add_argument('''--print-checkpoint-structure'''			,	action='''store_true'''					)
  parser.add_argument(
      '''path_to_checkpoint'''			,	type=_UpperCamelCase			,	help='''Path to the checkpoint file (.zip archive or direct .pt file)'''			,	)
  parser.add_argument(
      '''--config_file'''			,	default=''''''			,	type=_UpperCamelCase			,	help='''An optional config json file describing the pre-trained model.'''			,	)
  _snake_case						=							parser.parse_args()

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

  _snake_case						=							input_state_dict.get('''args'''			,	_UpperCamelCase					)

  # Read the config, or default to the model released by NVIDIA.
  if args.config_file == "":
    if ds_args is not None:
      if ds_args.bias_gelu_fusion:
        _snake_case						=							'''gelu_fast'''
      elif ds_args.openai_gelu:
        _snake_case						=							'''gelu_new'''
      else:
        _snake_case						=							'''gelu'''
    else:
      # in the very early days this used to be "gelu_new"
      _snake_case						=							'''gelu_new'''

    # Spell out all parameters in case the defaults change.
    _snake_case						=							GPTaConfig(
        vocab_size=50_257			,	n_positions=1_024			,	n_embd=1_024			,	n_layer=24			,	n_head=16			,	n_inner=4_096			,	activation_function=_UpperCamelCase			,	resid_pdrop=0.1			,	embd_pdrop=0.1			,	attn_pdrop=0.1			,	layer_norm_epsilon=1E-5			,	initializer_range=0.02			,	summary_type='''cls_index'''			,	summary_use_proj=_UpperCamelCase			,	summary_activation=_UpperCamelCase			,	summary_proj_to_labels=_UpperCamelCase			,	summary_first_dropout=0.1			,	scale_attn_weights=_UpperCamelCase			,	use_cache=_UpperCamelCase			,	bos_token_id=50_256			,	eos_token_id=50_256			,	)
  else:
    _snake_case						=							GPTaConfig.from_json_file(args.config_file					)

  _snake_case						=							['''GPT2LMHeadModel''']

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

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

  # Add tokenizer class info to config
  # see https://github.com/huggingface/transformers/issues/13906)
  if ds_args is not None:
    _snake_case						=							ds_args.tokenizer_type
    if tokenizer_type == "GPT2BPETokenizer":
      _snake_case						=							'''gpt2'''
    elif tokenizer_type == "PretrainedFromHF":
      _snake_case						=							ds_args.tokenizer_name_or_path
    else:
      raise ValueError(F"""Unrecognized tokenizer_type {tokenizer_type}"""					)
  else:
    _snake_case						=							'''gpt2'''

  _snake_case						=							AutoTokenizer.from_pretrained(_UpperCamelCase					)
  _snake_case						=							type(_UpperCamelCase					).__name__
  _snake_case						=							tokenizer_class

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

  # Save tokenizer based on args
  print(F"""Adding {tokenizer_class} tokenizer files"""					)
  tokenizer.save_pretrained(_UpperCamelCase					)

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


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

if __name__ == "__main__":
 main()

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




from math import factorial



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     int:



  """simple docstring"""






  if n < k or k < 0:
    raise ValueError('''Please enter positive integers for n and k where n >= k'''					)
  return factorial(_UpperCamelCase					) // (factorial(_UpperCamelCase					) * factorial(n - k					))


if __name__ == "__main__":
 print(
     '''The number of five-card hands possible from a standard''',
     f'''fifty-two card deck is: {combinations(52, 5)}\n''',
 )

 print(
     '''If a class of 40 students must be arranged into groups of''',
     f'''4 for group projects, there are {combinations(40, 4)} ways''',
     '''to arrange them.\n''',
 )

 print(
     '''If 10 teams are competing in a Formula One race, there''',
     f'''are {combinations(10, 3)} ways that first, second and''',
     '''third place can be awarded.''',
 )

