
# 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    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				Dict=0  ):
 # Format the message.
 if name is None:
  __a :  List[str]						=  None
 else:
  __a :  Union[str, Any]						=  '''.''' * max(0  ,   spaces - 2  ) + '''# {:''' + str(5_0 - spaces  ) + '''s}'''
  __a :  Any						=  fmt.format(lowerCAmelCase_  )

 # Print and recurse (if needed).
 if isinstance(lowerCAmelCase_  ,   lowerCAmelCase_  ):
  if msg is not None:
   print(lowerCAmelCase_  )
  for k in val.keys():
   recursive_print(lowerCAmelCase_  ,   val[k]  ,   spaces + 2  )
 elif isinstance(lowerCAmelCase_  ,   torch.Tensor  ):
  print(lowerCAmelCase_  ,   ''':'''  ,   val.size()  )
 else:
  print(lowerCAmelCase_  ,   ''':'''  ,   lowerCAmelCase_  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				Tuple  ):
 # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
 # for compatibility with later versions of NVIDIA Megatron-LM.
 # The inverse operation is performed inside Megatron-LM to read checkpoints:
 # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
 # If param is the weight tensor of the self-attention block, the returned tensor
 # will have to be transposed one more time to be read by HuggingFace GPT2.
 __a :  int						=  param.size()
 if checkpoint_version == 1.0:
  # version 1.0 stores [num_heads * hidden_size * num_splits, :]
  __a :  str						=  (num_heads, hidden_size, num_splits) + input_shape[1:]
  __a :  List[Any]						=  param.view(*lowerCAmelCase_  )
  __a :  Any						=  param.transpose(0  ,   2  )
  __a :  Any						=  param.transpose(1  ,   2  ).contiguous()
 elif checkpoint_version >= 2.0:
  # other versions store [num_heads * num_splits * hidden_size, :]
  __a :  Tuple						=  (num_heads, num_splits, hidden_size) + input_shape[1:]
  __a :  int						=  param.view(*lowerCAmelCase_  )
  __a :  str						=  param.transpose(0  ,   1  ).contiguous()
 __a :  Union[str, Any]						=  param.view(*lowerCAmelCase_  )
 return param




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				List[str]  ):
 # The converted output model.
 __a :  Optional[Any]						=  {}

 # old versions did not store training args
 __a :  Any						=  input_state_dict.get('''args'''  ,   lowerCAmelCase_  )
 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.padded_vocab_size
  __a :  Any						=  ds_args.max_position_embeddings
  __a :  Any						=  ds_args.hidden_size
  __a :  str						=  ds_args.num_layers
  __a :  Any						=  ds_args.num_attention_heads
  __a :  Any						=  ds_args.ffn_hidden_size
  # pprint(config)

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

 # The model.
 __a :  Tuple						=  input_state_dict['''model''']
 # The language model.
 __a :  Union[str, Any]						=  model['''language_model''']
 # The embeddings.
 __a :  Dict						=  lm['''embedding''']

 # The word embeddings.
 __a :  Optional[Any]						=  embeddings['''word_embeddings''']['''weight''']
 # Truncate the embedding table to vocab_size rows.
 __a :  Optional[int]						=  word_embeddings[: config.vocab_size, :]
 __a :  Tuple						=  word_embeddings

 # The position embeddings.
 __a :  Union[str, Any]						=  embeddings['''position_embeddings''']['''weight''']
 # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
 __a :  List[Any]						=  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.
 __a :  Optional[int]						=  pos_embeddings

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

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

 # The simple map of names for "automated" rules.
 __a :  Optional[Any]						=  {
     '''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.
  __a :  Union[str, Any]						=  layer_re.match(lowerCAmelCase_  )

  # 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 :  int						=  m.group(2  )
  # Is it a weight or a bias?
  __a :  Any						=  m.group(3  )

  # The name of the layer.
  __a :  List[str]						=  f"transformer.h.{layer_idx}"

  # For layernorm(s), simply store the layer norm.
  if op_name.endswith('''layernorm'''  ):
   __a :  Union[str, Any]						=  '''ln_1''' if op_name.startswith('''input'''  ) else '''ln_2'''
   __a :  Dict						=  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.
   __a :  Union[str, Any]						=  torch.tril(torch.ones((n_positions, n_positions)  ,   dtype=torch.floataa  )  ).view(
       1  ,   1  ,   lowerCAmelCase_  ,   lowerCAmelCase_  )
   __a :  List[Any]						=  causal_mask

   # Insert a "dummy" tensor for masked_bias.
   __a :  Any						=  torch.tensor(-1e4  ,   dtype=torch.floataa  )
   __a :  Any						=  masked_bias

   __a :  Union[str, Any]						=  fix_query_key_value_ordering(lowerCAmelCase_  ,   lowerCAmelCase_  ,   3  ,   lowerCAmelCase_  ,   lowerCAmelCase_  )
   # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
   __a :  List[str]						=  out_val.transpose(0  ,   1  ).contiguous()
   # Store.
   __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":
   __a :  Any						=  fix_query_key_value_ordering(lowerCAmelCase_  ,   lowerCAmelCase_  ,   3  ,   lowerCAmelCase_  ,   lowerCAmelCase_  )
   # Store. No change of shape.
   __a :  Optional[Any]						=  out_val

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

  # Copy the bias.
  elif weight_or_bias == "bias":
   __a :  Any						=  megatron_to_transformers[op_name]
   __a :  List[str]						=  val

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

 # The final layernorm.
 __a :  Dict						=  transformer['''final_layernorm.weight''']
 __a :  Dict						=  transformer['''final_layernorm.bias''']

 # For LM head, transformers' wants the matrix to weight embeddings.
 __a :  Union[str, Any]						=  word_embeddings

 # It should be done!
 return output_state_dict




def    __UpperCamelCase     (				):
 # Create the argument parser.
 __a :  Any						=  argparse.ArgumentParser()
 parser.add_argument('''--print-checkpoint-structure'''  ,   action='''store_true'''  )
 parser.add_argument(
     '''path_to_checkpoint'''  ,   type=lowerCAmelCase_  ,   help='''Path to the checkpoint file (.zip archive or direct .pt file)'''  ,   )
 parser.add_argument(
     '''--config_file'''  ,   default=''''''  ,   type=lowerCAmelCase_  ,   help='''An optional config json file describing the pre-trained model.'''  ,   )
 __a :  str						=  parser.parse_args()

 # Extract the basename.
 __a :  Any						=  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 :  Optional[int]						=  torch.load(lowerCAmelCase_  ,   map_location='''cpu'''  )
 else:
  __a :  Dict						=  torch.load(args.path_to_checkpoint  ,   map_location='''cpu'''  )

 __a :  List[Any]						=  input_state_dict.get('''args'''  ,   lowerCAmelCase_  )

 # 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:
    __a :  Optional[int]						=  '''gelu_fast'''
   elif ds_args.openai_gelu:
    __a :  Dict						=  '''gelu_new'''
   else:
    __a :  Any						=  '''gelu'''
  else:
   # in the very early days this used to be "gelu_new"
   __a :  Union[str, Any]						=  '''gelu_new'''

  # Spell out all parameters in case the defaults change.
  __a :  Optional[int]						=  GPTaConfig(
      vocab_size=5_0_2_5_7  ,   n_positions=1_0_2_4  ,   n_embd=1_0_2_4  ,   n_layer=2_4  ,   n_head=1_6  ,   n_inner=4_0_9_6  ,   activation_function=lowerCAmelCase_  ,   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=lowerCAmelCase_  ,   summary_activation=lowerCAmelCase_  ,   summary_proj_to_labels=lowerCAmelCase_  ,   summary_first_dropout=0.1  ,   scale_attn_weights=lowerCAmelCase_  ,   use_cache=lowerCAmelCase_  ,   bos_token_id=5_0_2_5_6  ,   eos_token_id=5_0_2_5_6  ,   )
 else:
  __a :  Any						=  GPTaConfig.from_json_file(args.config_file  )

 __a :  Tuple						=  ['''GPT2LMHeadModel''']

 # Convert.
 print('''Converting'''  )
 __a :  List[str]						=  convert_megatron_checkpoint(lowerCAmelCase_  ,   lowerCAmelCase_  ,   lowerCAmelCase_  )

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

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

 __a :  Optional[Any]						=  AutoTokenizer.from_pretrained(lowerCAmelCase_  )
 __a :  Any						=  type(lowerCAmelCase_  ).__name__
 __a :  Tuple						=  tokenizer_class

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

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

 # Store the state_dict to file.
 __a :  List[str]						=  os.path.join(lowerCAmelCase_  ,   '''pytorch_model.bin'''  )
 print(f"Saving checkpoint to \"{output_checkpoint_file}\""  )
 torch.save(lowerCAmelCase_  ,   lowerCAmelCase_  )


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

if __name__ == "__main__":
      main()

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







import re

from filelock import FileLock


try:
     import nltk

     _snake_case  :    Any						    =  True
except (ImportError, ModuleNotFoundError):
     _snake_case  :    Union[str, Any]						    =  False

if NLTK_AVAILABLE:
     with FileLock('.lock') as lock:
          nltk.download('punkt', quiet=True)

def   a_    ( lowerCAmelCase_      :			str	):
   re.sub('<n>',	'',	lowerCAmelCase_	)  # remove pegasus newline char
   assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
   return "\n".join(nltk.sent_tokenize(lowerCAmelCase_	)	)

