






'''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_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 							lowercase_				(    __A :       Union[str, Any]					,      __A :       Dict					,      __A :       List[str]=0			)       ->      Union[str, Any]:



 """simple docstring"""




 if name is None:
  lowercase					:   Optional[Any]				  =None
 else:
  lowercase					:   int				  ='''.''' * max(0					,      spaces - 2			) + '''# {:''' + str(5_0 - spaces			) + '''s}'''
  lowercase					:   Optional[int]				  =fmt.format(__A			)

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


def 							lowercase_				(    __A :       Optional[Any]					,      __A :       Optional[int]					,      __A :       Optional[Any]					,      __A :       Optional[int]					,      __A :       Union[str, Any]			)       ->      Optional[Any]:



 """simple docstring"""




 lowercase					:   Optional[int]				  =param.size()
 if checkpoint_version == 1.0:
  # version 1.0 stores [num_heads * hidden_size * num_splits, :]
  lowercase					:   int				  =(num_heads, hidden_size, num_splits) + input_shape[1:]
  lowercase					:   int				  =param.view(*__A			)
  lowercase					:   Optional[Any]				  =param.transpose(0					,      2			)
  lowercase					:   Union[str, Any]				  =param.transpose(1					,      2			).contiguous()
 elif checkpoint_version >= 2.0:
  # other versions store [num_heads * num_splits * hidden_size, :]
  lowercase					:   List[Any]				  =(num_heads, num_splits, hidden_size) + input_shape[1:]
  lowercase					:   Tuple				  =param.view(*__A			)
  lowercase					:   Any				  =param.transpose(0					,      1			).contiguous()
 lowercase					:   str				  =param.view(*__A			)
 return param


def 							lowercase_				(    __A :       Dict					,      __A :       Union[str, Any]					,      __A :       List[Any]			)       ->      Optional[Any]:



 """simple docstring"""




 lowercase					:   Union[str, Any]				  ={}

 # old versions did not store training args
 lowercase					:   int				  =input_state_dict.get('''args'''					,      __A			)
 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))

  lowercase					:   Tuple				  =ds_args.padded_vocab_size
  lowercase					:   Any				  =ds_args.max_position_embeddings
  lowercase					:   Dict				  =ds_args.hidden_size
  lowercase					:   Any				  =ds_args.num_layers
  lowercase					:   Dict				  =ds_args.num_attention_heads
  lowercase					:   int				  =ds_args.ffn_hidden_size
  # pprint(config)

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

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

 # The word embeddings.
 lowercase					:   Optional[Any]				  =embeddings['''word_embeddings''']['''weight''']
 # Truncate the embedding table to vocab_size rows.
 lowercase					:   Dict				  =word_embeddings[: config.vocab_size, :]
 lowercase					:   str				  =word_embeddings

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

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

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

 # The simple map of names for "automated" rules.
 lowercase					:   List[str]				  ={
     '''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.
  lowercase					:   Optional[Any]				  =layer_re.match(__A			)

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

  # The index of the layer.
  lowercase					:   List[Any]				  =int(m.group(1			)			)
  # The name of the operation.
  lowercase					:   Dict				  =m.group(2			)
  # Is it a weight or a bias?
  lowercase					:   Any				  =m.group(3			)

  # The name of the layer.
  lowercase					:   int				  =F'transformer.h.{layer_idx}'

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

   # Insert a "dummy" tensor for masked_bias.
   lowercase					:   Tuple				  =torch.tensor(-1E4					,      dtype=torch.floataa			)
   lowercase					:   int				  =masked_bias

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

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

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

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

 # The final layernorm.
 lowercase					:   List[Any]				  =transformer['''final_layernorm.weight''']
 lowercase					:   str				  =transformer['''final_layernorm.bias''']

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

 # It should be done!
 return output_state_dict


def 							lowercase_				(    )       ->      Union[str, Any]:



 """simple docstring"""




 lowercase					:   Optional[Any]				  =argparse.ArgumentParser()
 parser.add_argument('''--print-checkpoint-structure'''					,      action='''store_true'''			)
 parser.add_argument(
     '''path_to_checkpoint'''					,      type=__A					,      help='''Path to the checkpoint file (.zip archive or direct .pt file)'''					,      )
 parser.add_argument(
     '''--config_file'''					,      default=''''''					,      type=__A					,      help='''An optional config json file describing the pre-trained model.'''					,      )
 lowercase					:   Dict				  =parser.parse_args()

 # Extract the basename.
 lowercase					:   Dict				  =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:
    lowercase					:   Optional[Any]				  =torch.load(__A					,      map_location='''cpu'''			)
 else:
  lowercase					:   str				  =torch.load(args.path_to_checkpoint					,      map_location='''cpu'''			)

 lowercase					:   List[Any]				  =input_state_dict.get('''args'''					,      __A			)

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

  # Spell out all parameters in case the defaults change.
  lowercase					:   List[str]				  =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=__A					,      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=__A					,      summary_activation=__A					,      summary_proj_to_labels=__A					,      summary_first_dropout=0.1					,      scale_attn_weights=__A					,      use_cache=__A					,      bos_token_id=5_0_2_5_6					,      eos_token_id=5_0_2_5_6					,      )
 else:
  lowercase					:   List[Any]				  =GPTaConfig.from_json_file(args.config_file			)

 lowercase					:   Any				  =['''GPT2LMHeadModel''']

 # Convert.
 print('''Converting'''			)
 lowercase					:   Dict				  =convert_megatron_checkpoint(__A					,      __A					,      __A			)

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

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

 lowercase					:   Optional[int]				  =AutoTokenizer.from_pretrained(__A			)
 lowercase					:   Any				  =type(__A			).__name__
 lowercase					:   Union[str, Any]				  =tokenizer_class

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

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

 # Store the state_dict to file.
 lowercase					:   Any				  =os.path.join(__A					,      '''pytorch_model.bin'''			)
 print(F'Saving checkpoint to "{output_checkpoint_file}"'			)
 torch.save(__A					,      __A			)


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

if __name__ == "__main__":
      main()

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











'''simple docstring'''



def 							lowercase_				(    __A :       int = 6_0_0_8_5_1_4_7_5_1_4_3			)       ->      int:



 """simple docstring"""




 try:
  lowercase					:   Any				  =int(__A			)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''			)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''			)
 lowercase					:   Optional[Any]				  =1
 lowercase					:   Dict				  =2
 while i * i <= n:
  while n % i == 0:
   lowercase					:   Optional[int]				  =i
   n //= i
  i += 1
 if n > 1:
  lowercase					:   Dict				  =n
 return int(__A			)


if __name__ == "__main__":
      print(f"""{solution() = }""")



