




# 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 		SCREAMING_SNAKE_CASE__   (       lowercase		,lowercase		,lowercase=0	)    ->  List[str]:


   if name is None:
      snake_case :							List[Any]           =			None
   else:
      snake_case :							int           =			""".""" * max(0		,spaces - 2	) + """# {:""" + str(50 - spaces	) + """s}"""
      snake_case :							Tuple           =			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 		SCREAMING_SNAKE_CASE__   (       lowercase		,lowercase		,lowercase		,lowercase		,lowercase	)    ->  Optional[Any]:
   snake_case :							Optional[int]           =			param.size()
   if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      snake_case :							Union[str, Any]           =			(num_heads, hidden_size, num_splits) + input_shape[1:]
      snake_case :							List[Any]           =			param.view(*__lowerCAmelCase	)
      snake_case :							List[str]           =			param.transpose(0		,2	)
      snake_case :							Optional[Any]           =			param.transpose(1		,2	).contiguous()
   elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      snake_case :							int           =			(num_heads, num_splits, hidden_size) + input_shape[1:]
      snake_case :							Optional[Any]           =			param.view(*__lowerCAmelCase	)
      snake_case :							Any           =			param.transpose(0		,1	).contiguous()
   snake_case :							Tuple           =			param.view(*__lowerCAmelCase	)
   return param




def 		SCREAMING_SNAKE_CASE__   (       lowercase		,lowercase		,lowercase	)    ->  Union[str, Any]:
   snake_case :							Tuple           =			{}

   # old versions did not store training args
   snake_case :							Union[str, 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))

      snake_case :							Dict           =			ds_args.padded_vocab_size
      snake_case :							Optional[int]           =			ds_args.max_position_embeddings
      snake_case :							str           =			ds_args.hidden_size
      snake_case :							List[Any]           =			ds_args.num_layers
      snake_case :							List[Any]           =			ds_args.num_attention_heads
      snake_case :							Optional[Any]           =			ds_args.ffn_hidden_size
      # pprint(config)

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

   # The model.
   snake_case :							List[str]           =			input_state_dict["""model"""]
   # The language model.
   snake_case :							Any           =			model["""language_model"""]
   # The embeddings.
   snake_case :							Tuple           =			lm["""embedding"""]

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

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

   # The transformer.
   snake_case :							Tuple           =			lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

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

   # The simple map of names for "automated" rules.
   snake_case :							Tuple           =			{
       """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 :							Tuple           =			layer_re.match(__lowerCAmelCase	)

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

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

      # The name of the layer.
      snake_case :							int           =			f"""transformer.h.{layer_idx}"""

      # For layernorm(s), simply store the layer norm.
      if op_name.endswith("""layernorm"""	):
         snake_case :							Any           =			"""ln_1""" if op_name.startswith("""input"""	) else """ln_2"""
         snake_case :							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.
         snake_case :							Any           =			torch.tril(torch.ones((n_positions, n_positions)		,dtype=torch.floataa	)	).view(
             1		,1		,__lowerCAmelCase		,__lowerCAmelCase	)
         snake_case :							Any           =			causal_mask

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

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

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

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

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

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

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

   # It should be done!
   return output_state_dict




def 		SCREAMING_SNAKE_CASE__   (       )    ->  int:
   snake_case :							Dict           =			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."""		,)
   snake_case :							str           =			parser.parse_args()

   # Extract the basename.
   snake_case :							Optional[int]           =			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 :							Tuple           =			torch.load(__lowerCAmelCase		,map_location="""cpu"""	)
   else:
      snake_case :							Optional[Any]           =			torch.load(args.path_to_checkpoint		,map_location="""cpu"""	)

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

      # Spell out all parameters in case the defaults change.
      snake_case :							Optional[Any]           =			GPTaConfig(
          vocab_size=50257		,n_positions=1024		,n_embd=1024		,n_layer=24		,n_head=16		,n_inner=4096		,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=50256		,eos_token_id=50256		,)
   else:
      snake_case :							str           =			GPTaConfig.from_json_file(args.config_file	)

   snake_case :							str           =			["""GPT2LMHeadModel"""]

   # Convert.
   print("""Converting"""	)
   snake_case :							Optional[int]           =			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:
      snake_case :							List[str]           =			ds_args.tokenizer_type
      if tokenizer_type == "GPT2BPETokenizer":
         snake_case :							Optional[int]           =			"""gpt2"""
      elif tokenizer_type == "PretrainedFromHF":
         snake_case :							str           =			ds_args.tokenizer_name_or_path
      else:
         raise ValueError(f"""Unrecognized tokenizer_type {tokenizer_type}"""	)
   else:
      snake_case :							str           =			"""gpt2"""

   snake_case :							int           =			AutoTokenizer.from_pretrained(__lowerCAmelCase	)
   snake_case :							Any           =			type(__lowerCAmelCase	).__name__
   snake_case :							Optional[Any]           =			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.
   snake_case :							Dict           =			os.path.join(__lowerCAmelCase		,"""pytorch_model.bin"""	)
   print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""	)
   torch.save(__lowerCAmelCase		,__lowerCAmelCase	)


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

if __name__ == "__main__":
       main()

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







from math import sqrt






def   lowerCamelCase__							(							__lowerCAmelCase    :    int							):




			"""simple docstring"""


			lowerCAmelCase_         =      0
			for i in range(1       ,				int(sqrt(__lowerCAmelCase							) + 1							)							):
						if n % i == 0 and i != sqrt(__lowerCAmelCase							):
									total += i + n // i
						elif i == sqrt(__lowerCAmelCase							):
									total += i
			return total - n






def   lowerCamelCase__							(							__lowerCAmelCase    :    int = 10000							):




			"""simple docstring"""


			lowerCAmelCase_         =      sum(
			    i
			    for i in range(1       ,				__lowerCAmelCase							)
			    if sum_of_divisors(sum_of_divisors(__lowerCAmelCase							)							) == i and sum_of_divisors(__lowerCAmelCase							) != i							)
			return total


if __name__ == "__main__":
			print(solution(int(str(input()).strip())))



