'''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_bert/convert_megatron_bert_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 MegatronBertConfig

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=0				):
     # Format the message.
     if name is None:
          lowercase_       :							Dict  					=       None
     else:
          lowercase_       :							Optional[int]  					=       '.' * max(0	,							spaces - 2				) + '# {:' + str(50 - spaces				) + 's}'
          lowercase_       :							Tuple  					=       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 (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     # 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 BERT.
     lowercase_       :							Union[str, Any]  					=       param.size()
     if checkpoint_version == 1.0:
          # version 1.0 stores [num_heads * hidden_size * num_splits, :]
          lowercase_       :							Union[str, Any]  					=       (num_heads, hidden_size, num_splits) + input_shape[1:]
          lowercase_       :							str  					=       param.view(*SCREAMING_SNAKE_CASE_				)
          lowercase_       :							Optional[Any]  					=       param.transpose(0	,							2				)
          lowercase_       :							Optional[int]  					=       param.transpose(1	,							2				).contiguous()
     elif checkpoint_version >= 2.0:
          # other versions store [num_heads * num_splits * hidden_size, :]
          lowercase_       :							Union[str, Any]  					=       (num_heads, num_splits, hidden_size) + input_shape[1:]
          lowercase_       :							List[Any]  					=       param.view(*SCREAMING_SNAKE_CASE_				)
          lowercase_       :							str  					=       param.transpose(0	,							1				).contiguous()
     lowercase_       :							int  					=       param.view(*SCREAMING_SNAKE_CASE_				)
     return param

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     # The converted output model.
     lowercase_       :							int  					=       {}

     # old versions did not store training args
     lowercase_       :							Tuple  					=       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))

          lowercase_       :							Optional[Any]  					=       ds_args.tokenizer_type
          lowercase_       :							Union[str, Any]  					=       ds_args.padded_vocab_size
          lowercase_       :							Dict  					=       ds_args.max_position_embeddings
          lowercase_       :							Optional[int]  					=       ds_args.hidden_size
          lowercase_       :							List[str]  					=       ds_args.num_layers
          lowercase_       :							Any  					=       ds_args.num_attention_heads
          lowercase_       :							Optional[int]  					=       ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
          # pprint(config)

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

     # The model.
     lowercase_       :							Any  					=       input_state_dict['model']
     # The language model.
     lowercase_       :							Optional[int]  					=       model['language_model']
     # The embeddings.
     lowercase_       :							List[Any]  					=       lm['embedding']

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

     # The position embeddings.
     lowercase_       :							Union[str, Any]  					=       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.
     lowercase_       :							str  					=       pos_embeddings

     # The token-type embeddings.
     lowercase_       :							Optional[Any]  					=       embeddings['tokentype_embeddings']['weight']
     # Store the position embeddings.
     lowercase_       :							Union[str, Any]  					=       tokentype_embeddings

     # The transformer.
     lowercase_       :							List[Any]  					=       lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

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

     # The simple map of names for "automated" rules.
     lowercase_       :							str  					=       {
         '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.
     lowercase_       :							str  					=       None

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

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

          # The index of the layer.
          lowercase_       :							Tuple  					=       int(m.group(1				)				)
          # The name of the operation.
          lowercase_       :							Tuple  					=       m.group(2				)
          # Is it a weight or a bias?
          lowercase_       :							Union[str, Any]  					=       m.group(3				)

          # The name of the layer.
          lowercase_       :							Dict  					=       f'''bert.encoder.layer.{layer_idx}'''

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('layernorm'				):
               lowercase_       :							str  					=       'attention.ln' if op_name.startswith('input'				) else 'ln'
               lowercase_       :							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":
               # Make sure the QKV pointer is nil.
               assert attention_qkv_weight is None, ""

               lowercase_       :							Any  					=       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.
               lowercase_       :							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":
               # 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.
               lowercase_       :							Any  					=       attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               lowercase_       :							Union[str, Any]  					=       attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
               lowercase_       :							Union[str, Any]  					=       attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

               lowercase_       :							List[str]  					=       fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							3	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)
               # Split the bias.
               lowercase_       :							Union[str, Any]  					=       out_val[0 * config.hidden_size : 1 * config.hidden_size]
               lowercase_       :							Optional[int]  					=       out_val[1 * config.hidden_size : 2 * config.hidden_size]
               lowercase_       :							Dict  					=       out_val[2 * config.hidden_size : 3 * config.hidden_size]

               # Store.
               lowercase_       :							List[str]  					=       q
               lowercase_       :							Tuple  					=       q_bias
               lowercase_       :							Any  					=       k
               lowercase_       :							Optional[Any]  					=       k_bias
               lowercase_       :							Any  					=       v
               lowercase_       :							Union[str, Any]  					=       v_bias

               # Clear the stored tensor.
               lowercase_       :							Optional[Any]  					=       None

          # Copy weights and biases as is.
          elif weight_or_bias in ["weight", "bias"]:
               lowercase_       :							List[Any]  					=       megatron_to_transformers[op_name]
               lowercase_       :							Dict  					=       val

    # The final layernorm.
     lowercase_       :							Union[str, Any]  					=       transformer['final_layernorm.weight']
     lowercase_       :							Any  					=       transformer['final_layernorm.bias']

     # The pooler.
     lowercase_       :							Tuple  					=       lm['pooler']

     # Store the matrix and the bias.
     lowercase_       :							Union[str, Any]  					=       pooler['dense.weight']
     lowercase_       :							int  					=       pooler['dense.bias']

     # The LM head from Megatron (for RACE).
     lowercase_       :							Union[str, Any]  					=       model['lm_head']

     # The transform matrix.
     lowercase_       :							Any  					=       lm_head['dense.weight']
     lowercase_       :							Dict  					=       lm_head['dense.bias']

     # The transform LN.
     lowercase_       :							Optional[int]  					=       lm_head['layernorm.weight']
     lowercase_       :							Union[str, Any]  					=       lm_head['layernorm.bias']

     # For the decoder, we replicate the weights.
     lowercase_       :							Optional[int]  					=       word_embeddings
     lowercase_       :							List[str]  					=       lm_head['bias']

     # The classifier from Megatron (for MLNI).
     lowercase_       :							List[Any]  					=       model['binary_head']

     # Store the classifier.
     lowercase_       :							Tuple  					=       binary_head['weight']
     lowercase_       :							Optional[Any]  					=       binary_head['bias']

     # It should be done!
     return output_state_dict

def _UpperCamelCase (							):
     # Create the argument parser.
     lowercase_       :							Optional[int]  					=       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.'	,							)
     lowercase_       :							Tuple  					=       parser.parse_args()

     # Extract the basename.
     lowercase_       :							List[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:
                    lowercase_       :							List[Any]  					=       torch.load(SCREAMING_SNAKE_CASE_	,							map_location='cpu'				)
     else:
          lowercase_       :							Union[str, Any]  					=       torch.load(args.path_to_checkpoint	,							map_location='cpu'				)

     if args.config_file == "":
          # Default config of megatron-bert 345m
          lowercase_       :							List[str]  					=       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
          lowercase_       :							Union[str, Any]  					=       input_state_dict['model']['lm_head']['bias'].numel()
     else:
          lowercase_       :							Optional[int]  					=       MegatronBertConfig.from_json_file(args.config_file				)

     # Convert.
     print('Converting'				)
     lowercase_       :							Optional[Any]  					=       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.
     lowercase_       :							List[Any]  					=       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()

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





'''simple docstring'''




def _UpperCamelCase (							SCREAMING_SNAKE_CASE_ = 1_000				):
     lowercase_       :							Tuple  					=       [0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8]
     # number of letters in twenty, thirty, ..., ninety (0 for numbers less than
     # 20 due to inconsistency in teens)
     lowercase_       :							List[Any]  					=       [0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

     lowercase_       :							Optional[Any]  					=       0

     for i in range(1	,							n + 1				):
          if i < 1_000:
               if i >= 100:
                    # add number of letters for "n hundred"
                    count += ones_counts[i // 100] + 7

                    if i % 100 != 0:
                         # add number of letters for "and" if number is not multiple
                         # of 100
                         count += 3

               if 0 < i % 100 < 20:
                    # add number of letters for one, two, three, ..., nineteen
                    # (could be combined with below if not for inconsistency in
                    # teens)
                    count += ones_counts[i % 100]
               else:
                    # add number of letters for twenty, twenty one, ..., ninety
                    # nine
                    count += ones_counts[i % 10]
                    count += tens_counts[(i % 100 - i % 10) // 10]
          else:
               count += ones_counts[i // 1_000] + 8
     return count


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




