






'''simple docstring'''

import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
     import numpy as np
     import torch

     from . import (
         AlbertForPreTraining,
         BartForConditionalGeneration,
         BertForPreTraining,
         BertForQuestionAnswering,
         BertForSequenceClassification,
         CamembertForMaskedLM,
         CTRLLMHeadModel,
         DistilBertForMaskedLM,
         DistilBertForQuestionAnswering,
         DPRContextEncoder,
         DPRQuestionEncoder,
         DPRReader,
         ElectraForPreTraining,
         FlaubertWithLMHeadModel,
         GPTaLMHeadModel,
         LayoutLMForMaskedLM,
         LxmertForPreTraining,
         LxmertVisualFeatureEncoder,
         OpenAIGPTLMHeadModel,
         RobertaForMaskedLM,
         RobertaForSequenceClassification,
         TaForConditionalGeneration,
         TransfoXLLMHeadModel,
         XLMRobertaForMaskedLM,
         XLMWithLMHeadModel,
         XLNetLMHeadModel,
     )


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE__											=     {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=False	,		__UpperCamelCase=True					)->				str:
      if model_type not in MODEL_CLASSES:
            raise ValueError(F"Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()					)}."					)

      UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      MODEL_CLASSES[model_type]

      # Initialise TF model
      if config_file in aws_config_map:
            UpperCamelCase       						=      cached_file(SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		force_download=not use_cached_models					)
      UpperCamelCase       						=      config_class.from_json_file(SCREAMING_SNAKE_CASE_					)
      UpperCamelCase       						=      True
      UpperCamelCase       						=      True
      print(F"Building TensorFlow model from configuration: {config}"					)
      UpperCamelCase       						=      model_class(SCREAMING_SNAKE_CASE_					)

      # Load weights from tf checkpoint
      if pytorch_checkpoint_path in aws_config_map.keys():
            UpperCamelCase       						=      cached_file(
                SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		force_download=not use_cached_models					)
      # Load PyTorch checkpoint in tf2 model:
      UpperCamelCase       						=      load_pytorch_checkpoint_in_tfa_model(SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_					)

      if compare_with_pt_model:
            UpperCamelCase       						=      tf_model(tf_model.dummy_inputs	,		training=SCREAMING_SNAKE_CASE_					)  # build the network

            UpperCamelCase       						=      torch.load(SCREAMING_SNAKE_CASE_	,		map_location="""cpu"""					)
            UpperCamelCase       						=      pt_model_class.from_pretrained(
                pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_	,		config=SCREAMING_SNAKE_CASE_	,		state_dict=SCREAMING_SNAKE_CASE_					)

            with torch.no_grad():
                  UpperCamelCase       						=      pt_model(**pt_model.dummy_inputs					)

            UpperCamelCase       						=      pto[0].numpy()
            UpperCamelCase       						=      tfo[0].numpy()
            UpperCamelCase       						=      np.amax(np.abs(np_pt - np_tf					)					)
            print(F"Max absolute difference between models outputs {diff}"					)
            assert diff <= 2E-2, F"Error, model absolute difference is >2e-2: {diff}"

      # Save pytorch-model
      print(F"Save TensorFlow model to {tf_dump_path}"					)
      tf_model.save_weights(SCREAMING_SNAKE_CASE_	,		save_format="""h5"""					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		__UpperCamelCase=False	,		__UpperCamelCase=False	,		__UpperCamelCase=False	,		__UpperCamelCase=False	,		)->				Union[str, Any]:
      if args_model_type is None:
            UpperCamelCase       						=      list(MODEL_CLASSES.keys()					)
      else:
            UpperCamelCase       						=      [args_model_type]

      for j, model_type in enumerate(SCREAMING_SNAKE_CASE_	,		start=1					):
            print("""=""" * 100					)
            print(F" Converting model type {j}/{len(SCREAMING_SNAKE_CASE_					)}: {model_type}"					)
            print("""=""" * 100					)
            if model_type not in MODEL_CLASSES:
                  raise ValueError(F"Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()					)}."					)

            UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      MODEL_CLASSES[model_type]

            if model_shortcut_names_or_path is None:
                  UpperCamelCase       						=      list(aws_model_maps.keys()					)
            if config_shortcut_names_or_path is None:
                  UpperCamelCase       						=      model_shortcut_names_or_path

            for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                zip(SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_					)	,		start=1					):
                  print("""-""" * 100					)
                  if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                        if not only_convert_finetuned_models:
                              print(F"    Skipping finetuned checkpoint {model_shortcut_name}"					)
                              continue
                        UpperCamelCase       						=      model_shortcut_name
                  elif only_convert_finetuned_models:
                        print(F"    Skipping not finetuned checkpoint {model_shortcut_name}"					)
                        continue
                  print(
                      F"    Converting checkpoint {i}/{len(SCREAMING_SNAKE_CASE_					)}: {model_shortcut_name} - model_type {model_type}"					)
                  print("""-""" * 100					)

                  if config_shortcut_name in aws_config_map:
                        UpperCamelCase       						=      cached_file(SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		force_download=not use_cached_models					)
                  else:
                        UpperCamelCase       						=      config_shortcut_name

                  if model_shortcut_name in aws_model_maps:
                        UpperCamelCase       						=      cached_file(SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		force_download=not use_cached_models					)
                  else:
                        UpperCamelCase       						=      model_shortcut_name

                  if os.path.isfile(SCREAMING_SNAKE_CASE_					):
                        UpperCamelCase       						=      """converted_model"""

                  convert_pt_checkpoint_to_tf(
                      model_type=SCREAMING_SNAKE_CASE_	,		pytorch_checkpoint_path=SCREAMING_SNAKE_CASE_	,		config_file=SCREAMING_SNAKE_CASE_	,		tf_dump_path=os.path.join(SCREAMING_SNAKE_CASE_	,		model_shortcut_name + """-tf_model.h5"""					)	,		compare_with_pt_model=SCREAMING_SNAKE_CASE_	,		)
                  if remove_cached_files:
                        os.remove(SCREAMING_SNAKE_CASE_					)
                        os.remove(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
     )
     parser.add_argument(
         '--model_type',
         default=None,
         type=str,
         help=(
             f'Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '
             'convert all the models from AWS.'
         ),
     )
     parser.add_argument(
         '--pytorch_checkpoint_path',
         default=None,
         type=str,
         help=(
             'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
             'If not given, will download and convert all the checkpoints from AWS.'
         ),
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         help=(
             'The config json file corresponding to the pre-trained model. \n'
             'This specifies the model architecture. If not given and '
             '--pytorch_checkpoint_path is not given or is a shortcut name '
             'use the configuration associated to the shortcut name on the AWS'
         ),
     )
     parser.add_argument(
         '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
     )
     parser.add_argument(
         '--use_cached_models',
         action='store_true',
         help='Use cached models if possible instead of updating to latest checkpoint versions.',
     )
     parser.add_argument(
         '--remove_cached_files',
         action='store_true',
         help='Remove pytorch models after conversion (save memory when converting in batches).',
     )
     parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     # if args.pytorch_checkpoint_path is not None:
     #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
     #                                 args.pytorch_checkpoint_path,
     #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
     #                                 args.tf_dump_path,
     #                                 compare_with_pt_model=args.compare_with_pt_model,
     #                                 use_cached_models=args.use_cached_models)
     # else:
     convert_all_pt_checkpoints_to_tf(
         args.model_type.lower() if args.model_type is not None else None,
         args.tf_dump_path,
         model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
         if args.pytorch_checkpoint_path is not None
         else None,
         config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
         compare_with_pt_model=args.compare_with_pt_model,
         use_cached_models=args.use_cached_models,
         remove_cached_files=args.remove_cached_files,
         only_convert_finetuned_models=args.only_convert_finetuned_models,
     )








'''simple docstring'''

from math import sqrt







def 					lowercase__				(   __UpperCamelCase					)->				int:
      UpperCamelCase       						=      0
      for i in range(1	,		int(sqrt(__UpperCamelCase					) + 1					)					):
            if n % i == 0 and i != sqrt(__UpperCamelCase					):
                  total += i + n // i
            elif i == sqrt(__UpperCamelCase					):
                  total += i
      return total - n







def 					lowercase__				(   __UpperCamelCase = 10000					)->				int:
      UpperCamelCase       						=      sum(
          i
          for i in range(1	,		__UpperCamelCase					)
          if sum_of_divisors(sum_of_divisors(__UpperCamelCase					)					) == i and sum_of_divisors(__UpperCamelCase					) != i					)
      return total


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