





'''simple docstring'''






import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


snake_case_     			=						logging.get_logger(__name__)


snake_case_     			=						{
    """suno/bark-small""": """https://huggingface.co/suno/bark-small/resolve/main/config.json""",
    """suno/bark""": """https://huggingface.co/suno/bark/resolve/main/config.json""",
}

snake_case_     			=						"""
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the \"intermediate\" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
"""


class 	_lowercase    (				a					):
   _UpperCamelCase									= """bark_module"""
   _UpperCamelCase									= ["""past_key_values"""]

   _UpperCamelCase									= {
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
       """vocab_size""": """input_vocab_size""",
       """window_size""": """block_size""",
   }


   def __init__(			self     , _UpperCAmelCase=1_024     , _UpperCAmelCase=10_048     , _UpperCAmelCase=10_048     , _UpperCAmelCase=12     , _UpperCAmelCase=12     , _UpperCAmelCase=768     , _UpperCAmelCase=0.0     , _UpperCAmelCase=True     , _UpperCAmelCase=0.02     , _UpperCAmelCase=True     , **_UpperCAmelCase     , ):
       A							:							int				=    block_size
       A							:							Dict				=    input_vocab_size
       A							:							str				=    output_vocab_size
       A							:							str				=    num_layers
       A							:							Union[str, Any]				=    num_heads
       A							:							Dict				=    hidden_size
       A							:							Any				=    dropout
       A							:							str				=    bias
       A							:							str				=    use_cache
       A							:							List[str]				=    initializer_range

       super().__init__(**_UpperCAmelCase       )


   @classmethod
   def        snake_case					(			cls     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = False     , _UpperCAmelCase = False     , _UpperCAmelCase = None     , _UpperCAmelCase = "main"     , **_UpperCAmelCase     , ):
       A							:							Union[str, Any]				=    cache_dir
       A							:							Tuple				=    force_download
       A							:							Dict				=    local_files_only
       A							:							int				=    revision

       cls._set_token_in_kwargs(_UpperCAmelCase     , _UpperCAmelCase       )

       A,						A							:							Optional[int]				=    cls.get_config_dict(_UpperCAmelCase     , **_UpperCAmelCase       )

       # get the config dict if we are loading from Bark
       if config_dict.get('''model_type'''       ) == "bark":
           A							:							Optional[int]				=    config_dict[f'''{cls.model_type}_config''']

       if "model_type" in config_dict and hasattr(cls     , '''model_type'''       ) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
               f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''       )

       return cls.from_dict(_UpperCAmelCase     , **_UpperCAmelCase       )







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkSemanticConfig"""		,       model="""BarkSemanticModel"""					)		,       """
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""		,       )
class 	_lowercase    (				a					):
   _UpperCamelCase									= """semantic"""







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkCoarseConfig"""		,       model="""BarkCoarseModel"""					)		,       """
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""		,       )
class 	_lowercase    (				a					):
   _UpperCamelCase									= """coarse_acoustics"""







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkFineConfig"""		,       model="""BarkFineModel"""					)		,       """
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""		,       )
class 	_lowercase    (				a					):
   _UpperCamelCase									= """fine_acoustics"""


   def __init__(			self     , _UpperCAmelCase=True     , _UpperCAmelCase=8     , _UpperCAmelCase=1     , **_UpperCAmelCase       ):
       A							:							Dict				=    n_codes_total
       A							:							int				=    n_codes_given

       super().__init__(tie_word_embeddings=_UpperCAmelCase     , **_UpperCAmelCase       )







class 	_lowercase    (				a					):
   _UpperCamelCase									= """bark"""
   _UpperCamelCase									= True


   def __init__(			self     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase=0.02     , **_UpperCAmelCase     , ):
       if semantic_config is None:
           A							:							Union[str, Any]				=    {}
           logger.info('''semantic_config is None. initializing the semantic model with default values.'''       )

       if coarse_acoustics_config is None:
           A							:							Dict				=    {}
           logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''       )

       if fine_acoustics_config is None:
           A							:							int				=    {}
           logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''       )

       if codec_config is None:
           A							:							Tuple				=    {}
           logger.info('''codec_config is None. initializing the codec model with default values.'''       )

       A							:							Optional[Any]				=    BarkSemanticConfig(**_UpperCAmelCase       )
       A							:							Optional[Any]				=    BarkCoarseConfig(**_UpperCAmelCase       )
       A							:							List[Any]				=    BarkFineConfig(**_UpperCAmelCase       )
       A							:							List[str]				=    AutoConfig.for_model(**_UpperCAmelCase       )

       A							:							List[Any]				=    initializer_range

       super().__init__(**_UpperCAmelCase       )


   @classmethod
   def        snake_case					(			cls     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , **_UpperCAmelCase     , ):
       return cls(
           semantic_config=semantic_config.to_dict()     , coarse_acoustics_config=coarse_acoustics_config.to_dict()     , fine_acoustics_config=fine_acoustics_config.to_dict()     , codec_config=codec_config.to_dict()     , **_UpperCAmelCase     , )


   def        snake_case					(			self       ):
       A							:							str				=    copy.deepcopy(self.__dict__       )

       A							:							str				=    self.semantic_config.to_dict()
       A							:							Any				=    self.coarse_acoustics_config.to_dict()
       A							:							Any				=    self.fine_acoustics_config.to_dict()
       A							:							str				=    self.codec_config.to_dict()

       A							:							Dict				=    self.__class__.model_type
       return output







'''simple docstring'''






def 				_lowerCamelCase(	UpperCamelCase__     :			int = 1_000		)		->	int:
    A							:							Optional[Any]				=    [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)
    A							:							Tuple				=    [0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

    A							:							str				=    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())))
