





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


SCREAMING_SNAKE_CASE_		  =			logging.get_logger(__name__)


SCREAMING_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""",
}

SCREAMING_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 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      Optional[int]						      =     "bark_module"
     __snake_case       :      List[str]						      =     ["past_key_values"]

     __snake_case       :      Optional[int]						      =     {
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
         "vocab_size": "input_vocab_size",
         "window_size": "block_size",
     }






     def __init__(				self		:       Optional[int]		,lowerCamelCase__		:       Optional[Any]=1024		,lowerCamelCase__		:       Optional[Any]=10048		,lowerCamelCase__		:       List[str]=10048		,lowerCamelCase__		:       Optional[int]=12		,lowerCamelCase__		:       List[str]=12		,lowerCamelCase__		:       Any=768		,lowerCamelCase__		:       str=0.0		,lowerCamelCase__		:       int=True		,lowerCamelCase__		:       Dict=0.02		,lowerCamelCase__		:       str=True		,**lowerCamelCase__		:       Optional[int]		,)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		block_size
           SCREAMING_SNAKE_CASE						=		input_vocab_size
           SCREAMING_SNAKE_CASE						=		output_vocab_size
           SCREAMING_SNAKE_CASE						=		num_layers
           SCREAMING_SNAKE_CASE						=		num_heads
           SCREAMING_SNAKE_CASE						=		hidden_size
           SCREAMING_SNAKE_CASE						=		dropout
           SCREAMING_SNAKE_CASE						=		bias
           SCREAMING_SNAKE_CASE						=		use_cache
           SCREAMING_SNAKE_CASE						=		initializer_range

           super().__init__(**lowerCamelCase__			)






     @classmethod
     def 							SCREAMING_SNAKE_CASE__    (				cls		:       str		,lowerCamelCase__		:       Union[str, os.PathLike]		,lowerCamelCase__		:       Optional[Union[str, os.PathLike]] = None		,lowerCamelCase__		:       bool = False		,lowerCamelCase__		:       bool = False		,lowerCamelCase__		:       Optional[Union[str, bool]] = None		,lowerCamelCase__		:       str = "main"		,**lowerCamelCase__		:       Optional[Any]		,)	->						"PretrainedConfig":




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		cache_dir
           SCREAMING_SNAKE_CASE						=		force_download
           SCREAMING_SNAKE_CASE						=		local_files_only
           SCREAMING_SNAKE_CASE						=		revision

           cls._set_token_in_kwargs(lowerCamelCase__		,lowerCamelCase__			)

           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		cls.get_config_dict(lowerCamelCase__		,**lowerCamelCase__			)

           # get the config dict if we are loading from Bark
           if config_dict.get("""model_type"""			) == "bark":
                 SCREAMING_SNAKE_CASE						=		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(lowerCamelCase__		,**lowerCamelCase__			)

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig"				, model="BarkSemanticModel"  )				, "\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				, )
class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      int						      =     "semantic"

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig"				, model="BarkCoarseModel"  )				, "\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				, )
class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      Tuple						      =     "coarse_acoustics"

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig"				, model="BarkFineModel"  )				, "\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				, )
class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      int						      =     "fine_acoustics"






     def __init__(				self		:       Tuple		,lowerCamelCase__		:       Union[str, Any]=True		,lowerCamelCase__		:       List[Any]=8		,lowerCamelCase__		:       int=1		,**lowerCamelCase__		:       List[str]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		n_codes_total
           SCREAMING_SNAKE_CASE						=		n_codes_given

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

class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      Optional[int]						      =     "bark"
     __snake_case       :      int						      =     True






     def __init__(				self		:       str		,lowerCamelCase__		:       Dict = None		,lowerCamelCase__		:       Dict = None		,lowerCamelCase__		:       Dict = None		,lowerCamelCase__		:       Dict = None		,lowerCamelCase__		:       Dict=0.02		,**lowerCamelCase__		:       str		,)	->						str:




           '''simple docstring'''


           if semantic_config is None:
                 SCREAMING_SNAKE_CASE						=		{}
                 logger.info("""semantic_config is None. initializing the semantic model with default values."""			)

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

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

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

           SCREAMING_SNAKE_CASE						=		BarkSemanticConfig(**lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		BarkCoarseConfig(**lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		BarkFineConfig(**lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		AutoConfig.for_model(**lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		initializer_range

           super().__init__(**lowerCamelCase__			)






     @classmethod
     def 							SCREAMING_SNAKE_CASE__    (				cls		:       List[str]		,lowerCamelCase__		:       BarkSemanticConfig		,lowerCamelCase__		:       BarkCoarseConfig		,lowerCamelCase__		:       BarkFineConfig		,lowerCamelCase__		:       AutoConfig		,**lowerCamelCase__		:       Union[str, Any]		,)	->						List[Any]:




           '''simple docstring'''


           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()		,**lowerCamelCase__		,)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		copy.deepcopy(self.__dict__			)

           SCREAMING_SNAKE_CASE						=		self.semantic_config.to_dict()
           SCREAMING_SNAKE_CASE						=		self.coarse_acoustics_config.to_dict()
           SCREAMING_SNAKE_CASE						=		self.fine_acoustics_config.to_dict()
           SCREAMING_SNAKE_CASE						=		self.codec_config.to_dict()

           SCREAMING_SNAKE_CASE						=		self.__class__.model_type
           return output









import os



def   __lowercase  ( )					->  int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		os.path.dirname(os.path.realpath(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		os.path.join(_SCREAMING_SNAKE_CASE					,		"""triangle.txt"""      )

      with open(_SCREAMING_SNAKE_CASE      ) as in_file:
            SCREAMING_SNAKE_CASE						=		[[int(_SCREAMING_SNAKE_CASE      ) for i in line.split()] for line in in_file]

      while len(_SCREAMING_SNAKE_CASE      ) != 1:
            SCREAMING_SNAKE_CASE						=		triangle.pop()
            SCREAMING_SNAKE_CASE						=		triangle[-1]
            for j in range(len(_SCREAMING_SNAKE_CASE      ) - 1      ):
                  curr_row[j] += max(last_row[j]					,		last_row[j + 1]      )
      return triangle[0][0]


if __name__ == "__main__":
      print(solution())


