

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


_UpperCamelCase			       =			logging.get_logger(__name__)


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

_UpperCamelCase			       =			'''
    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						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""bark_module"""
 __SCREAMING_SNAKE_CASE												=				["""past_key_values"""]

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




 def __init__(self       ,       __a=1024       ,       __a=10048       ,       __a=10048       ,       __a=12       ,       __a=12       ,       __a=768       ,       __a=0.0       ,       __a=True       ,       __a=0.02       ,       __a=True       ,       **__a       ,       ) ->					Optional[int]:






  """simple docstring"""





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

  super().__init__(**__a )




 @classmethod
 def   UpperCamelCase__							(cls       ,       __a       ,       __a = None       ,       __a = False       ,       __a = False       ,       __a = None       ,       __a = "main"       ,       **__a       ,       ) ->					"PretrainedConfig":






  """simple docstring"""





  UpperCAmelCase__							=				cache_dir
  UpperCAmelCase__							=				force_download
  UpperCAmelCase__							=				local_files_only
  UpperCAmelCase__							=				revision

  cls._set_token_in_kwargs(__a       ,       __a )

  UpperCAmelCase__				,    UpperCAmelCase__							=				cls.get_config_dict(__a       ,       **__a )

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







@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						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""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						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""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						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""fine_acoustics"""




 def __init__(self       ,       __a=True       ,       __a=8       ,       __a=1       ,       **__a ) ->					Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__							=				n_codes_total
  UpperCAmelCase__							=				n_codes_given

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







class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""bark"""
 __SCREAMING_SNAKE_CASE												=				True




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






  """simple docstring"""





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

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

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

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

  UpperCAmelCase__							=				BarkSemanticConfig(**__a )
  UpperCAmelCase__							=				BarkCoarseConfig(**__a )
  UpperCAmelCase__							=				BarkFineConfig(**__a )
  UpperCAmelCase__							=				AutoConfig.for_model(**__a )

  UpperCAmelCase__							=				initializer_range

  super().__init__(**__a )




 @classmethod
 def   UpperCamelCase__							(cls       ,       __a       ,       __a       ,       __a       ,       __a       ,       **__a       ,       ) ->					List[str]:






  """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()       ,       **__a       ,       )




 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				copy.deepcopy(self.__dict__ )

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

  UpperCAmelCase__							=				self.__class__.model_type
  return output






import os
def 						UpperCamelCase_(			)       -> int:
 UpperCAmelCase__							=				os.path.dirname(os.path.realpath(snake_case__   )   )
 UpperCAmelCase__							=				os.path.join(snake_case__  ,    'triangle.txt'   )

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

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


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



