


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


_A									=		logging.get_logger(__name__)


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

_A									=		'\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n'






class        UpperCAmelCase__			(   _a						):



    """simple docstring"""






    UpperCAmelCase__		:						List[str]    =    """bark_module"""
    UpperCAmelCase__		:						str    =    ["""past_key_values"""]

    UpperCAmelCase__		:						Tuple    =    {
        """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_   ,  )    ->			int:
         __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__(**lowerCAmelCase__       )






    @classmethod
    def _a   (     cls   ,  A_   ,  A_ = None   ,  A_ = False   ,  A_ = False   ,  A_ = None   ,  A_ = "main"   ,  **A_   ,  )    ->			Optional[Any]:
         __UpperCamelCase       				=cache_dir
         __UpperCamelCase       				=force_download
         __UpperCamelCase       				=local_files_only
         __UpperCamelCase       				=revision

         cls._set_token_in_kwargs(lowerCAmelCase__   ,  lowerCAmelCase__       )

         __UpperCamelCase				, __UpperCamelCase       				=cls.get_config_dict(lowerCAmelCase__   ,  **lowerCAmelCase__       )

         # 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(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__			(   _a						):



    """simple docstring"""






    UpperCAmelCase__		:						Union[str, Any]    =    """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__			(   _a						):



    """simple docstring"""






    UpperCAmelCase__		:						str    =    """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__			(   _a						):



    """simple docstring"""






    UpperCAmelCase__		:						str    =    """fine_acoustics"""






    def __init__(     self   ,  A_=True   ,  A_=8   ,  A_=1   ,  **A_       )    ->			List[str]:
         __UpperCamelCase       				=n_codes_total
         __UpperCamelCase       				=n_codes_given

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






class        UpperCAmelCase__			(   _a						):



    """simple docstring"""






    UpperCAmelCase__		:						Optional[Any]    =    """bark"""
    UpperCAmelCase__		:						Any    =    True






    def __init__(     self   ,  A_ = None   ,  A_ = None   ,  A_ = None   ,  A_ = None   ,  A_=0.02   ,  **A_   ,  )    ->			Any:
         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(**lowerCAmelCase__       )
         __UpperCamelCase       				=BarkCoarseConfig(**lowerCAmelCase__       )
         __UpperCamelCase       				=BarkFineConfig(**lowerCAmelCase__       )
         __UpperCamelCase       				=AutoConfig.for_model(**lowerCAmelCase__       )

         __UpperCamelCase       				=initializer_range

         super().__init__(**lowerCAmelCase__       )






    @classmethod
    def _a   (     cls   ,  A_   ,  A_   ,  A_   ,  A_   ,  **A_   ,  )    ->			int:
         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 _a   (     self       )    ->			List[str]:
         __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 heapq

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Dict:




       '''simple docstring'''






       _A=						[(0, start)]  # cost from start node,end node
       _A=						set()
       while heap:
              ((_A),      (_A))=						heapq.heappop(lowerCAmelCase_ )
              if u in visited:
                     continue
              visited.add(lowerCAmelCase_ )
              if u == end:
                     return cost
              for v, c in graph[u]:
                     if v in visited:
                            continue
                     _A=						cost + c
                     heapq.heappush(lowerCAmelCase_							,       (next_item, v) )
       return -1


UpperCAmelCase_    		=				{
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
UpperCAmelCase_    		=				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
UpperCAmelCase_    		=				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

UpperCAmelCase_    		=				dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

UpperCAmelCase_    		=				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

UpperCAmelCase_    		=				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()