






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 							__snake_case(       __UpperCamelCase   ):
 lowercase			:							int		   =		'''bark_module'''
 lowercase			:							Optional[Any]		   =		['''past_key_values''']

 lowercase			:							Dict		   =		{
     '''num_attention_heads''': '''num_heads''',
     '''num_hidden_layers''': '''num_layers''',
     '''vocab_size''': '''input_vocab_size''',
     '''window_size''': '''block_size''',
 }



 def __init__(						self : List[str]			,		UpperCAmelCase__ : Optional[int]=1_024			,		UpperCAmelCase__ : Any=10_048			,		UpperCAmelCase__ : Tuple=10_048			,		UpperCAmelCase__ : Any=12			,		UpperCAmelCase__ : Union[str, Any]=12			,		UpperCAmelCase__ : str=768			,		UpperCAmelCase__ : List[str]=0.0			,		UpperCAmelCase__ : Optional[Any]=True			,		UpperCAmelCase__ : Dict=0.02			,		UpperCAmelCase__ : int=True			,		**UpperCAmelCase__ : Dict			,		):

    """simple docstring"""






    lowercase__    :						str       =			block_size
    lowercase__    :						Dict       =			input_vocab_size
    lowercase__    :						Dict       =			output_vocab_size
    lowercase__    :						Any       =			num_layers
    lowercase__    :						Dict       =			num_heads
    lowercase__    :						List[Any]       =			hidden_size
    lowercase__    :						List[Any]       =			dropout
    lowercase__    :						List[str]       =			bias
    lowercase__    :						List[Any]       =			use_cache
    lowercase__    :						Dict       =			initializer_range

    super().__init__(**UpperCAmelCase__						)







 @classmethod
 def     UpperCAmelCase     (						cls : Tuple			,		UpperCAmelCase__ : Union[str, os.PathLike]			,		UpperCAmelCase__ : Optional[Union[str, os.PathLike]] = None			,		UpperCAmelCase__ : bool = False			,		UpperCAmelCase__ : bool = False			,		UpperCAmelCase__ : Optional[Union[str, bool]] = None			,		UpperCAmelCase__ : str = "main"			,		**UpperCAmelCase__ : Optional[Any]			,		):

    """simple docstring"""






    lowercase__    :						Tuple       =			cache_dir
    lowercase__    :						Dict       =			force_download
    lowercase__    :						Dict       =			local_files_only
    lowercase__    :						Optional[int]       =			revision

    cls._set_token_in_kwargs(UpperCAmelCase__			,		UpperCAmelCase__						)

    lowercase__  ,       lowercase__    :						int       =			cls.get_config_dict(UpperCAmelCase__			,		**UpperCAmelCase__						)

    # get the config dict if we are loading from Bark
    if config_dict.get("""model_type"""						) == "bark":
       lowercase__    :						Union[str, Any]       =			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 							__snake_case(       __UpperCamelCase   ):
 lowercase			:							List[str]		   =		'''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 							__snake_case(       __UpperCamelCase   ):
 lowercase			:							Dict		   =		'''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 							__snake_case(       __UpperCamelCase   ):
 lowercase			:							Tuple		   =		'''fine_acoustics'''



 def __init__(						self : List[str]			,		UpperCAmelCase__ : Optional[Any]=True			,		UpperCAmelCase__ : Optional[int]=8			,		UpperCAmelCase__ : Union[str, Any]=1			,		**UpperCAmelCase__ : Union[str, Any]						):

    """simple docstring"""






    lowercase__    :						Tuple       =			n_codes_total
    lowercase__    :						int       =			n_codes_given

    super().__init__(tie_word_embeddings=UpperCAmelCase__			,		**UpperCAmelCase__						)
class 							__snake_case(       __UpperCamelCase   ):
 lowercase			:							int		   =		'''bark'''
 lowercase			:							str		   =		True



 def __init__(						self : Optional[Any]			,		UpperCAmelCase__ : Dict = None			,		UpperCAmelCase__ : Dict = None			,		UpperCAmelCase__ : Dict = None			,		UpperCAmelCase__ : Dict = None			,		UpperCAmelCase__ : Union[str, Any]=0.02			,		**UpperCAmelCase__ : Tuple			,		):

    """simple docstring"""






    if semantic_config is None:
       lowercase__    :						Optional[Any]       =			{}
       logger.info("""semantic_config is None. initializing the semantic model with default values."""						)

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

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

    if codec_config is None:
       lowercase__    :						Optional[Any]       =			{}
       logger.info("""codec_config is None. initializing the codec model with default values."""						)

    lowercase__    :						Union[str, Any]       =			BarkSemanticConfig(**UpperCAmelCase__						)
    lowercase__    :						Dict       =			BarkCoarseConfig(**UpperCAmelCase__						)
    lowercase__    :						Dict       =			BarkFineConfig(**UpperCAmelCase__						)
    lowercase__    :						Optional[Any]       =			AutoConfig.for_model(**UpperCAmelCase__						)

    lowercase__    :						Optional[Any]       =			initializer_range

    super().__init__(**UpperCAmelCase__						)



 @classmethod
 def     UpperCAmelCase     (						cls : List[str]			,		UpperCAmelCase__ : BarkSemanticConfig			,		UpperCAmelCase__ : BarkCoarseConfig			,		UpperCAmelCase__ : BarkFineConfig			,		UpperCAmelCase__ : AutoConfig			,		**UpperCAmelCase__ : int			,		):

    """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()			,		**UpperCAmelCase__			,		)







 def     UpperCAmelCase     (						self : Tuple						):

    """simple docstring"""






    lowercase__    :						List[str]       =			copy.deepcopy(self.__dict__						)

    lowercase__    :						List[str]       =			self.semantic_config.to_dict()
    lowercase__    :						List[str]       =			self.coarse_acoustics_config.to_dict()
    lowercase__    :						Optional[Any]       =			self.fine_acoustics_config.to_dict()
    lowercase__    :						Dict       =			self.codec_config.to_dict()

    lowercase__    :						List[str]       =			self.__class__.model_type
    return output










from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
a 			=							[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
def    a__    (	A       ):
   return [int(A       ) for c in F'''{ruleset:08}'''[:8]]
def    a__    (	A		,					A		,					A       ):
   lowercase__    :						List[Any]       =			len(cells[0]       )  # 31
   lowercase__    :						int       =			[]
   for i in range(A       ):
      # Get the neighbors of each cell
      # Handle neighbours outside bounds by using 0 as their value
      lowercase__    :						Optional[Any]       =			0 if i == 0 else cells[time][i - 1]
      lowercase__    :						Dict       =			0 if i == population - 1 else cells[time][i + 1]
      # Define a new cell and add it to the new generation
      lowercase__    :						str       =			7 - int(F'''{left_neighbor}{cells[time][i]}{right_neighbor}'''		,					2       )
      next_generation.append(rule[situation]       )
   return next_generation
def    a__    (	A       ):
   lowercase__    :						Tuple       =			Image.new("""RGB"""		,					(len(cells[0]       ), len(A       ))       )
   lowercase__    :						Optional[Any]       =			img.load()
   # Generates image
   for w in range(img.width       ):
      for h in range(img.height       ):
         lowercase__    :						Union[str, Any]       =			255 - int(255 * cells[h][w]       )
         lowercase__    :						int       =			(color, color, color)
   return img


if __name__ == "__main__":
 a 			=							bin(int(input('Rule:\n').strip()))[2:]
 a 			=							format_ruleset(int(rule_num))
 for time in range(16):
  CELLS.append(new_generation(CELLS, rule, time))
 a 			=							generate_image(CELLS)
 # Uncomment to save the image
 # img.save(f"rule_{rule_num}.png")
 img.show()


