
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     :				Optional[int]             =       logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE     :				Dict             =       {
    '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     :				List[Any]             =       '\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 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"bark_module"
		_lowerCAmelCase						=		["past_key_values"]

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





		def __init__(							self      :		Optional[Any]  ,					lowerCamelCase__      :		str=1_0_2_4  ,					lowerCamelCase__      :		Tuple=1_0_0_4_8  ,					lowerCamelCase__      :		Tuple=1_0_0_4_8  ,					lowerCamelCase__      :		str=1_2  ,					lowerCamelCase__      :		List[str]=1_2  ,					lowerCamelCase__      :		Any=7_6_8  ,					lowerCamelCase__      :		Tuple=0.0  ,					lowerCamelCase__      :		List[Any]=True  ,					lowerCamelCase__      :		int=0.0_2  ,					lowerCamelCase__      :		Tuple=True  ,					**lowerCamelCase__      :		List[Any]  ,					):
							lowerCAmelCase						:   Optional[Any]							=       block_size
							lowerCAmelCase						:   List[str]							=       input_vocab_size
							lowerCAmelCase						:   Union[str, Any]							=       output_vocab_size
							lowerCAmelCase						:   List[str]							=       num_layers
							lowerCAmelCase						:   Dict							=       num_heads
							lowerCAmelCase						:   Tuple							=       hidden_size
							lowerCAmelCase						:   Tuple							=       dropout
							lowerCAmelCase						:   Tuple							=       bias
							lowerCAmelCase						:   List[Any]							=       use_cache
							lowerCAmelCase						:   int							=       initializer_range

							super().__init__(**lowerCamelCase__			)





		@classmethod
		def  _A							(							cls      :		Dict  ,					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__      :		List[Any]  ,					):
							lowerCAmelCase						:   Optional[int]							=       cache_dir
							lowerCAmelCase						:   Optional[int]							=       force_download
							lowerCAmelCase						:   Tuple							=       local_files_only
							lowerCAmelCase						:   Optional[int]							=       revision

							cls._set_token_in_kwargs(lowerCamelCase__  ,					lowerCamelCase__			)

							lowerCAmelCase		,							lowerCAmelCase						:   Tuple							=       cls.get_config_dict(lowerCamelCase__  ,					**lowerCamelCase__			)

							# get the config dict if we are loading from Bark
							if config_dict.get('''model_type'''			) == "bark":
												lowerCAmelCase						:   List[str]							=       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 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"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 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"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 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"fine_acoustics"





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

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



class 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"bark"
		_lowerCAmelCase						=		True





		def __init__(							self      :		Optional[Any]  ,					lowerCamelCase__      :		Dict = None  ,					lowerCamelCase__      :		Dict = None  ,					lowerCamelCase__      :		Dict = None  ,					lowerCamelCase__      :		Dict = None  ,					lowerCamelCase__      :		Union[str, Any]=0.0_2  ,					**lowerCamelCase__      :		Optional[Any]  ,					):
							if semantic_config is None:
												lowerCAmelCase						:   Any							=       {}
												logger.info('''semantic_config is None. initializing the semantic model with default values.'''			)

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

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

							if codec_config is None:
												lowerCAmelCase						:   List[Any]							=       {}
												logger.info('''codec_config is None. initializing the codec model with default values.'''			)

							lowerCAmelCase						:   Optional[int]							=       BarkSemanticConfig(**lowerCamelCase__			)
							lowerCAmelCase						:   Optional[int]							=       BarkCoarseConfig(**lowerCamelCase__			)
							lowerCAmelCase						:   Tuple							=       BarkFineConfig(**lowerCamelCase__			)
							lowerCAmelCase						:   Tuple							=       AutoConfig.for_model(**lowerCamelCase__			)

							lowerCAmelCase						:   int							=       initializer_range

							super().__init__(**lowerCamelCase__			)





		@classmethod
		def  _A							(							cls      :		Any  ,					lowerCamelCase__      :		BarkSemanticConfig  ,					lowerCamelCase__      :		BarkCoarseConfig  ,					lowerCamelCase__      :		BarkFineConfig  ,					lowerCamelCase__      :		AutoConfig  ,					**lowerCamelCase__      :		Optional[Any]  ,					):
							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      :		Tuple			):
							lowerCAmelCase						:   Optional[int]							=       copy.deepcopy(self.__dict__			)

							lowerCAmelCase						:   Union[str, Any]							=       self.semantic_config.to_dict()
							lowerCAmelCase						:   Optional[int]							=       self.coarse_acoustics_config.to_dict()
							lowerCAmelCase						:   Any							=       self.fine_acoustics_config.to_dict()
							lowerCAmelCase						:   Optional[int]							=       self.codec_config.to_dict()

							lowerCAmelCase						:   Tuple							=       self.__class__.model_type
							return output


def       UpperCAmelCase__							(    __magic_name__  :       int = 99_80_01					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       0
					for i in range(9_99							,					99							,					-1					):  # 3 digit numbers range from 999 down to 100
										for j in range(9_99							,					99							,					-1					):
															lowerCAmelCase						:   Any							=       str(i * j					)
															if product_string == product_string[::-1] and i * j < n:
																				lowerCAmelCase						:   Optional[int]							=       max(__magic_name__							,					i * j					)
					return answer


if __name__ == "__main__":
		print(f"""{solution() = }""")
