




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      a__       (   snake_case__ ):
						_a				:       Tuple   =       """bark_module"""
						_a				:       Optional[int]   =       ["""past_key_values"""]

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






						def __init__(  self						,     _A=1_0_2_4						,     _A=1_0_0_4_8						,     _A=1_0_0_4_8						,     _A=1_2						,     _A=1_2						,     _A=7_6_8						,     _A=0.0						,     _A=True						,     _A=0.02						,     _A=True						,     **_A						,     ):




											"""simple docstring"""



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

											super().__init__(**_A     )






						@classmethod
						def 							__SCREAMING_SNAKE_CASE(  cls						,     _A						,     _A = None						,     _A = False						,     _A = False						,     _A = None						,     _A = "main"						,     **_A						,     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			cache_dir
											__lowerCAmelCase							  =			force_download
											__lowerCAmelCase							  =			local_files_only
											__lowerCAmelCase							  =			revision

											cls._set_token_in_kwargs(_A						,     _A     )

											__lowerCAmelCase  ,				__lowerCAmelCase							  =			cls.get_config_dict(_A						,     **_A     )

											# get the config dict if we are loading from Bark
											if config_dict.get("model_type"     ) == "bark":
																__lowerCAmelCase							  =			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      a__       (   snake_case__ ):
						_a				:       Union[str, Any]   =       """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      a__       (   snake_case__ ):
						_a				:       Optional[Any]   =       """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      a__       (   snake_case__ ):
						_a				:       List[Any]   =       """fine_acoustics"""






						def __init__(  self						,     _A=True						,     _A=8						,     _A=1						,     **_A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			n_codes_total
											__lowerCAmelCase							  =			n_codes_given

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






class      a__       (   snake_case__ ):
						_a				:       str   =       """bark"""
						_a				:       Dict   =       True






						def __init__(  self						,     _A = None						,     _A = None						,     _A = None						,     _A = None						,     _A=0.02						,     **_A						,     ):




											"""simple docstring"""



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

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

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

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

											__lowerCAmelCase							  =			BarkSemanticConfig(**_A     )
											__lowerCAmelCase							  =			BarkCoarseConfig(**_A     )
											__lowerCAmelCase							  =			BarkFineConfig(**_A     )
											__lowerCAmelCase							  =			AutoConfig.for_model(**_A     )

											__lowerCAmelCase							  =			initializer_range

											super().__init__(**_A     )






						@classmethod
						def 							__SCREAMING_SNAKE_CASE(  cls						,     _A						,     _A						,     _A						,     _A						,     **_A						,     ):




											"""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 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			copy.deepcopy(self.__dict__     )

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

											__lowerCAmelCase							  =			self.__class__.model_type
											return output










import decimal







def       _a (		):
					__lowerCAmelCase							  =			0
					__lowerCAmelCase							  =			decimal.Context(prec=1_05	)
					for i in range(2 , 1_00	):
										__lowerCAmelCase							  =			decimal.Decimal(SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			number.sqrt(SCREAMING_SNAKE_CASE_	)
										if len(str(SCREAMING_SNAKE_CASE_	)	) > 1:
															answer += int(str(SCREAMING_SNAKE_CASE_	)[0]	)
															__lowerCAmelCase							  =			str(SCREAMING_SNAKE_CASE_	)[2:1_01]
															answer += sum(int(SCREAMING_SNAKE_CASE_	) for x in sqrt_number_str	)
					return answer


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(f'''{solution() = }''')




