

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


lowerCAmelCase_ 			=			logging.get_logger(__name__)


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

lowerCAmelCase_ 			=			'''
    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 			snake_case_     (	__A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Any		    =       "bark_module"
			SCREAMING_SNAKE_CASE  :     Dict		    =       ["past_key_values"]

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







			def __init__(  self				:       Tuple							,     _UpperCamelCase				:       Any=1_0_2_4							,     _UpperCamelCase				:       List[Any]=1_0_0_4_8							,     _UpperCamelCase				:       Optional[int]=1_0_0_4_8							,     _UpperCamelCase				:       Optional[int]=1_2							,     _UpperCamelCase				:       Optional[int]=1_2							,     _UpperCamelCase				:       str=7_6_8							,     _UpperCamelCase				:       Optional[int]=0.0							,     _UpperCamelCase				:       Union[str, Any]=True							,     _UpperCamelCase				:       Dict=0.02							,     _UpperCamelCase				:       int=True							,     **_UpperCamelCase				:       int							,     )   ->Optional[int]:
									snake_case_     =			block_size
									snake_case_     =			input_vocab_size
									snake_case_     =			output_vocab_size
									snake_case_     =			num_layers
									snake_case_     =			num_heads
									snake_case_     =			hidden_size
									snake_case_     =			dropout
									snake_case_     =			bias
									snake_case_     =			use_cache
									snake_case_     =			initializer_range

									super().__init__(**_UpperCamelCase							)







			@classmethod
			def        snake_case__(  cls				:       Union[str, Any]							,     _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[int]							,     )   ->"PretrainedConfig":
									snake_case_     =			cache_dir
									snake_case_     =			force_download
									snake_case_     =			local_files_only
									snake_case_     =			revision

									cls._set_token_in_kwargs(_UpperCamelCase							,     _UpperCamelCase							)

									snake_case_,					snake_case_     =			cls.get_config_dict(_UpperCamelCase							,     **_UpperCamelCase							)

									# get the config dict if we are loading from Bark
									if config_dict.get('''model_type'''							) == "bark":
															snake_case_     =			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"      )			, "\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 			snake_case_     (	__A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     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 			snake_case_     (	__A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Any		    =       "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 			snake_case_     (	__A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Tuple		    =       "fine_acoustics"







			def __init__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       Optional[Any]=True							,     _UpperCamelCase				:       Dict=8							,     _UpperCamelCase				:       Optional[Any]=1							,     **_UpperCamelCase				:       Union[str, Any]							)   ->Tuple:
									snake_case_     =			n_codes_total
									snake_case_     =			n_codes_given

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






class 			snake_case_     (	__A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     List[Any]		    =       "bark"
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       True







			def __init__(  self				:       int							,     _UpperCamelCase				:       Dict = None							,     _UpperCamelCase				:       Dict = None							,     _UpperCamelCase				:       Dict = None							,     _UpperCamelCase				:       Dict = None							,     _UpperCamelCase				:       Any=0.02							,     **_UpperCamelCase				:       Optional[int]							,     )   ->Tuple:
									if semantic_config is None:
															snake_case_     =			{}
															logger.info('''semantic_config is None. initializing the semantic model with default values.'''							)

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

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

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

									snake_case_     =			BarkSemanticConfig(**_UpperCamelCase							)
									snake_case_     =			BarkCoarseConfig(**_UpperCamelCase							)
									snake_case_     =			BarkFineConfig(**_UpperCamelCase							)
									snake_case_     =			AutoConfig.for_model(**_UpperCamelCase							)

									snake_case_     =			initializer_range

									super().__init__(**_UpperCamelCase							)







			@classmethod
			def        snake_case__(  cls				:       Optional[int]							,     _UpperCamelCase				:       BarkSemanticConfig							,     _UpperCamelCase				:       BarkCoarseConfig							,     _UpperCamelCase				:       BarkFineConfig							,     _UpperCamelCase				:       AutoConfig							,     **_UpperCamelCase				:       List[str]							,     )   ->List[str]:
									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        snake_case__(  self				:       Any							)   ->Union[str, Any]:
									snake_case_     =			copy.deepcopy(self.__dict__							)

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

									snake_case_     =			self.__class__.model_type
									return output


lowerCAmelCase_ 			=			(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
lowerCAmelCase_ 			=			(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False

						# Initialization of sum and number of digits.
						snake_case_     =			0
						snake_case_     =			0
						snake_case_     =			n
						# Calculation of digits of the number
						while temp > 0:
												number_of_digits += 1
												temp //= 10
						# Dividing number into separate digits and find Armstrong number
						snake_case_     =			n
						while temp > 0:
												snake_case_     =			temp % 10
												total += rem**number_of_digits
												temp //= 10
						return n == total


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False

						# Init a "histogram" of the digits
						snake_case_     =			[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
						snake_case_     =			0
						snake_case_     =			0
						snake_case_     =			n
						while temp > 0:
												snake_case_,					snake_case_     =			divmod(SCREAMING_SNAKE_CASE__						,	10 )
												digit_histogram[rem] += 1
												digit_total += 1

						for cnt, i in zip(SCREAMING_SNAKE_CASE__						,	range(len(SCREAMING_SNAKE_CASE__ ) ) ):
												total += cnt * i**digit_total

						return n == total


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False
						snake_case_     =			len(str(SCREAMING_SNAKE_CASE__ ) )  # the power that all digits will be raised to
						# check if sum of each digit multiplied expo times is equal to number
						return n == sum(int(SCREAMING_SNAKE_CASE__ ) ** expo for i in str(SCREAMING_SNAKE_CASE__ ) )


def 						__SCREAMING_SNAKE_CASE      ():
						snake_case_     =			int(input('''Enter an integer to see if it is an Armstrong number: ''' ).strip() )
						print(F'''{num} is {'' if armstrong_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )
						print(F'''{num} is {'' if narcissistic_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )
						print(F'''{num} is {'' if pluperfect_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()