
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_  =	"\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     _SCREAMING_SNAKE_CASE							(		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 :		List[str]					,      snake_case_ :		Tuple=1024					,      snake_case_ :		List[Any]=1_0048					,      snake_case_ :		Any=1_0048					,      snake_case_ :		Union[str, Any]=12					,      snake_case_ :		Optional[int]=12					,      snake_case_ :		str=768					,      snake_case_ :		Optional[int]=0.0					,      snake_case_ :		int=True					,      snake_case_ :		List[Any]=0.02					,      snake_case_ :		str=True					,      **snake_case_ :		Tuple					,      ):


								"""simple docstring"""

								A     :  Optional[Any]   =				block_size
								A     :  int   =				input_vocab_size
								A     :  int   =				output_vocab_size
								A     :  List[str]   =				num_layers
								A     :  Optional[int]   =				num_heads
								A     :  List[str]   =				hidden_size
								A     :  List[str]   =				dropout
								A     :  List[Any]   =				bias
								A     :  Dict   =				use_cache
								A     :  Union[str, Any]   =				initializer_range

								super().__init__(**snake_case_							)






				@classmethod
				def        _UpperCAmelCase						(		cls :		int					,      snake_case_ :		Union[str, os.PathLike]					,      snake_case_ :		Optional[Union[str, os.PathLike]] = None					,      snake_case_ :		bool = False					,      snake_case_ :		bool = False					,      snake_case_ :		Optional[Union[str, bool]] = None					,      snake_case_ :		str = "main"					,      **snake_case_ :		List[str]					,      ):


								"""simple docstring"""

								A     :  str   =				cache_dir
								A     :  Optional[int]   =				force_download
								A     :  Dict   =				local_files_only
								A     :  Optional[Any]   =				revision

								cls._set_token_in_kwargs(snake_case_					,      snake_case_							)

								A ,       A     :  int   =				cls.get_config_dict(snake_case_					,      **snake_case_							)

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



@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     _SCREAMING_SNAKE_CASE							(		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     _SCREAMING_SNAKE_CASE							(		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     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     'fine_acoustics'





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


								"""simple docstring"""

								A     :  Optional[Any]   =				n_codes_total
								A     :  List[str]   =				n_codes_given

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



class     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     'bark'
				lowerCamelCase_       =     True





				def __init__(		self :		List[Any]					,      snake_case_ :		Dict = None					,      snake_case_ :		Dict = None					,      snake_case_ :		Dict = None					,      snake_case_ :		Dict = None					,      snake_case_ :		Dict=0.02					,      **snake_case_ :		List[Any]					,      ):


								"""simple docstring"""

								if semantic_config is None:
												A     :  List[str]   =				{}
												logger.info('''semantic_config is None. initializing the semantic model with default values.'''							)

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

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

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

								A     :  Tuple   =				BarkSemanticConfig(**snake_case_							)
								A     :  List[str]   =				BarkCoarseConfig(**snake_case_							)
								A     :  str   =				BarkFineConfig(**snake_case_							)
								A     :  Dict   =				AutoConfig.for_model(**snake_case_							)

								A     :  List[str]   =				initializer_range

								super().__init__(**snake_case_							)





				@classmethod
				def        _UpperCAmelCase						(		cls :		Optional[int]					,      snake_case_ :		BarkSemanticConfig					,      snake_case_ :		BarkCoarseConfig					,      snake_case_ :		BarkFineConfig					,      snake_case_ :		AutoConfig					,      **snake_case_ :		str					,      ):


								"""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()					,      **snake_case_					,      )






				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								A     :  Tuple   =				copy.deepcopy(self.__dict__							)

								A     :  Optional[int]   =				self.semantic_config.to_dict()
								A     :  Optional[Any]   =				self.coarse_acoustics_config.to_dict()
								A     :  Dict   =				self.fine_acoustics_config.to_dict()
								A     :  Optional[int]   =				self.codec_config.to_dict()

								A     :  Union[str, Any]   =				self.__class__.model_type
								return output

UpperCamelCase_  =	10_00





def 							_lowerCamelCase			(					lowerCamelCase_:  float		):



				'''simple docstring'''







				return x * x





def 							_lowerCamelCase			(					lowerCamelCase_:  Tuple				,	lowerCamelCase_:  float				,	lowerCamelCase_:  float				,	lowerCamelCase_:  int = 4		):



				'''simple docstring'''







				assert callable(
				    lowerCamelCase_		), f"""the function(object) passed should be callable your input : {function}"""
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""a should be float or integer your input : {a}"""
				assert isinstance(function(lowerCamelCase_		)				,	(float, int)		), (
				    "the function should return integer or float return type of your function, "
				    f"""{type(lowerCamelCase_		)}"""
				)
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""b should be float or integer your input : {b}"""
				assert (
				    isinstance(lowerCamelCase_				,	lowerCamelCase_		) and precision > 0
				), f"""precision should be positive integer your input : {precision}"""

				# just applying the formula of simpson for approximate integration written in
				# mentioned article in first comment of this file and above this function

				A     :  Tuple   =				(b - a) / N_STEPS
				A     :  List[str]   =				function(lowerCamelCase_		) + function(lowerCamelCase_		)

				for i in range(1				,	lowerCamelCase_		):
								A     :  int   =				a + h * i
								result += function(lowerCamelCase_		) * (4 if i % 2 else 2)

				result *= h / 3
				return round(lowerCamelCase_				,	lowerCamelCase_		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()