

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 		UpperCAmelCase__      (      _lowerCAmelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	"bark_module"
			UpperCamelCase      						=	["past_key_values"]

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




			def __init__(       self							:			Union[str, Any]	,	a_							:			Tuple=10_24	,	a_							:			List[str]=1_00_48	,	a_							:			str=1_00_48	,	a_							:			int=12	,	a_							:			int=12	,	a_							:			Optional[Any]=7_68	,	a_							:			List[Any]=0.0	,	a_							:			Tuple=True	,	a_							:			List[Any]=0.0_2	,	a_							:			Union[str, Any]=True	,	**a_							:			Dict	,	):
					'''simple docstring'''





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

					super().__init__(**_lowerCamelCase			)




			@classmethod
			def 			snake_case__					(       cls							:			Tuple	,	a_							:			Union[str, os.PathLike]	,	a_							:			Optional[Union[str, os.PathLike]] = None	,	a_							:			bool = False	,	a_							:			bool = False	,	a_							:			Optional[Union[str, bool]] = None	,	a_							:			str = "main"	,	**a_							:			Tuple	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     cache_dir
					__UpperCAmelCase   :					List[Any]													=     force_download
					__UpperCAmelCase   :					Optional[Any]													=     local_files_only
					__UpperCAmelCase   :					Any													=     revision

					cls._set_token_in_kwargs(_lowerCamelCase	,	_lowerCamelCase			)

					__UpperCAmelCase							, __UpperCAmelCase   :					Tuple													=     cls.get_config_dict(_lowerCamelCase	,	**_lowerCamelCase			)

					# get the config dict if we are loading from Bark
					if config_dict.get('''model_type'''			) == "bark":
							__UpperCAmelCase   :					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 		UpperCAmelCase__      (      _lowerCAmelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	"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 		UpperCAmelCase__      (      _lowerCAmelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	"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 		UpperCAmelCase__      (      _lowerCAmelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	"fine_acoustics"




			def __init__(       self							:			List[str]	,	a_							:			Optional[Any]=True	,	a_							:			Dict=8	,	a_							:			List[str]=1	,	**a_							:			Union[str, Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     n_codes_total
					__UpperCAmelCase   :					str													=     n_codes_given

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

class 		UpperCAmelCase__      (      _lowerCAmelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	"bark"
			UpperCamelCase      						=	True




			def __init__(       self							:			List[str]	,	a_							:			Dict = None	,	a_							:			Dict = None	,	a_							:			Dict = None	,	a_							:			Dict = None	,	a_							:			Optional[Any]=0.0_2	,	**a_							:			Dict	,	):
					'''simple docstring'''





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

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

					if fine_acoustics_config is None:
							__UpperCAmelCase   :					Union[str, Any]													=     {}
							logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''			)

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

					__UpperCAmelCase   :					Tuple													=     BarkSemanticConfig(**_lowerCamelCase			)
					__UpperCAmelCase   :					List[Any]													=     BarkCoarseConfig(**_lowerCamelCase			)
					__UpperCAmelCase   :					Optional[Any]													=     BarkFineConfig(**_lowerCamelCase			)
					__UpperCAmelCase   :					Any													=     AutoConfig.for_model(**_lowerCamelCase			)

					__UpperCAmelCase   :					Optional[Any]													=     initializer_range

					super().__init__(**_lowerCamelCase			)




			@classmethod
			def 			snake_case__					(       cls							:			Tuple	,	a_							:			BarkSemanticConfig	,	a_							:			BarkCoarseConfig	,	a_							:			BarkFineConfig	,	a_							:			AutoConfig	,	**a_							:			List[Any]	,	):
					'''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()	,	**_lowerCamelCase	,	)




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     copy.deepcopy(self.__dict__			)

					__UpperCAmelCase   :					str													=     self.semantic_config.to_dict()
					__UpperCAmelCase   :					Union[str, Any]													=     self.coarse_acoustics_config.to_dict()
					__UpperCAmelCase   :					str													=     self.fine_acoustics_config.to_dict()
					__UpperCAmelCase   :					Tuple													=     self.codec_config.to_dict()

					__UpperCAmelCase   :					Optional[Any]													=     self.__class__.model_type
					return output










from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__A             =TypeVar("T", bound=bool)

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			List[Any]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					T													=     value
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None




			@property
			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					return self._value




			@staticmethod
			def 			snake_case__					(       a_							:			RandomizedHeapNode[T] | None	,	a_							:			RandomizedHeapNode[T] | None			):
					'''simple docstring'''





					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__UpperCAmelCase							, __UpperCAmelCase   :					int													=     roota, roota

					if random.choice([True, False]			):
							__UpperCAmelCase							, __UpperCAmelCase   :					Union[str, Any]													=     roota.right, roota.left

					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(roota.left	,	a_			)

					return roota

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			Iterable[T] | None = ()			):
					'''simple docstring'''





					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None

					if data:
							for item in data:
									self.insert(a_			)




			def 			snake_case__					(       self							:			List[str]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(self._root	,	RandomizedHeapNode(a_			)			)




			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     self.top()

					if self._root is None:
							return None

					__UpperCAmelCase   :					Tuple													=     RandomizedHeapNode.merge(self._root.left	,	self._root.right			)

					return result




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					if not self._root:
							raise IndexError('''Can\'t get top element for the empty heap.'''			)
					return self._root.value




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					str													=     None




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     []
					while self:
							result.append(self.pop()			)

					return result




			def __bool__(       self							:			List[Any]			):
					'''simple docstring'''





					return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





