
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__				:  Optional[Any]       					=				logging.get_logger(__name__)


UpperCAmelCase__				:  Any       					=				{
    '''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__				:  List[str]       					=				'''\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_			(						__UpperCamelCase   ):




							'''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     ,						UpperCamelCase=10_24     ,						UpperCamelCase=1_00_48     ,						UpperCamelCase=1_00_48     ,						UpperCamelCase=12     ,						UpperCamelCase=12     ,						UpperCamelCase=7_68     ,						UpperCamelCase=0.0     ,						UpperCamelCase=True     ,						UpperCamelCase=0.02     ,						UpperCamelCase=True     ,						**UpperCamelCase     ,						) ->	Union[str, Any]:
										UpperCamelCase__    :		Optional[int]     =				block_size
										UpperCamelCase__    :		int     =				input_vocab_size
										UpperCamelCase__    :		List[Any]     =				output_vocab_size
										UpperCamelCase__    :		Optional[Any]     =				num_layers
										UpperCamelCase__    :		str     =				num_heads
										UpperCamelCase__    :		List[str]     =				hidden_size
										UpperCamelCase__    :		Tuple     =				dropout
										UpperCamelCase__    :		str     =				bias
										UpperCamelCase__    :		List[str]     =				use_cache
										UpperCamelCase__    :		Optional[int]     =				initializer_range

										super().__init__(**__lowerCamelCase)






							@classmethod
							def        lowerCAmelCase__ (				cls     ,						UpperCamelCase     ,						UpperCamelCase = None     ,						UpperCamelCase = False     ,						UpperCamelCase = False     ,						UpperCamelCase = None     ,						UpperCamelCase = "main"     ,						**UpperCamelCase     ,						) ->	"PretrainedConfig":
										UpperCamelCase__    :		Optional[int]     =				cache_dir
										UpperCamelCase__    :		Tuple     =				force_download
										UpperCamelCase__    :		Optional[int]     =				local_files_only
										UpperCamelCase__    :		Any     =				revision

										cls._set_token_in_kwargs(__lowerCamelCase     ,						__lowerCamelCase)

										UpperCamelCase__    :		str     =				cls.get_config_dict(__lowerCamelCase     ,						**__lowerCamelCase)

										# get the config dict if we are loading from Bark
										if config_dict.get('model_type') == "bark":
													UpperCamelCase__    :		Optional[int]     =				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_			(						__UpperCamelCase   ):




							'''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_			(						__UpperCamelCase   ):




							'''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_			(						__UpperCamelCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							"""fine_acoustics"""
							def __init__(				self     ,						UpperCamelCase=True     ,						UpperCamelCase=8     ,						UpperCamelCase=1     ,						**UpperCamelCase) ->	Optional[Any]:
										UpperCamelCase__    :		Tuple     =				n_codes_total
										UpperCamelCase__    :		Optional[int]     =				n_codes_given

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



class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							"""bark"""
							UpperCamelCase_					   =							True
							def __init__(				self     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase=0.02     ,						**UpperCamelCase     ,						) ->	Any:
										if semantic_config is None:
													UpperCamelCase__    :		List[Any]     =				{}
													logger.info('semantic_config is None. initializing the semantic model with default values.')

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

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

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

										UpperCamelCase__    :		Dict     =				BarkSemanticConfig(**__lowerCamelCase)
										UpperCamelCase__    :		Optional[int]     =				BarkCoarseConfig(**__lowerCamelCase)
										UpperCamelCase__    :		Union[str, Any]     =				BarkFineConfig(**__lowerCamelCase)
										UpperCamelCase__    :		Optional[int]     =				AutoConfig.for_model(**__lowerCamelCase)

										UpperCamelCase__    :		Any     =				initializer_range

										super().__init__(**__lowerCamelCase)
							@classmethod
							def        lowerCAmelCase__ (				cls     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						**UpperCamelCase     ,						) ->	Tuple:
										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        lowerCAmelCase__ (				self) ->	Any:
										UpperCamelCase__    :		Union[str, Any]     =				copy.deepcopy(self.__dict__)

										UpperCamelCase__    :		int     =				self.semantic_config.to_dict()
										UpperCamelCase__    :		Any     =				self.coarse_acoustics_config.to_dict()
										UpperCamelCase__    :		List[str]     =				self.fine_acoustics_config.to_dict()
										UpperCamelCase__    :		int     =				self.codec_config.to_dict()

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





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union



class 			UpperCamelCase_			(						enum.Enum   ):




							'''simple docstring'''

							UpperCamelCase_					   =							"""timm"""
							UpperCamelCase_					   =							"""transformers"""



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Dict:
			if stage_names is None:
						raise ValueError('Stage_names must be set for transformers backbones' )

			if out_features is not None:
						if not isinstance(__SCREAMING_SNAKE_CASE				,    (list,) ):
									raise ValueError(F"""out_features must be a list {type(__SCREAMING_SNAKE_CASE )}""" )
						if any(feat not in stage_names for feat in out_features ):
									raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}""" )

			if out_indices is not None:
						if not isinstance(__SCREAMING_SNAKE_CASE				,    (list, tuple) ):
									raise ValueError(F"""out_indices must be a list or tuple, got {type(__SCREAMING_SNAKE_CASE )}""" )
						if any(idx >= len(__SCREAMING_SNAKE_CASE ) for idx in out_indices ):
									raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}' )

			if out_features is not None and out_indices is not None:
						if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
									raise ValueError('out_features and out_indices should have the same length if both are set' )
						if out_features != [stage_names[idx] for idx in out_indices]:
									raise ValueError('out_features and out_indices should correspond to the same stages if both are set' )



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    )	->     List[Any]:
			if out_indices is None and out_features is None:
						UpperCamelCase__    :		List[Any]     =				[len(__SCREAMING_SNAKE_CASE ) - 1]
						UpperCamelCase__    :		Tuple     =				[stage_names[-1]]
			elif out_indices is None and out_features is not None:
						UpperCamelCase__    :		str     =				[stage_names.index(__SCREAMING_SNAKE_CASE ) for layer in out_features]
			elif out_features is None and out_indices is not None:
						UpperCamelCase__    :		int     =				[stage_names[idx] for idx in out_indices]
			return out_features, out_indices



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    )	->     Tuple[List[str], List[int]]:
			verify_out_features_out_indices(out_features=__SCREAMING_SNAKE_CASE				,    out_indices=__SCREAMING_SNAKE_CASE				,    stage_names=__SCREAMING_SNAKE_CASE )
			UpperCamelCase__   ,			UpperCamelCase__    :		str     =				_align_output_features_output_indices(
			    out_features=__SCREAMING_SNAKE_CASE				,    out_indices=__SCREAMING_SNAKE_CASE				,    stage_names=__SCREAMING_SNAKE_CASE )
			# Verify that the aligned out_features and out_indices are valid
			verify_out_features_out_indices(out_features=__SCREAMING_SNAKE_CASE				,    out_indices=__SCREAMING_SNAKE_CASE				,    stage_names=__SCREAMING_SNAKE_CASE )
			return output_features, output_indices



class 			UpperCamelCase_			:




							'''simple docstring'''

							UpperCamelCase_					   =							None
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	None:
										if getattr(self     ,						'_backbone'     ,						UpperCamelCase) is None:
													raise ValueError('self._backbone must be set before calling _init_timm_backbone')

										# These will diagree with the defaults for the transformers models e.g. for resnet50
										# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
										# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
										UpperCamelCase__    :		int     =				[stage['module'] for stage in self._backbone.feature_info.info]
										UpperCamelCase__    :		str     =				[stage['num_chs'] for stage in self._backbone.feature_info.info]
										UpperCamelCase__    :		Optional[int]     =				self._backbone.feature_info.out_indices
										UpperCamelCase__    :		Optional[int]     =				self._backbone.feature_info.module_name()

										# We verify the out indices and out features are valid
										verify_out_features_out_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				out_features, out_indices
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	None:
										UpperCamelCase__    :		List[str]     =				getattr(UpperCamelCase     ,						'stage_names')
										UpperCamelCase__    :		List[str]     =				getattr(UpperCamelCase     ,						'out_features'     ,						UpperCamelCase)
										UpperCamelCase__    :		Optional[Any]     =				getattr(UpperCamelCase     ,						'out_indices'     ,						UpperCamelCase)

										UpperCamelCase__    :		Tuple     =				stage_names
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[Any]     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=UpperCamelCase)
										# Number of channels for each stage. This is set in the transformer backbone model init
										UpperCamelCase__    :		Dict     =				None
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	None:
										UpperCamelCase__    :		List[Any]     =				config

										UpperCamelCase__    :		Dict     =				getattr(UpperCamelCase     ,						'use_timm_backbone'     ,						UpperCamelCase)
										UpperCamelCase__    :		List[str]     =				BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

										if self.backbone_type == BackboneType.TIMM:
													self._init_timm_backbone(UpperCamelCase)
										elif self.backbone_type == BackboneType.TRANSFORMERS:
													self._init_transformers_backbone(UpperCamelCase)
										else:
													raise ValueError(F"""backbone_type {self.backbone_type} not supported.""")
							@property
							def        lowerCAmelCase__ (				self) ->	List[str]:
										return self._out_features
							@out_features.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	int:
										UpperCamelCase__   ,			UpperCamelCase__    :		str     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
							@property
							def        lowerCAmelCase__ (				self) ->	List[Any]:
										return self._out_indices
							@out_indices.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	int:
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
							@property
							def        lowerCAmelCase__ (				self) ->	List[str]:
										# the current backbones will output the number of channels for each stage
										# even if that stage is not in the out_features list.
										return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names)}
							@property
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										return [self.out_feature_channels[name] for name in self.out_features]
							def        lowerCAmelCase__ (				self     ,						*UpperCamelCase     ,						**UpperCamelCase) ->	str:
										UpperCamelCase__    :		List[Any]     =				dict(inspect.signature(self.forward).parameters)
										UpperCamelCase__    :		Any     =				{k: v for k, v in kwargs.items() if k in signature}
										return self(*UpperCamelCase     ,						**UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						) ->	Union[str, Any]:
										raise NotImplementedError('This method should be implemented by the derived class.')






							def        lowerCAmelCase__ (				self) ->	int:
										UpperCamelCase__    :		Any     =				super().to_dict()
										UpperCamelCase__    :		Optional[Any]     =				output.pop('_out_features')
										UpperCamelCase__    :		Any     =				output.pop('_out_indices')
										return output



class 			UpperCamelCase_			:




							'''simple docstring'''

							@property
							def        lowerCAmelCase__ (				self) ->	Tuple:
										return self._out_features
							@out_features.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	Tuple:
										UpperCamelCase__   ,			UpperCamelCase__    :		Union[str, Any]     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
							@property
							def        lowerCAmelCase__ (				self) ->	Optional[int]:
										return self._out_indices
							@out_indices.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	int:
										UpperCamelCase__   ,			UpperCamelCase__    :		str     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)






							def        lowerCAmelCase__ (				self) ->	Tuple:
										UpperCamelCase__    :		List[Any]     =				super().to_dict()
										UpperCamelCase__    :		Optional[int]     =				output.pop('_out_features')
										UpperCamelCase__    :		Tuple     =				output.pop('_out_indices')
										return output



