"""simple docstring"""






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


lowerCAmelCase__  :  List[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',
}

lowerCAmelCase__  :  List[Any]											=     '\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     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"bark_module"
					snake_case__       =			["past_key_values"]

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







					def __init__(    self       :     Optional[int]   ,lowerCamelCase__       :     Any=1_024   ,lowerCamelCase__       :     Union[str, Any]=10_048   ,lowerCamelCase__       :     Any=10_048   ,lowerCamelCase__       :     str=12   ,lowerCamelCase__       :     Union[str, Any]=12   ,lowerCamelCase__       :     Optional[int]=768   ,lowerCamelCase__       :     Tuple=0.0   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     List[str]=0.0_2   ,lowerCamelCase__       :     Optional[int]=True   ,**lowerCamelCase__       :     Dict   ,):
									UpperCAmelCase__					=							block_size
									UpperCAmelCase__					=							input_vocab_size
									UpperCAmelCase__					=							output_vocab_size
									UpperCAmelCase__					=							num_layers
									UpperCAmelCase__					=							num_heads
									UpperCAmelCase__					=							hidden_size
									UpperCAmelCase__					=							dropout
									UpperCAmelCase__					=							bias
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							initializer_range

									super().__init__(**lowerCamelCase__      )







					@classmethod
					def 		__lowerCAmelCase						(    cls       :     Optional[Any]   ,lowerCamelCase__       :     Union[str, os.PathLike]   ,lowerCamelCase__       :     Optional[Union[str, os.PathLike]] = None   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     Optional[Union[str, bool]] = None   ,lowerCamelCase__       :     str = "main"   ,**lowerCamelCase__       :     Union[str, Any]   ,):
									UpperCAmelCase__					=							cache_dir
									UpperCAmelCase__					=							force_download
									UpperCAmelCase__					=							local_files_only
									UpperCAmelCase__					=							revision

									cls._set_token_in_kwargs(lowerCamelCase__   ,lowerCamelCase__      )

									UpperCAmelCase__				,     UpperCAmelCase__					=							cls.get_config_dict(lowerCamelCase__   ,**lowerCamelCase__      )

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




					"""simple docstring"""

					snake_case__       =			"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	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"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	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"fine_acoustics"







					def __init__(    self       :     Union[str, Any]   ,lowerCamelCase__       :     Optional[int]=True   ,lowerCamelCase__       :     Optional[int]=8   ,lowerCamelCase__       :     int=1   ,**lowerCamelCase__       :     Any      ):
									UpperCAmelCase__					=							n_codes_total
									UpperCAmelCase__					=							n_codes_given

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






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"bark"
					snake_case__       =			True







					def __init__(    self       :     List[Any]   ,lowerCamelCase__       :     Dict = None   ,lowerCamelCase__       :     Dict = None   ,lowerCamelCase__       :     Dict = None   ,lowerCamelCase__       :     Dict = None   ,lowerCamelCase__       :     int=0.0_2   ,**lowerCamelCase__       :     List[Any]   ,):
									if semantic_config is None:
													UpperCAmelCase__					=							{}
													logger.info('semantic_config is None. initializing the semantic model with default values.'      )

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

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

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

									UpperCAmelCase__					=							BarkSemanticConfig(**lowerCamelCase__      )
									UpperCAmelCase__					=							BarkCoarseConfig(**lowerCamelCase__      )
									UpperCAmelCase__					=							BarkFineConfig(**lowerCamelCase__      )
									UpperCAmelCase__					=							AutoConfig.for_model(**lowerCamelCase__      )

									UpperCAmelCase__					=							initializer_range

									super().__init__(**lowerCamelCase__      )







					@classmethod
					def 		__lowerCAmelCase						(    cls       :     Dict   ,lowerCamelCase__       :     BarkSemanticConfig   ,lowerCamelCase__       :     BarkCoarseConfig   ,lowerCamelCase__       :     BarkFineConfig   ,lowerCamelCase__       :     AutoConfig   ,**lowerCamelCase__       :     int   ,):
									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__					=							copy.deepcopy(self.__dict__      )

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

									UpperCAmelCase__					=							self.__class__.model_type
									return output




"""simple docstring"""






from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase__  :  List[str]											=     '\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


lowerCAmelCase__  :  List[str]											=     '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


lowerCAmelCase__  :  Dict											=     '\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,   _KWARGS_DESCRIPTION    )
class     snake_case	(			datasets.Metric    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(
									        {
									            'predictions': datasets.Sequence(datasets.Value('int32'      )      ),
									            'references': datasets.Sequence(datasets.Value('int32'      )      ),
									        }
									        if self.config_name == 'multilabel'
									        else {
									            'predictions': datasets.Value('int32'      ),
									            'references': datasets.Value('int32'      ),
									        }      )   ,reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']   ,)







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     List[str]   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     Tuple=None      ):
									return {
									    "accuracy": float(
									        accuracy_score(lowerCamelCase__   ,lowerCamelCase__   ,normalize=lowerCamelCase__   ,sample_weight=lowerCamelCase__      )      )
									}



