"""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									=	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      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    List[str]    				=		'''bark_module'''
						lowerCAmelCase__	:    Tuple    				=		['''past_key_values''']

						lowerCAmelCase__	:    Optional[int]    				=		{
						    '''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=0.0_2		,__UpperCAmelCase=True		,**__UpperCAmelCase		,)       ->					List[Any]:
													A__						  =				block_size
													A__						  =				input_vocab_size
													A__						  =				output_vocab_size
													A__						  =				num_layers
													A__						  =				num_heads
													A__						  =				hidden_size
													A__						  =				dropout
													A__						  =				bias
													A__						  =				use_cache
													A__						  =				initializer_range

													super().__init__(**__UpperCAmelCase  )





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = False		,__UpperCAmelCase = False		,__UpperCAmelCase = None		,__UpperCAmelCase = "main"		,**__UpperCAmelCase		,)       ->					int:
													A__						  =				cache_dir
													A__						  =				force_download
													A__						  =				local_files_only
													A__						  =				revision

													cls._set_token_in_kwargs(__UpperCAmelCase		,__UpperCAmelCase  )

													A__						  =				cls.get_config_dict(__UpperCAmelCase		,**__UpperCAmelCase  )

													# get the config dict if we are loading from Bark
													if config_dict.get('model_type'  ) == "bark":
																				A__						  =				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      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    int    				=		'''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__(      __A			):
						lowerCAmelCase__	:    Tuple    				=		'''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__(      __A			):
						lowerCAmelCase__	:    int    				=		'''fine_acoustics'''





						def __init__(							self		,__UpperCAmelCase=True		,__UpperCAmelCase=8		,__UpperCAmelCase=1		,**__UpperCAmelCase  )       ->					Tuple:
													A__						  =				n_codes_total
													A__						  =				n_codes_given

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






class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    Tuple    				=		'''bark'''
						lowerCAmelCase__	:    Any    				=		True





						def __init__(							self		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase=0.0_2		,**__UpperCAmelCase		,)       ->					Union[str, Any]:







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

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

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

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

													A__						  =				BarkSemanticConfig(**__UpperCAmelCase  )
													A__						  =				BarkCoarseConfig(**__UpperCAmelCase  )
													A__						  =				BarkFineConfig(**__UpperCAmelCase  )
													A__						  =				AutoConfig.for_model(**__UpperCAmelCase  )

													A__						  =				initializer_range

													super().__init__(**__UpperCAmelCase  )





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase		,)       ->					Optional[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()		,**__UpperCAmelCase		,)





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				copy.deepcopy(self.__dict__  )

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

													A__						  =				self.__class__.model_type
													return output













"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase_						:					Optional[int]      				=			"""\
@inproceedings{ramshaw-marcus-1995-text,
    title = \"Text Chunking using Transformation-Based Learning\",
    author = \"Ramshaw, Lance  and
      Marcus, Mitch\",
    booktitle = \"Third Workshop on Very Large Corpora\",
    year = \"1995\",
    url = \"https://www.aclweb.org/anthology/W95-0107\",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
"""

UpperCamelCase_						:					Any      				=			"""\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

UpperCamelCase_						:					int      				=			"""
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,	_KWARGS_DESCRIPTION    )
class   _lowercase			(  datasets.Metric    ):


      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            return datasets.MetricInfo(
                description=_DESCRIPTION				, citation=_CITATION				, homepage='''https://github.com/chakki-works/seqeval'''				, inputs_description=_KWARGS_DESCRIPTION				, features=datasets.Features(
                    {
                        '''predictions''': datasets.Sequence(datasets.Value('''string'''				, id='''label'''    )				, id='''sequence'''    ),
                        '''references''': datasets.Sequence(datasets.Value('''string'''				, id='''label'''    )				, id='''sequence'''    ),
                    }    )				, codebase_urls=['''https://github.com/chakki-works/seqeval''']				, reference_urls=['''https://github.com/chakki-works/seqeval''']				, )







      def _UpperCamelCase    (     self					:						List[Any]				, a					:						List[str]				, a					:						Optional[Any]				, a					:						bool = False				, a					:						Optional[str] = None				, a					:						Optional[str] = None				, a					:						Optional[List[int]] = None				, a					:						Union[str, int] = "warn"				, ):





            """simple docstring"""







            if scheme is not None:
                  try:
                        __snake_case   :     List[str]	      =importlib.import_module('''seqeval.scheme'''    )
                        __snake_case   :     int	      =getattr(a				, a    )
                  except AttributeError:
                        raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''    )
            __snake_case   :     Optional[Any]	      =classification_report(
                y_true=a				, y_pred=a				, suffix=a				, output_dict=a				, scheme=a				, mode=a				, sample_weight=a				, zero_division=a				, )
            report.pop('''macro avg'''    )
            report.pop('''weighted avg'''    )
            __snake_case   :     Union[str, Any]	      =report.pop('''micro avg'''    )

            __snake_case   :     str	      ={
                type_name: {
                    '''precision''': score['''precision'''],
                    '''recall''': score['''recall'''],
                    '''f1''': score['''f1-score'''],
                    '''number''': score['''support'''],
                }
                for type_name, score in report.items()
            }
            __snake_case   :     str	      =overall_score['''precision''']
            __snake_case   :     int	      =overall_score['''recall''']
            __snake_case   :     str	      =overall_score['''f1-score''']
            __snake_case   :     Union[str, Any]	      =accuracy_score(y_true=a				, y_pred=a    )

            return scores


