





'''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


A__       :     Optional[int]          =logging.get_logger(__name__)


A__       :     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''',
}

A__       :     List[Any]          ='''
    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  (    snake_case_     ):
      _lowercase:    List[Any]		  =     '''bark_module'''
      _lowercase:    Optional[Any]		  =     ['''past_key_values''']

      _lowercase:    Tuple		  =     {
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
          '''vocab_size''': '''input_vocab_size''',
          '''window_size''': '''block_size''',
      }




      def __init__(    self    :				Dict    ,    __snake_case    :				Optional[int]=10_24    ,    __snake_case    :				List[Any]=1_00_48    ,    __snake_case    :				Tuple=1_00_48    ,    __snake_case    :				Optional[int]=12    ,    __snake_case    :				str=12    ,    __snake_case    :				Optional[int]=7_68    ,    __snake_case    :				Dict=0.0    ,    __snake_case    :				List[Any]=True    ,    __snake_case    :				int=0.02    ,    __snake_case    :				int=True    ,    **__snake_case    :				Any    ,    )       ->   Tuple:
            _lowerCAmelCase							  =							block_size
            _lowerCAmelCase							  =							input_vocab_size
            _lowerCAmelCase							  =							output_vocab_size
            _lowerCAmelCase							  =							num_layers
            _lowerCAmelCase							  =							num_heads
            _lowerCAmelCase							  =							hidden_size
            _lowerCAmelCase							  =							dropout
            _lowerCAmelCase							  =							bias
            _lowerCAmelCase							  =							use_cache
            _lowerCAmelCase							  =							initializer_range

            super().__init__(**__snake_case						)




      @classmethod
      def        lowercase__					(    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    :				str    ,    )       ->   "PretrainedConfig":
            _lowerCAmelCase							  =							cache_dir
            _lowerCAmelCase							  =							force_download
            _lowerCAmelCase							  =							local_files_only
            _lowerCAmelCase							  =							revision

            cls._set_token_in_kwargs(__snake_case    ,    __snake_case						)

            _lowerCAmelCase  ,							_lowerCAmelCase							  =							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":
                  _lowerCAmelCase							  =							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'''     )							,	'''
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''							,	)
class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    str		  =     '''semantic'''




@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkCoarseConfig'''							,	model='''BarkCoarseModel'''     )							,	'''
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''							,	)
class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    List[str]		  =     '''coarse_acoustics'''




@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkFineConfig'''							,	model='''BarkFineModel'''     )							,	'''
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''							,	)
class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    Dict		  =     '''fine_acoustics'''




      def __init__(    self    :				str    ,    __snake_case    :				Union[str, Any]=True    ,    __snake_case    :				Union[str, Any]=8    ,    __snake_case    :				Tuple=1    ,    **__snake_case    :				str						)       ->   str:
            _lowerCAmelCase							  =							n_codes_total
            _lowerCAmelCase							  =							n_codes_given

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




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    List[str]		  =     '''bark'''
      _lowercase:    List[Any]		  =     True




      def __init__(    self    :				str    ,    __snake_case    :				Dict = None    ,    __snake_case    :				Dict = None    ,    __snake_case    :				Dict = None    ,    __snake_case    :				Dict = None    ,    __snake_case    :				List[Any]=0.02    ,    **__snake_case    :				List[Any]    ,    )       ->   List[str]:
            if semantic_config is None:
                  _lowerCAmelCase							  =							{}
                  logger.info("""semantic_config is None. initializing the semantic model with default values."""						)

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

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

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

            _lowerCAmelCase							  =							BarkSemanticConfig(**__snake_case						)
            _lowerCAmelCase							  =							BarkCoarseConfig(**__snake_case						)
            _lowerCAmelCase							  =							BarkFineConfig(**__snake_case						)
            _lowerCAmelCase							  =							AutoConfig.for_model(**__snake_case						)

            _lowerCAmelCase							  =							initializer_range

            super().__init__(**__snake_case						)




      @classmethod
      def        lowercase__					(    cls    :				Optional[Any]    ,    __snake_case    :				BarkSemanticConfig    ,    __snake_case    :				BarkCoarseConfig    ,    __snake_case    :				BarkFineConfig    ,    __snake_case    :				AutoConfig    ,    **__snake_case    :				Tuple    ,    )       ->   Union[str, Any]:
            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        lowercase__					(    self    :				Tuple						)       ->   Optional[int]:
            _lowerCAmelCase							  =							copy.deepcopy(self.__dict__						)

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

            _lowerCAmelCase							  =							self.__class__.model_type
            return output













'''simple docstring'''






import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      from transformers.models.electra.modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
      )




class 				UpperCAmelCase  (    unittest.TestCase     ):




      def __init__(    self    :				str    ,    __snake_case    :				str    ,    __snake_case    :				str=13    ,    __snake_case    :				Optional[Any]=7    ,    __snake_case    :				str=True    ,    __snake_case    :				Any=True    ,    __snake_case    :				List[str]=True    ,    __snake_case    :				Any=True    ,    __snake_case    :				List[str]=99    ,    __snake_case    :				Dict=24    ,    __snake_case    :				Any=32    ,    __snake_case    :				List[str]=5    ,    __snake_case    :				Any=4    ,    __snake_case    :				Optional[Any]=37    ,    __snake_case    :				Union[str, Any]="gelu"    ,    __snake_case    :				Dict=0.1    ,    __snake_case    :				int=0.1    ,    __snake_case    :				Dict=5_12    ,    __snake_case    :				List[Any]=16    ,    __snake_case    :				Dict=2    ,    __snake_case    :				str=0.02    ,    __snake_case    :				Any=4    ,    )       ->   Union[str, Any]:
            _lowerCAmelCase							  =							parent
            _lowerCAmelCase							  =							batch_size
            _lowerCAmelCase							  =							seq_length
            _lowerCAmelCase							  =							is_training
            _lowerCAmelCase							  =							use_attention_mask
            _lowerCAmelCase							  =							use_token_type_ids
            _lowerCAmelCase							  =							use_labels
            _lowerCAmelCase							  =							vocab_size
            _lowerCAmelCase							  =							hidden_size
            _lowerCAmelCase							  =							embedding_size
            _lowerCAmelCase							  =							num_hidden_layers
            _lowerCAmelCase							  =							num_attention_heads
            _lowerCAmelCase							  =							intermediate_size
            _lowerCAmelCase							  =							hidden_act
            _lowerCAmelCase							  =							hidden_dropout_prob
            _lowerCAmelCase							  =							attention_probs_dropout_prob
            _lowerCAmelCase							  =							max_position_embeddings
            _lowerCAmelCase							  =							type_vocab_size
            _lowerCAmelCase							  =							type_sequence_label_size
            _lowerCAmelCase							  =							initializer_range
            _lowerCAmelCase							  =							num_choices




      def        lowercase__					(    self    :				Optional[Any]						)       ->   Optional[Any]:
            _lowerCAmelCase							  =							ids_tensor([self.batch_size, self.seq_length]    ,    self.vocab_size						)

            _lowerCAmelCase							  =							None
            if self.use_attention_mask:
                  _lowerCAmelCase							  =							random_attention_mask([self.batch_size, self.seq_length]						)

            _lowerCAmelCase							  =							None
            if self.use_token_type_ids:
                  _lowerCAmelCase							  =							ids_tensor([self.batch_size, self.seq_length]    ,    self.type_vocab_size						)

            _lowerCAmelCase							  =							ElectraConfig(
                vocab_size=self.vocab_size    ,    hidden_size=self.hidden_size    ,    embedding_size=self.embedding_size    ,    num_hidden_layers=self.num_hidden_layers    ,    num_attention_heads=self.num_attention_heads    ,    intermediate_size=self.intermediate_size    ,    hidden_act=self.hidden_act    ,    hidden_dropout_prob=self.hidden_dropout_prob    ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,    max_position_embeddings=self.max_position_embeddings    ,    type_vocab_size=self.type_vocab_size    ,    initializer_range=self.initializer_range    ,    )

            return config, input_ids, token_type_ids, attention_mask




      def        lowercase__					(    self    :				Any						)       ->   Optional[int]:
            _lowerCAmelCase							  =							self.prepare_config_and_inputs()
            _lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase							  =							config_and_inputs
            _lowerCAmelCase							  =							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
            return config, inputs_dict




@require_flax
class 				UpperCAmelCase  (    snake_case_							,	unittest.TestCase     ):
      _lowercase:    str		  =     True

      _lowercase:    Optional[Any]		  =     (
          (
              FlaxElectraModel,
              FlaxElectraForCausalLM,
              FlaxElectraForMaskedLM,
              FlaxElectraForPreTraining,
              FlaxElectraForTokenClassification,
              FlaxElectraForQuestionAnswering,
              FlaxElectraForMultipleChoice,
              FlaxElectraForSequenceClassification,
          )
          if is_flax_available()
          else ()
      )




      def        lowercase__					(    self    :				Tuple						)       ->   Optional[int]:
            _lowerCAmelCase							  =							FlaxElectraModelTester(self						)




      @slow
      def        lowercase__					(    self    :				List[Any]						)       ->   str:
            for model_class_name in self.all_model_classes:
                  if model_class_name == FlaxElectraForMaskedLM:
                        _lowerCAmelCase							  =							model_class_name.from_pretrained("""google/electra-small-generator"""						)
                  else:
                        _lowerCAmelCase							  =							model_class_name.from_pretrained("""google/electra-small-discriminator"""						)
                  _lowerCAmelCase							  =							model(np.ones((1, 1)						)						)
                  self.assertIsNotNone(__snake_case						)






