

'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_						   =			logging.get_logger(__name__)

lowerCAmelCase_						   =			{
    "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
}







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''mega'''

				def __init__(					self,	lowercase_=30522,	lowercase_=128,	lowercase_=4,	lowercase_=256,	lowercase_=16,	lowercase_=True,	lowercase_=64,	lowercase_=False,	lowercase_=-1,	lowercase_=None,	lowercase_=True,	lowercase_="scalenorm",	lowercase_=True,	lowercase_="silu",	lowercase_="softmax",	lowercase_=0.1,	lowercase_=0.1,	lowercase_=0.1,	lowercase_=False,	lowercase_=True,	lowercase_=256,	lowercase_=True,	lowercase_=0.1,	lowercase_=2048,	lowercase_=False,	lowercase_=2,	lowercase_=0.02,	lowercase_=0.2,	lowercase_=0.02,	lowercase_=1.0,	lowercase_=1,	lowercase_=0,	lowercase_=2,	lowercase_="rotary",	lowercase_=None,	lowercase_=True,	lowercase_=True,	**lowercase_,	)						->       List[Any]:
					super().__init__(pad_token_id=lowercase_,	bos_token_id=lowercase_,	eos_token_id=lowercase_,	**lowercase_   )

					snake_case							 =				vocab_size
					snake_case							 =				hidden_size
					snake_case							 =				num_hidden_layers
					snake_case							 =				activation
					snake_case							 =				attention_activation
					snake_case							 =				intermediate_size
					snake_case							 =				ema_projection_size
					snake_case							 =				bidirectional
					snake_case							 =				shared_representation_size
					snake_case							 =				use_chunking
					snake_case							 =				chunk_size
					snake_case							 =				truncation
					snake_case							 =				normalize_before_mega
					snake_case							 =				normalization_type
					snake_case							 =				norm_affine
					snake_case							 =				dropout_prob
					snake_case							 =				hidden_dropout_prob
					snake_case							 =				attention_probs_dropout_prob
					snake_case							 =				use_feature_dropout
					snake_case							 =				use_normalized_ffn
					snake_case							 =				nffn_hidden_size
					snake_case							 =				normalize_before_ffn
					snake_case							 =				nffn_activation_dropout_prob
					snake_case							 =				max_positions
					snake_case							 =				add_token_type_embeddings
					snake_case							 =				type_vocab_size
					snake_case							 =				initializer_range
					snake_case							 =				ema_delta_alpha_range
					snake_case							 =				ema_beta_range
					snake_case							 =				ema_gamma_omega_range
					snake_case							 =				relative_positional_bias
					snake_case							 =				use_cache
					snake_case							 =				classifier_dropout
					snake_case							 =				add_lm_hidden_dense_layer
					snake_case							 =				1  # not used but required by Hugging Face







class lowerCamelCase      (						__lowerCAmelCase							):

				@property
				def 				_lowerCamelCase						(					self   )						->       Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						snake_case							 =				{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						snake_case							 =				{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					    ]   )








'''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_						   =			"\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 lowerCamelCase      (						__lowerCAmelCase							):
				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,	lowercase_=1024,	lowercase_=10048,	lowercase_=10048,	lowercase_=12,	lowercase_=12,	lowercase_=768,	lowercase_=0.0,	lowercase_=True,	lowercase_=0.02,	lowercase_=True,	**lowercase_,	)						->       List[str]:
					snake_case							 =				block_size
					snake_case							 =				input_vocab_size
					snake_case							 =				output_vocab_size
					snake_case							 =				num_layers
					snake_case							 =				num_heads
					snake_case							 =				hidden_size
					snake_case							 =				dropout
					snake_case							 =				bias
					snake_case							 =				use_cache
					snake_case							 =				initializer_range

					super().__init__(**lowercase_   )






				@classmethod
				def 				_lowerCamelCase						(					cls,	lowercase_,	lowercase_ = None,	lowercase_ = False,	lowercase_ = False,	lowercase_ = None,	lowercase_ = "main",	**lowercase_,	)						->       "PretrainedConfig":
					snake_case							 =				cache_dir
					snake_case							 =				force_download
					snake_case							 =				local_files_only
					snake_case							 =				revision

					cls._set_token_in_kwargs(lowercase_,	lowercase_   )

					snake_case , snake_case							 =				cls.get_config_dict(lowercase_,	**lowercase_   )

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







@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 lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''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 lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''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 lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''fine_acoustics'''

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

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







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''bark'''
				snake_case_           =   True

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

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

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

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

					snake_case							 =				BarkSemanticConfig(**lowercase_   )
					snake_case							 =				BarkCoarseConfig(**lowercase_   )
					snake_case							 =				BarkFineConfig(**lowercase_   )
					snake_case							 =				AutoConfig.for_model(**lowercase_   )

					snake_case							 =				initializer_range

					super().__init__(**lowercase_   )

				@classmethod
				def 				_lowerCamelCase						(					cls,	lowercase_,	lowercase_,	lowercase_,	lowercase_,	**lowercase_,	)						->       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(),	**lowercase_,	)






				def 				_lowerCamelCase						(					self   )						->       Dict:
					snake_case							 =				copy.deepcopy(self.__dict__   )

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

					snake_case							 =				self.__class__.model_type
					return output





