
"""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 		= logging.get_logger(__name__)


__A 		= {
    '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 		= '\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__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''bark_module'''
			lowerCamelCase						      =	['''past_key_values''']

			lowerCamelCase						      =	{
			    '''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  ,  )				->  Optional[int]:
				_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__(**__UpperCAmelCase						)



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

				cls._set_token_in_kwargs(__UpperCAmelCase  ,  __UpperCAmelCase						)

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

				# 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(__UpperCAmelCase  ,  **__UpperCAmelCase						)







@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__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''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__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''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__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''fine_acoustics'''



			def __init__(							self  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=8  ,  __UpperCAmelCase=1  ,  **__UpperCAmelCase						)				->  List[str]:
				_lowerCAmelCase			=n_codes_total
				_lowerCAmelCase			=n_codes_given

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







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''bark'''
			lowerCamelCase						      =	True



			def __init__(							self  ,  __UpperCAmelCase = None  ,  __UpperCAmelCase = None  ,  __UpperCAmelCase = None  ,  __UpperCAmelCase = None  ,  __UpperCAmelCase=0.0_2  ,  **__UpperCAmelCase  ,  )				->  Any:
				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(**__UpperCAmelCase						)
				_lowerCAmelCase			=BarkCoarseConfig(**__UpperCAmelCase						)
				_lowerCAmelCase			=BarkFineConfig(**__UpperCAmelCase						)
				_lowerCAmelCase			=AutoConfig.for_model(**__UpperCAmelCase						)

				_lowerCAmelCase			=initializer_range

				super().__init__(**__UpperCAmelCase						)



			@classmethod
			def       _lowerCAmelCase	(							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       _lowerCAmelCase	(							self						)				->  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"""


from typing import Dict, Optional, Tuple

import torch



def 					_lowerCamelCase(__UpperCamelCase )       ->				torch.Tensor:
	_lowerCAmelCase			=boundaries[1] - boundaries[0]
	_lowerCAmelCase			=boundaries + step / 2
	_lowerCAmelCase			=torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1 )]					,				dim=0 )
	return bin_centers



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				)       ->				Tuple[torch.Tensor, torch.Tensor]:
	_lowerCAmelCase			=_calculate_bin_centers(__UpperCamelCase )
	return (
	    torch.sum(aligned_distance_error_probs * bin_centers					,				dim=-1 ),
	    bin_centers[-1],
	)



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase = 31					,				__UpperCamelCase = 64					,				**__UpperCamelCase					,				)       ->				Dict[str, torch.Tensor]:
	_lowerCAmelCase			=torch.linspace(0					,				__UpperCamelCase					,				steps=(no_bins - 1)					,				device=logits.device )

	_lowerCAmelCase			=torch.nn.functional.softmax(__UpperCamelCase					,				dim=-1 )
	_lowerCAmelCase		,							_lowerCAmelCase			=_calculate_expected_aligned_error(
	    alignment_confidence_breaks=__UpperCamelCase					,				aligned_distance_error_probs=__UpperCamelCase					,				)

	return {
	    "aligned_confidence_probs": aligned_confidence_probs,
	    "predicted_aligned_error": predicted_aligned_error,
	    "max_predicted_aligned_error": max_predicted_aligned_error,
	}



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase = None					,				__UpperCamelCase = 31					,				__UpperCamelCase = 64					,				__UpperCamelCase = 1E-8					,				**__UpperCamelCase					,				)       ->				torch.Tensor:
	if residue_weights is None:
		_lowerCAmelCase			=logits.new_ones(logits.shape[-2] )

	_lowerCAmelCase			=torch.linspace(0					,				__UpperCamelCase					,				steps=(no_bins - 1)					,				device=logits.device )

	_lowerCAmelCase			=_calculate_bin_centers(__UpperCamelCase )
	torch.sum(__UpperCamelCase )
	_lowerCAmelCase			=logits.shape[-2]
	_lowerCAmelCase			=max(__UpperCamelCase					,				19 )

	_lowerCAmelCase			=1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

	_lowerCAmelCase			=torch.nn.functional.softmax(__UpperCamelCase					,				dim=-1 )

	_lowerCAmelCase			=1.0 / (1 + (bin_centers**2) / (da**2))
	_lowerCAmelCase			=torch.sum(probs * tm_per_bin					,				dim=-1 )

	_lowerCAmelCase			=residue_weights / (eps + residue_weights.sum())
	_lowerCAmelCase			=torch.sum(predicted_tm_term * normed_residue_mask					,				dim=-1 )

	_lowerCAmelCase			=per_alignment * residue_weights

	_lowerCAmelCase			=(weighted == torch.max(__UpperCamelCase )).nonzero()[0]
	return per_alignment[tuple(__UpperCamelCase )]



