






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


__snake_case			:			Union[str, Any]			   =   logging.get_logger(__name__)


__snake_case			:			Tuple			   =   {
    """suno/bark-small""": """https://huggingface.co/suno/bark-small/resolve/main/config.json""",
    """suno/bark""": """https://huggingface.co/suno/bark/resolve/main/config.json""",
}

__snake_case			:			Tuple			   =   """
    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 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				Dict            =    '''bark_module'''
						_SCREAMING_SNAKE_CASE		:				Union[str, Any]            =    ['''past_key_values''']

						_SCREAMING_SNAKE_CASE		:				Any            =    {
						    '''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=0.02   ,  _UpperCamelCase=True   ,  **_UpperCamelCase   ,  ):






												"""simple docstring"""
												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 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase = None   ,  _UpperCamelCase = False   ,  _UpperCamelCase = False   ,  _UpperCamelCase = None   ,  _UpperCamelCase = "main"   ,  **_UpperCamelCase   ,  ):






												"""simple docstring"""
												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 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				List[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 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				List[Any]            =    '''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 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				List[Any]            =    '''fine_acoustics'''






						def __init__(		self   ,  _UpperCamelCase=True   ,  _UpperCamelCase=8   ,  _UpperCamelCase=1   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  n_codes_total
												lowerCAmelCase__			  =  n_codes_given

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




class 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				Optional[int]            =    '''bark'''
						_SCREAMING_SNAKE_CASE		:				int            =    True






						def __init__(		self   ,  _UpperCamelCase = None   ,  _UpperCamelCase = None   ,  _UpperCamelCase = None   ,  _UpperCamelCase = None   ,  _UpperCamelCase=0.02   ,  **_UpperCamelCase   ,  ):






												"""simple docstring"""
												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 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  **_UpperCamelCase   ,  ):






												"""simple docstring"""
												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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												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













def 		_UpperCamelCase      (			UpperCamelCase_				:    float			,				UpperCamelCase_				:    float			,				UpperCamelCase_				:    float						)     ->      list[int]:

						"""simple docstring"""



						if hue < 0 or hue > 360:
												raise Exception('hue should be between 0 and 360'						)

						if saturation < 0 or saturation > 1:
												raise Exception('saturation should be between 0 and 1'						)

						if value < 0 or value > 1:
												raise Exception('value should be between 0 and 1'						)

						lowerCAmelCase__			  =  value * saturation
						lowerCAmelCase__			  =  hue / 60
						lowerCAmelCase__			  =  chroma * (1 - abs(hue_section % 2 - 1						))
						lowerCAmelCase__			  =  value - chroma

						if hue_section >= 0 and hue_section <= 1:
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
						elif hue_section > 1 and hue_section <= 2:
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
						elif hue_section > 2 and hue_section <= 3:
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
						elif hue_section > 3 and hue_section <= 4:
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
						elif hue_section > 4 and hue_section <= 5:
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
						else:
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)

						return [red, green, blue]







def 		_UpperCamelCase      (			UpperCamelCase_				:    int			,				UpperCamelCase_				:    int			,				UpperCamelCase_				:    int						)     ->      list[float]:

						"""simple docstring"""



						if red < 0 or red > 255:
												raise Exception('red should be between 0 and 255'						)

						if green < 0 or green > 255:
												raise Exception('green should be between 0 and 255'						)

						if blue < 0 or blue > 255:
												raise Exception('blue should be between 0 and 255'						)

						lowerCAmelCase__			  =  red / 255
						lowerCAmelCase__			  =  green / 255
						lowerCAmelCase__			  =  blue / 255
						lowerCAmelCase__			  =  max(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  value - min(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  0 if value == 0 else chroma / value

						if chroma == 0:
												lowerCAmelCase__			  =  0.0
						elif value == float_red:
												lowerCAmelCase__			  =  60 * (0 + (float_green - float_blue) / chroma)
						elif value == float_green:
												lowerCAmelCase__			  =  60 * (2 + (float_blue - float_red) / chroma)
						else:
												lowerCAmelCase__			  =  60 * (4 + (float_red - float_green) / chroma)

						lowerCAmelCase__			  =  (hue + 360) % 360

						return [hue, saturation, value]







def 		_UpperCamelCase      (			UpperCamelCase_				:    list[float]			,				UpperCamelCase_				:    list[float]						)     ->      bool:

						"""simple docstring"""



						lowerCAmelCase__			  =  abs(hsv_a[0] - hsv_a[0]						) < 0.2
						lowerCAmelCase__			  =  abs(hsv_a[1] - hsv_a[1]						) < 0.002
						lowerCAmelCase__			  =  abs(hsv_a[2] - hsv_a[2]						) < 0.002

						return check_hue and check_saturation and check_value





