'''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


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


__SCREAMING_SNAKE_CASE	:					Optional[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''',
}

__SCREAMING_SNAKE_CASE	:					Union[str, 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      lowerCamelCase_     (__lowerCAmelCase   ):
						'''simple docstring'''

						__UpperCamelCase:  int      =						'''bark_module'''
						__UpperCamelCase:  Optional[int]      =						['''past_key_values''']

						__UpperCamelCase:  Any      =						{
						    '''num_attention_heads''': '''num_heads''',
						    '''num_hidden_layers''': '''num_layers''',
						    '''vocab_size''': '''input_vocab_size''',
						    '''window_size''': '''block_size''',
						}




						def __init__(	self						:    Any						, A						:    str=1024						, A						:    Optional[int]=10048						, A						:    Optional[Any]=10048						, A						:    Dict=12						, A						:    Optional[int]=12						, A						:    Union[str, Any]=768						, A						:    int=0.0						, A						:    Dict=True						, A						:    List[Any]=0.02						, A						:    Any=True						, **A						:    int						, ):
													_UpperCAmelCase					:  int											=   block_size
													_UpperCAmelCase					:  Optional[int]											=   input_vocab_size
													_UpperCAmelCase					:  Tuple											=   output_vocab_size
													_UpperCAmelCase					:  Dict											=   num_layers
													_UpperCAmelCase					:  Any											=   num_heads
													_UpperCAmelCase					:  Dict											=   hidden_size
													_UpperCAmelCase					:  Dict											=   dropout
													_UpperCAmelCase					:  int											=   bias
													_UpperCAmelCase					:  Optional[Any]											=   use_cache
													_UpperCAmelCase					:  Union[str, Any]											=   initializer_range

													super().__init__(**__SCREAMING_SNAKE_CASE	)





						@classmethod
						def 					_A     (	cls						:    Any						, A						:    Tuple						, A						:    Union[str, Any] = None						, A						:    List[Any] = False						, A						:    Union[str, Any] = False						, A						:    Dict = None						, A						:    Any = "main"						, **A						:    List[Any]						, ):
													_UpperCAmelCase					:  Any											=   cache_dir
													_UpperCAmelCase					:  Any											=   force_download
													_UpperCAmelCase					:  Any											=   local_files_only
													_UpperCAmelCase					:  List[Any]											=   revision

													cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE						, __SCREAMING_SNAKE_CASE	)

													_UpperCAmelCase				,  _UpperCAmelCase					:  Union[str, Any]											=   cls.get_config_dict(__SCREAMING_SNAKE_CASE						, **__SCREAMING_SNAKE_CASE	)

													# get the config dict if we are loading from Bark
													if config_dict.get("model_type"	) == "bark":
																				_UpperCAmelCase					:  Dict											=   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(__SCREAMING_SNAKE_CASE						, **__SCREAMING_SNAKE_CASE	)

@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      lowerCamelCase_     (__lowerCAmelCase   ):
						'''simple docstring'''

						__UpperCamelCase:  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      lowerCamelCase_     (__lowerCAmelCase   ):
						'''simple docstring'''

						__UpperCamelCase:  Dict      =						'''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      lowerCamelCase_     (__lowerCAmelCase   ):
						'''simple docstring'''

						__UpperCamelCase:  Optional[int]      =						'''fine_acoustics'''




						def __init__(	self						:    Optional[Any]						, A						:    List[Any]=True						, A						:    str=8						, A						:    Optional[int]=1						, **A						:    Optional[Any]	):
													_UpperCAmelCase					:  Union[str, Any]											=   n_codes_total
													_UpperCAmelCase					:  Any											=   n_codes_given

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

class      lowerCamelCase_     (__lowerCAmelCase   ):
						'''simple docstring'''

						__UpperCamelCase:  List[str]      =						'''bark'''
						__UpperCamelCase:  Any      =						True




						def __init__(	self						:    Optional[Any]						, A						:    str = None						, A						:    str = None						, A						:    Optional[Any] = None						, A						:    List[str] = None						, A						:    List[Any]=0.02						, **A						:    List[Any]						, ):




													if semantic_config is None:
																				_UpperCAmelCase					:  List[str]											=   {}
																				logger.info("semantic_config is None. initializing the semantic model with default values."	)

													if coarse_acoustics_config is None:
																				_UpperCAmelCase					:  Optional[Any]											=   {}
																				logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."	)

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

													if codec_config is None:
																				_UpperCAmelCase					:  List[str]											=   {}
																				logger.info("codec_config is None. initializing the codec model with default values."	)

													_UpperCAmelCase					:  Optional[int]											=   BarkSemanticConfig(**__SCREAMING_SNAKE_CASE	)
													_UpperCAmelCase					:  int											=   BarkCoarseConfig(**__SCREAMING_SNAKE_CASE	)
													_UpperCAmelCase					:  Optional[int]											=   BarkFineConfig(**__SCREAMING_SNAKE_CASE	)
													_UpperCAmelCase					:  int											=   AutoConfig.for_model(**__SCREAMING_SNAKE_CASE	)

													_UpperCAmelCase					:  Optional[int]											=   initializer_range

													super().__init__(**__SCREAMING_SNAKE_CASE	)




						@classmethod
						def 					_A     (	cls						:    List[Any]						, A						:    str						, A						:    Any						, A						:    Dict						, A						:    Optional[int]						, **A						:    List[str]						, ):
													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()						, **__SCREAMING_SNAKE_CASE						, )





						def 					_A     (	self						:    str	):
													_UpperCAmelCase					:  Union[str, Any]											=   copy.deepcopy(self.__dict__	)

													_UpperCAmelCase					:  Optional[Any]											=   self.semantic_config.to_dict()
													_UpperCAmelCase					:  Dict											=   self.coarse_acoustics_config.to_dict()
													_UpperCAmelCase					:  str											=   self.fine_acoustics_config.to_dict()
													_UpperCAmelCase					:  str											=   self.codec_config.to_dict()

													_UpperCAmelCase					:  List[str]											=   self.__class__.model_type
													return output


'''simple docstring'''




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter

class      lowerCamelCase_     (TensorFormatter[Mapping, np.ndarray, Mapping]   ):
      '''simple docstring'''

      def __init__(	self						:    str						, A						:    List[str]=None						, **A						:    Optional[Any]	):
             super().__init__(features=A	)
             _UpperCAmelCase					:  Tuple											=   np_array_kwargs




      def 					_A     (	self						:    str						, A						:    Tuple	):
             if isinstance(A						, A	):
                    if column and all(
                        isinstance(A						, np.ndarray	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                           return np.stack(A	)
                    else:
                           # don't use np.array(column, dtype=object)
                           # since it fails in certain cases
                           # see https://stackoverflow.com/q/51005699
                           _UpperCAmelCase					:  List[str]											=   np.empty(len(A	)						, dtype=A	)
                           _UpperCAmelCase					:  int											=   column
                           return out
             return column




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             if isinstance(A						, (str, bytes, type(A	))	):
                    return value
             elif isinstance(A						, (np.character, np.ndarray)	) and np.issubdtype(value.dtype						, np.character	):
                    return value
             elif isinstance(A						, np.number	):
                    return value

             _UpperCAmelCase					:  List[str]											=   {}

             if isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.integer	):
                    _UpperCAmelCase					:  str											=   {"dtype": np.intaa}
             elif isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.floating	):
                    _UpperCAmelCase					:  int											=   {"dtype": np.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(A						, PIL.Image.Image	):
                           return np.asarray(A						, **self.np_array_kwargs	)

             return np.asarray(A						, **{**default_dtype, **self.np_array_kwargs}	)




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             # support for torch, tf, jax etc.
             if config.TORCH_AVAILABLE and "torch" in sys.modules:
                    import torch

                    if isinstance(A						, torch.Tensor	):
                           return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
             if hasattr(A						, "__array__"	) and not isinstance(A						, (np.ndarray, np.character, np.number)	):
                    _UpperCAmelCase					:  Dict											=   data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(A						, np.ndarray	):
                    if data_struct.dtype == object:
                           return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             if isinstance(A						, (list, tuple)	):
                    return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             return self._tensorize(A	)




      def 					_A     (	self						:    List[Any]						, A						:    dict	):
             return map_nested(self._recursive_tensorize						, A						, map_list=A	)




      def 					_A     (	self						:    List[str]						, A						:    pa.Table	):
             _UpperCAmelCase					:  str											=   self.numpy_arrow_extractor().extract_row(A	)
             _UpperCAmelCase					:  Dict											=   self.python_features_decoder.decode_row(A	)
             return self.recursive_tensorize(A	)




      def 					_A     (	self						:    str						, A						:    pa.Table	):
             _UpperCAmelCase					:  List[str]											=   self.numpy_arrow_extractor().extract_column(A	)
             _UpperCAmelCase					:  Optional[Any]											=   self.python_features_decoder.decode_column(A						, pa_table.column_names[0]	)
             _UpperCAmelCase					:  Tuple											=   self.recursive_tensorize(A	)
             _UpperCAmelCase					:  Tuple											=   self._consolidate(A	)
             return column





      def 					_A     (	self						:    Union[str, Any]						, A						:    pa.Table	):
             _UpperCAmelCase					:  Any											=   self.numpy_arrow_extractor().extract_batch(A	)
             _UpperCAmelCase					:  Any											=   self.python_features_decoder.decode_batch(A	)
             _UpperCAmelCase					:  List[str]											=   self.recursive_tensorize(A	)
             for column_name in batch:
                    _UpperCAmelCase					:  Optional[Any]											=   self._consolidate(batch[column_name]	)
             return batch

