

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_	     =     '''
    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      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       Optional[Any]						      =    "bark_module"
			SCREAMING_SNAKE_CASE					:       List[str]						      =    ["past_key_values"]

			SCREAMING_SNAKE_CASE					:       int						      =    {
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			    "vocab_size": "input_vocab_size",
			    "window_size": "block_size",
			}







			def __init__(					self			:    Any							,       _UpperCamelCase			:    Dict=1_0_2_4							,       _UpperCamelCase			:    Any=1_0_0_4_8							,       _UpperCamelCase			:    int=1_0_0_4_8							,       _UpperCamelCase			:    int=1_2							,       _UpperCamelCase			:    int=1_2							,       _UpperCamelCase			:    Dict=7_6_8							,       _UpperCamelCase			:    List[Any]=0.0							,       _UpperCamelCase			:    Any=True							,       _UpperCamelCase			:    int=0.02							,       _UpperCamelCase			:    Tuple=True							,       **_UpperCamelCase			:    Dict							,       )				->Dict:
									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__(**_UpperCamelCase							)







			@classmethod
			def     snake_case__(					cls			:    int							,       _UpperCamelCase			:    Union[str, os.PathLike]							,       _UpperCamelCase			:    Optional[Union[str, os.PathLike]] = None							,       _UpperCamelCase			:    bool = False							,       _UpperCamelCase			:    bool = False							,       _UpperCamelCase			:    Optional[Union[str, bool]] = None							,       _UpperCamelCase			:    str = "main"							,       **_UpperCamelCase			:    int							,       )				->"PretrainedConfig":
									snake_case_       =     cache_dir
									snake_case_       =     force_download
									snake_case_       =     local_files_only
									snake_case_       =     revision

									cls._set_token_in_kwargs(_UpperCamelCase							,       _UpperCamelCase							)

									snake_case_,   snake_case_       =     cls.get_config_dict(_UpperCamelCase							,       **_UpperCamelCase							)

									# 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(_UpperCamelCase							,       **_UpperCamelCase							)






@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      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       Optional[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      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       Union[str, Any]						      =    "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      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    "fine_acoustics"







			def __init__(					self			:    int							,       _UpperCamelCase			:    List[str]=True							,       _UpperCamelCase			:    List[str]=8							,       _UpperCamelCase			:    Tuple=1							,       **_UpperCamelCase			:    List[str]							)				->Dict:
									snake_case_       =     n_codes_total
									snake_case_       =     n_codes_given

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






class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[Any]						      =    "bark"
			SCREAMING_SNAKE_CASE					:       Dict						      =    True







			def __init__(					self			:    Optional[Any]							,       _UpperCamelCase			:    Dict = None							,       _UpperCamelCase			:    Dict = None							,       _UpperCamelCase			:    Dict = None							,       _UpperCamelCase			:    Dict = None							,       _UpperCamelCase			:    Union[str, Any]=0.02							,       **_UpperCamelCase			:    Any							,       )				->Dict:
									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(**_UpperCamelCase							)
									snake_case_       =     BarkCoarseConfig(**_UpperCamelCase							)
									snake_case_       =     BarkFineConfig(**_UpperCamelCase							)
									snake_case_       =     AutoConfig.for_model(**_UpperCamelCase							)

									snake_case_       =     initializer_range

									super().__init__(**_UpperCamelCase							)







			@classmethod
			def     snake_case__(					cls			:    Optional[Any]							,       _UpperCamelCase			:    BarkSemanticConfig							,       _UpperCamelCase			:    BarkCoarseConfig							,       _UpperCamelCase			:    BarkFineConfig							,       _UpperCamelCase			:    AutoConfig							,       **_UpperCamelCase			:    Dict							,       )				->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()							,       **_UpperCamelCase							,       )







			def     snake_case__(					self			:    Union[str, Any]							)				->Tuple:
									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








import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None	):
						snake_case_       =     None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype	) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												snake_case_       =     weight.view(torch.intaa	)
												snake_case_       =     '''bfloat16'''
						snake_case_       =     weight.cpu().numpy()
						snake_case_       =     os.path.join(SCREAMING_SNAKE_CASE__  ,		F'''{weight_name}.dat'''	)
						if index is not None:
												if dtype is None:
																		snake_case_       =     str(array.dtype	)
												snake_case_       =     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												snake_case_       =     array[None]
						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=array.dtype  ,		mode='''w+'''  ,		shape=array.shape	)
						snake_case_       =     array[:]
						file_array.flush()
						return index


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tuple(weight_info['''shape''']	)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												snake_case_       =     (1,)

						snake_case_       =     weight_info['''dtype''']
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												snake_case_       =     '''int16'''

						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=SCREAMING_SNAKE_CASE__  ,		shape=SCREAMING_SNAKE_CASE__  ,		mode='''r'''	)

						if len(weight_info['''shape''']	) == 0:
												snake_case_       =     weight[0]
						snake_case_       =     torch.tensor(SCREAMING_SNAKE_CASE__	)
						if weight_info["dtype"] == "bfloat16":
												snake_case_       =     weight.view(torch.bfloataa	)

						return weight


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						if index is None or len(SCREAMING_SNAKE_CASE__	) == 0:
												# Nothing to save
												return

						snake_case_       =     os.path.join(SCREAMING_SNAKE_CASE__  ,		'''index.json'''	)
						if os.path.isfile(SCREAMING_SNAKE_CASE__	):
												with open(SCREAMING_SNAKE_CASE__  ,		'''r'''  ,		encoding='''utf-8'''	) as f:
																		snake_case_       =     json.load(SCREAMING_SNAKE_CASE__	)
						else:
												snake_case_       =     {}
						current_index.update(SCREAMING_SNAKE_CASE__	)

						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''  ,		encoding='''utf-8'''	) as f:
												json.dump(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		indent=2	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						os.makedirs(SCREAMING_SNAKE_CASE__  ,		exist_ok=SCREAMING_SNAKE_CASE__	)
						snake_case_       =     {}
						for name, parameter in state_dict.items():
												snake_case_       =     offload_weight(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		index=SCREAMING_SNAKE_CASE__	)

						# Update index
						save_offload_index(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)





class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    Optional[Any]							,       _UpperCamelCase			:    Mapping							,       _UpperCamelCase			:    str							)				->int:
									snake_case_       =     dataset
									snake_case_       =     prefix







			def __getitem__(					self			:    List[Any]							,       _UpperCamelCase			:    Any							)				->Tuple:
									return self.dataset[f'''{self.prefix}{key}''']







			def __iter__(					self			:    Optional[int]							)				->int:
									return iter([key for key in self.dataset if key.startswith(self.prefix							)]							)







			def __len__(					self			:    Dict							)				->int:
									return len(self.dataset							)






class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    str							,       _UpperCamelCase			:    Dict[str, torch.Tensor] = None							,       _UpperCamelCase			:    Optional[Union[str, os.PathLike]] = None							,       _UpperCamelCase			:    Mapping = None							,       _UpperCamelCase			:    Tuple=None							,       )				->Dict:
									if state_dict is None and save_folder is None:
															raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''							)

									snake_case_       =     {} if state_dict is None else state_dict
									snake_case_       =     save_folder
									if index is None and save_folder is not None:
															with open(os.path.join(_UpperCamelCase							,       '''index.json'''							)							) as f:
																					snake_case_       =     json.load(_UpperCamelCase							)
									snake_case_       =     {} if index is None else index
									snake_case_       =     list(self.state_dict.keys()							)
									self.all_keys.extend([key for key in self.index if key not in self.all_keys]							)
									snake_case_       =     device







			def __getitem__(					self			:    int							,       _UpperCamelCase			:    str							)				->Union[str, Any]:
									# State dict gets priority
									if key in self.state_dict:
															return self.state_dict[key]
									snake_case_       =     self.index[key]
									if weight_info.get('''safetensors_file'''							) is not None:
															if not is_safetensors_available():
																					raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''							)

															from safetensors import safe_open

															snake_case_       =     '''cpu''' if self.device is None else self.device
															with safe_open(weight_info['''safetensors_file''']							,       framework='''pt'''							,       device=_UpperCamelCase							) as f:
																					snake_case_       =     f.get_tensor(weight_info.get('''weight_name'''							,       _UpperCamelCase							)							)

															if "dtype" in weight_info:
																					return tensor.to(getattr(_UpperCamelCase							,       weight_info['''dtype''']							)							)
															else:
																					return tensor

									snake_case_       =     os.path.join(self.save_folder							,       f'''{key}.dat'''							)
									return load_offloaded_weight(_UpperCamelCase							,       _UpperCamelCase							)







			def __iter__(					self			:    int							)				->List[Any]:
									return iter(self.all_keys							)







			def __len__(					self			:    Optional[Any]							)				->Optional[Any]:
									return len(self.all_keys							)






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + '''.'''	)
												    }	)
						return result