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


_lowerCamelCase :       Optional[int]     					=    {
    '''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 :       List[str]     					=    '''
    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__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Any					     =      "bark_module"
	_UpperCAmelCase :	Any					     =      ["past_key_values"]

	_UpperCAmelCase :	Tuple					     =      {
	    "num_attention_heads": "num_heads",
	    "num_hidden_layers": "num_layers",
	    "vocab_size": "input_vocab_size",
	    "window_size": "block_size",
	}



	def __init__(						self			:					str    ,						lowercase			:					Optional[Any]=1_024    ,						lowercase			:					Any=10_048    ,						lowercase			:					str=10_048    ,						lowercase			:					List[Any]=12    ,						lowercase			:					Optional[int]=12    ,						lowercase			:					Any=768    ,						lowercase			:					int=0.0    ,						lowercase			:					Dict=True    ,						lowercase			:					List[str]=0.02    ,						lowercase			:					Union[str, Any]=True    ,						**lowercase			:					Any    ,						):


				'''simple docstring'''


				_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 				A							(						cls			:					Optional[int]    ,						lowercase			:					Union[str, os.PathLike]    ,						lowercase			:					Optional[Union[str, os.PathLike]] = None    ,						lowercase			:					bool = False    ,						lowercase			:					bool = False    ,						lowercase			:					Optional[Union[str, bool]] = None    ,						lowercase			:					str = "main"    ,						**lowercase			:					Dict    ,						):


				'''simple docstring'''


				_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"      )							,"\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      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	List[str]					     =      "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      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Optional[int]					     =      "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      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Union[str, Any]					     =      "fine_acoustics"



	def __init__(						self			:					List[str]    ,						lowercase			:					Union[str, Any]=True    ,						lowercase			:					List[Any]=8    ,						lowercase			:					int=1    ,						**lowercase			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    n_codes_total
				_snake_case						=    n_codes_given

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




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      "bark"
	_UpperCAmelCase :	Optional[Any]					     =      True



	def __init__(						self			:					Any    ,						lowercase			:					Dict = None    ,						lowercase			:					Dict = None    ,						lowercase			:					Dict = None    ,						lowercase			:					Dict = None    ,						lowercase			:					Optional[int]=0.02    ,						**lowercase			:					int    ,						):


				'''simple docstring'''


				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 				A							(						cls			:					Any    ,						lowercase			:					BarkSemanticConfig    ,						lowercase			:					BarkCoarseConfig    ,						lowercase			:					BarkFineConfig    ,						lowercase			:					AutoConfig    ,						**lowercase			:					Optional[Any]    ,						):


				'''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()    ,						**lowercase    ,						)





	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				_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 inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


_lowerCamelCase :       Optional[Any]     					=    [nn.LayerNorm]

_lowerCamelCase :       Optional[int]     					=    logging.get_logger(__name__)

_lowerCamelCase :       Optional[int]     					=    version.parse(version.parse(torch.__version__).base_version)

_lowerCamelCase :       Dict     					=    parsed_torch_version_base >= version.parse('''2.0''')
_lowerCamelCase :       str     					=    parsed_torch_version_base >= version.parse('''1.12''')
_lowerCamelCase :       str     					=    parsed_torch_version_base >= version.parse('''1.11''')
_lowerCamelCase :       Dict     					=    parsed_torch_version_base < version.parse('''1.11''')






def  a_	( __lowercase     :			Optional[int] ,      __lowercase     :			Optional[int] ,      __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      self     :			Optional[int]				)				->						Dict:
			from torch import _softmax_backward_data

			if is_torch_less_than_1_11:
						return _softmax_backward_data(__lowercase ,      __lowercase ,      parent.dim ,      self				)
			else:
						return _softmax_backward_data(__lowercase ,      __lowercase ,      parent.dim ,      self.dtype				)






def  a_	( __lowercase     :			nn.Linear ,      __lowercase     :			torch.LongTensor ,      __lowercase     :			int = 0				)				->						nn.Linear:
			_snake_case						=    index.to(layer.weight.device				)
			_snake_case						=    layer.weight.index_select(__lowercase ,      __lowercase				).clone().detach()
			if layer.bias is not None:
						if dim == 1:
									_snake_case						=    layer.bias.clone().detach()
						else:
									_snake_case						=    layer.bias[index].clone().detach()
			_snake_case						=    list(layer.weight.size()				)
			_snake_case						=    len(__lowercase				)
			_snake_case						=    nn.Linear(new_size[1] ,      new_size[0] ,      bias=layer.bias is not None				).to(layer.weight.device				)
			_snake_case						=    False
			new_layer.weight.copy_(W.contiguous()				)
			_snake_case						=    True
			if layer.bias is not None:
						_snake_case						=    False
						new_layer.bias.copy_(b.contiguous()				)
						_snake_case						=    True
			return new_layer




class      SCREAMING_SNAKE_CASE__  (			nn.Module      ):





	'''simple docstring'''



	def __init__(						self			:					Optional[Any]    ,						lowercase			:					List[str]    ,						lowercase			:					Tuple			):


				'''simple docstring'''


				super().__init__()
				_snake_case						=    nf
				_snake_case						=    nn.Parameter(torch.empty(lowercase    ,						lowercase			)			)
				_snake_case						=    nn.Parameter(torch.zeros(lowercase			)			)
				nn.init.normal_(self.weight    ,						std=0.02			)





	def 				A							(						self			:					List[Any]    ,						lowercase			:					int			):


				'''simple docstring'''


				_snake_case						=    x.size()[:-1] + (self.nf,)
				_snake_case						=    torch.addmm(self.bias    ,						x.view(-1    ,						x.size(-1			)			)    ,						self.weight			)
				_snake_case						=    x.view(lowercase			)
				return x






def  a_	( __lowercase     :			ConvaD ,      __lowercase     :			torch.LongTensor ,      __lowercase     :			int = 1				)				->						ConvaD:
			_snake_case						=    index.to(layer.weight.device				)
			_snake_case						=    layer.weight.index_select(__lowercase ,      __lowercase				).clone().detach()
			if dim == 0:
						_snake_case						=    layer.bias.clone().detach()
			else:
						_snake_case						=    layer.bias[index].clone().detach()
			_snake_case						=    list(layer.weight.size()				)
			_snake_case						=    len(__lowercase				)
			_snake_case						=    ConvaD(new_size[1] ,      new_size[0]				).to(layer.weight.device				)
			_snake_case						=    False
			new_layer.weight.copy_(W.contiguous()				)
			_snake_case						=    True
			_snake_case						=    False
			new_layer.bias.copy_(b.contiguous()				)
			_snake_case						=    True
			return new_layer






def  a_	( __lowercase     :			Union[nn.Linear, ConvaD] ,      __lowercase     :			torch.LongTensor ,      __lowercase     :			Optional[int] = None				)				->						Union[nn.Linear, ConvaD]:
			if isinstance(__lowercase ,      nn.Linear				):
						return prune_linear_layer(__lowercase ,      __lowercase ,      dim=0 if dim is None else dim				)
			elif isinstance(__lowercase ,      __lowercase				):
						return prune_convad_layer(__lowercase ,      __lowercase ,      dim=1 if dim is None else dim				)
			else:
						raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''				)






def  a_	( __lowercase     :			Callable[..., torch.Tensor] ,      __lowercase     :			int ,      __lowercase     :			int ,      *__lowercase     :			List[str]				)				->						torch.Tensor:
			assert len(__lowercase				) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

			# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
			_snake_case						=    len(inspect.signature(__lowercase				).parameters				)
			if num_args_in_forward_chunk_fn != len(__lowercase				):
						raise ValueError(
						    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__lowercase				)} input '''
						    'tensors are given'				)

			if chunk_size > 0:
						_snake_case						=    input_tensors[0].shape[chunk_dim]
						for input_tensor in input_tensors:
									if input_tensor.shape[chunk_dim] != tensor_shape:
												raise ValueError(
												    f'''All input tenors have to be of the same shape: {tensor_shape}, '''
												    f'''found shape {input_tensor.shape[chunk_dim]}'''				)

						if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
									raise ValueError(
									    f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
									    f'''size {chunk_size}'''				)

						_snake_case						=    input_tensors[0].shape[chunk_dim] // chunk_size

						# chunk input tensor into tuples
						_snake_case						=    tuple(input_tensor.chunk(__lowercase ,      dim=__lowercase				) for input_tensor in input_tensors				)
						# apply forward fn to every tuple
						_snake_case						=    tuple(forward_fn(*__lowercase				) for input_tensors_chunk in zip(*__lowercase				)				)
						# concatenate output at same dimension
						return torch.cat(__lowercase ,      dim=__lowercase				)

			return forward_fn(*__lowercase				)






def  a_	( __lowercase     :			List[int] ,      __lowercase     :			int ,      __lowercase     :			int ,      __lowercase     :			Set[int]				)				->						Tuple[Set[int], torch.LongTensor]:
			_snake_case						=    torch.ones(__lowercase ,      __lowercase				)
			_snake_case						=    set(__lowercase				) - already_pruned_heads  # Convert to set and remove already pruned heads
			for head in heads:
						# Compute how many pruned heads are before the head and move the index accordingly
						_snake_case						=    head - sum(1 if h < head else 0 for h in already_pruned_heads				)
						_snake_case						=    0
			_snake_case						=    mask.view(-1				).contiguous().eq(1				)
			_snake_case						=    torch.arange(len(__lowercase				)				)[mask].long()
			return heads, index






def  a_	( *__lowercase     :			Union[torch.Tensor, List[torch.Tensor]] ,      __lowercase     :			Optional[str] = None				)				->						Tuple[torch.Tensor, ...]:
			return torch.meshgrid(*__lowercase ,      indexing=__lowercase				)






def  a_	( __lowercase     :			torch.Tensor				)				->						Tuple[torch.device, int, int]:
			return tensor.device, storage_ptr(__lowercase				), storage_size(__lowercase				)