

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


lowercase     :       str        =       logging.get_logger(__name__)


lowercase     :       Union[str, 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",
}

lowercase     :       Tuple        =       "\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 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				List[str]											=							'bark_module'
						lowercase  :				Any											=							['past_key_values']

						lowercase  :				Tuple											=							{
						    '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 , )							->	Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[int]								=      block_size
									__UpperCamelCase    :    int								=      input_vocab_size
									__UpperCamelCase    :    List[Any]								=      output_vocab_size
									__UpperCamelCase    :    Optional[Any]								=      num_layers
									__UpperCamelCase    :    str								=      num_heads
									__UpperCamelCase    :    List[str]								=      hidden_size
									__UpperCamelCase    :    Tuple								=      dropout
									__UpperCamelCase    :    str								=      bias
									__UpperCamelCase    :    List[str]								=      use_cache
									__UpperCamelCase    :    Optional[int]								=      initializer_range

									super().__init__(**__lowerCamelCase    )




						@classmethod
						def __lowerCamelCase					(				cls , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = "main" , **__UpperCamelCase , )							->	"PretrainedConfig":




									'''simple docstring'''


									__UpperCamelCase    :    Optional[int]								=      cache_dir
									__UpperCamelCase    :    Tuple								=      force_download
									__UpperCamelCase    :    Optional[int]								=      local_files_only
									__UpperCamelCase    :    Any								=      revision

									cls._set_token_in_kwargs(__lowerCamelCase , __lowerCamelCase    )

									__UpperCamelCase    :    str								=      cls.get_config_dict(__lowerCamelCase , **__lowerCamelCase    )

									# get the config dict if we are loading from Bark
									if config_dict.get("model_type"    ) == "bark":
												__UpperCamelCase    :    Optional[int]								=      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(__lowerCamelCase , **__lowerCamelCase    )







@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__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				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 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				Tuple											=							'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__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				List[str]											=							'fine_acoustics'

						def __init__(				self , __UpperCamelCase=True , __UpperCamelCase=8 , __UpperCamelCase=1 , **__UpperCamelCase    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Tuple								=      n_codes_total
									__UpperCamelCase    :    Optional[int]								=      n_codes_given

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







class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				List[Any]											=							'bark'
						lowercase  :				Optional[int]											=							True

						def __init__(				self , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase=0.02 , **__UpperCamelCase , )							->	Any:




									'''simple docstring'''


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

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

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

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

									__UpperCamelCase    :    Dict								=      BarkSemanticConfig(**__lowerCamelCase    )
									__UpperCamelCase    :    Optional[int]								=      BarkCoarseConfig(**__lowerCamelCase    )
									__UpperCamelCase    :    Union[str, Any]								=      BarkFineConfig(**__lowerCamelCase    )
									__UpperCamelCase    :    Optional[int]								=      AutoConfig.for_model(**__lowerCamelCase    )

									__UpperCamelCase    :    Any								=      initializer_range

									super().__init__(**__lowerCamelCase    )

						@classmethod
						def __lowerCamelCase					(				cls , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase , )							->	Tuple:




									'''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() , **__lowerCamelCase , )




						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      copy.deepcopy(self.__dict__    )

									__UpperCamelCase    :    int								=      self.semantic_config.to_dict()
									__UpperCamelCase    :    Any								=      self.coarse_acoustics_config.to_dict()
									__UpperCamelCase    :    List[str]								=      self.fine_acoustics_config.to_dict()
									__UpperCamelCase    :    int								=      self.codec_config.to_dict()

									__UpperCamelCase    :    Union[str, Any]								=      self.__class__.model_type
									return output


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowercase     :       str        =       logging.get_logger(__name__)


if is_vision_available():
					import PIL
class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				str											=							['pixel_values']

						def __init__(				self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = True , __UpperCamelCase = False , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )							->	None:




									'''simple docstring'''


									super().__init__(**__UpperCamelCase    )

									__UpperCamelCase    :    Optional[Any]								=      size if size is not None else {"height": 25_60, "width": 19_20}
									if isinstance(__UpperCamelCase , (tuple, list)    ):
												# The previous feature extractor size parameter was in (width, height) format
												__UpperCamelCase    :    Dict								=      size[::-1]
									__UpperCamelCase    :    Optional[int]								=      get_size_dict(__UpperCamelCase    )

									__UpperCamelCase    :    List[Any]								=      do_resize
									__UpperCamelCase    :    Tuple								=      size
									__UpperCamelCase    :    Optional[int]								=      resample
									__UpperCamelCase    :    Optional[Any]								=      do_thumbnail
									__UpperCamelCase    :    Any								=      do_align_long_axis
									__UpperCamelCase    :    str								=      do_pad
									__UpperCamelCase    :    str								=      do_rescale
									__UpperCamelCase    :    Union[str, Any]								=      rescale_factor
									__UpperCamelCase    :    Any								=      do_normalize
									__UpperCamelCase    :    List[Any]								=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__UpperCamelCase    :    Dict								=      image_std if image_std is not None else IMAGENET_STANDARD_STD

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None    )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      get_image_size(__UpperCamelCase    )
									__UpperCamelCase							,				__UpperCamelCase    :    str								=      size["height"], size["width"]

									if (output_width < output_height and input_width > input_height) or (
									    output_width > output_height and input_width < input_height
									):
												__UpperCamelCase    :    str								=      np.rotaa(__UpperCamelCase , 3    )

									if data_format is not None:
												__UpperCamelCase    :    str								=      to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase    )

									return image

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False , __UpperCamelCase = None , )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Union[str, Any]								=      size["height"], size["width"]
									__UpperCamelCase							,				__UpperCamelCase    :    str								=      get_image_size(__UpperCamelCase    )

									__UpperCamelCase    :    List[str]								=      output_width - input_width
									__UpperCamelCase    :    Union[str, Any]								=      output_height - input_height

									if random_padding:
												__UpperCamelCase    :    Any								=      np.random.randint(low=0 , high=delta_height + 1    )
												__UpperCamelCase    :    Union[str, Any]								=      np.random.randint(low=0 , high=delta_width + 1    )
									else:
												__UpperCamelCase    :    Any								=      delta_height // 2
												__UpperCamelCase    :    Dict								=      delta_width // 2

									__UpperCamelCase    :    Optional[int]								=      delta_height - pad_top
									__UpperCamelCase    :    Dict								=      delta_width - pad_left

									__UpperCamelCase    :    str								=      ((pad_top, pad_bottom), (pad_left, pad_right))
									return pad(__UpperCamelCase , __UpperCamelCase , data_format=__UpperCamelCase    )

						def __lowerCamelCase					(				self , *__UpperCamelCase , **__UpperCamelCase    )							->	Optional[Any]:




									'''simple docstring'''


									logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
									return self.pad_image(*__UpperCamelCase , **__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BICUBIC , __UpperCamelCase = None , **__UpperCamelCase , )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Optional[int]								=      get_image_size(__UpperCamelCase    )
									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      size["height"], size["width"]

									# We always resize to the smallest of either the input or output size.
									__UpperCamelCase    :    int								=      min(__UpperCamelCase , __UpperCamelCase    )
									__UpperCamelCase    :    List[str]								=      min(__UpperCamelCase , __UpperCamelCase    )

									if height == input_height and width == input_width:
												return image

									if input_height > input_width:
												__UpperCamelCase    :    List[Any]								=      int(input_width * height / input_height    )
									elif input_width > input_height:
												__UpperCamelCase    :    Tuple								=      int(input_height * width / input_width    )

									return resize(
									    __UpperCamelCase , size=(height, width) , resample=__UpperCamelCase , reducing_gap=2.0 , data_format=__UpperCamelCase , **__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BICUBIC , __UpperCamelCase = None , **__UpperCamelCase , )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      get_size_dict(__UpperCamelCase    )
									__UpperCamelCase    :    str								=      min(size["height"] , size["width"]    )
									__UpperCamelCase    :    Tuple								=      get_resize_output_image_size(__UpperCamelCase , size=__UpperCamelCase , default_to_square=__UpperCamelCase    )
									__UpperCamelCase    :    List[Any]								=      resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase    )
									return resized_image

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )							->	Dict:




									'''simple docstring'''


									return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )							->	np.ndarray:




									'''simple docstring'''


									return normalize(__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase    )




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = ChannelDimension.FIRST , **__UpperCamelCase , )							->	PIL.Image.Image:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      do_resize if do_resize is not None else self.do_resize
									__UpperCamelCase    :    Any								=      size if size is not None else self.size
									if isinstance(__UpperCamelCase , (tuple, list)    ):
												# Previous feature extractor had size in (width, height) format
												__UpperCamelCase    :    List[Any]								=      size[::-1]
									__UpperCamelCase    :    List[Any]								=      get_size_dict(__UpperCamelCase    )
									__UpperCamelCase    :    Union[str, Any]								=      resample if resample is not None else self.resample
									__UpperCamelCase    :    Any								=      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
									__UpperCamelCase    :    List[Any]								=      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
									__UpperCamelCase    :    Optional[int]								=      do_pad if do_pad is not None else self.do_pad
									__UpperCamelCase    :    Dict								=      do_rescale if do_rescale is not None else self.do_rescale
									__UpperCamelCase    :    List[str]								=      rescale_factor if rescale_factor is not None else self.rescale_factor
									__UpperCamelCase    :    Any								=      do_normalize if do_normalize is not None else self.do_normalize
									__UpperCamelCase    :    Union[str, Any]								=      image_mean if image_mean is not None else self.image_mean
									__UpperCamelCase    :    Dict								=      image_std if image_std is not None else self.image_std

									__UpperCamelCase    :    List[Any]								=      make_list_of_images(__UpperCamelCase    )

									if not valid_images(__UpperCamelCase    ):
												raise ValueError(
												    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
												    "torch.Tensor, tf.Tensor or jax.ndarray."    )

									if do_resize and size is None:
												raise ValueError("Size must be specified if do_resize is True."    )

									if do_rescale and rescale_factor is None:
												raise ValueError("Rescale factor must be specified if do_rescale is True."    )

									if do_pad and size is None:
												raise ValueError("Size must be specified if do_pad is True."    )

									if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("Image mean and std must be specified if do_normalize is True."    )

									# All transformations expect numpy arrays.
									__UpperCamelCase    :    Union[str, Any]								=      [to_numpy_array(__UpperCamelCase    ) for image in images]

									if do_align_long_axis:
												__UpperCamelCase    :    Tuple								=      [self.align_long_axis(__UpperCamelCase , size=__UpperCamelCase    ) for image in images]

									if do_resize:
												__UpperCamelCase    :    Optional[int]								=      [self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase    ) for image in images]

									if do_thumbnail:
												__UpperCamelCase    :    Union[str, Any]								=      [self.thumbnail(image=__UpperCamelCase , size=__UpperCamelCase    ) for image in images]

									if do_pad:
												__UpperCamelCase    :    Optional[Any]								=      [self.pad_image(image=__UpperCamelCase , size=__UpperCamelCase , random_padding=__UpperCamelCase    ) for image in images]

									if do_rescale:
												__UpperCamelCase    :    Optional[int]								=      [self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase    ) for image in images]

									if do_normalize:
												__UpperCamelCase    :    Union[str, Any]								=      [self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase    ) for image in images]

									__UpperCamelCase    :    List[str]								=      [to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase    ) for image in images]

									__UpperCamelCase    :    int								=      {"pixel_values": images}
									return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase    )