



"""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


__a           =							logging.get_logger(__name__)


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

__a           =							"\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 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Optional[Any]		=	"""bark_module"""
					_A						:     Optional[int]		=	["""past_key_values"""]

					_A						:     Union[str, Any]		=	{
					    """num_attention_heads""": """num_heads""",
					    """num_hidden_layers""": """num_layers""",
					    """vocab_size""": """input_vocab_size""",
					    """window_size""": """block_size""",
					}






					def __init__(			self:				Any		,	snake_case:				Any=1_024		,	snake_case:				int=10_048		,	snake_case:				int=10_048		,	snake_case:				Union[str, Any]=12		,	snake_case:				List[Any]=12		,	snake_case:				str=768		,	snake_case:				Union[str, Any]=0.0		,	snake_case:				List[Any]=True		,	snake_case:				List[Any]=0.0_2		,	snake_case:				List[Any]=True		,	**snake_case:				Tuple		,	)  ->      Tuple:
											snake_case_					:Optional[int]		=     block_size
											snake_case_					:str		=     input_vocab_size
											snake_case_					:List[str]		=     output_vocab_size
											snake_case_					:int		=     num_layers
											snake_case_					:List[str]		=     num_heads
											snake_case_					:Any		=     hidden_size
											snake_case_					:Optional[Any]		=     dropout
											snake_case_					:Any		=     bias
											snake_case_					:List[Any]		=     use_cache
											snake_case_					:str		=     initializer_range

											super().__init__(**snake_case		)






					@classmethod
					def   lowerCAmelCase_							(			cls:				List[Any]		,	snake_case:				Union[str, os.PathLike]		,	snake_case:				Optional[Union[str, os.PathLike]] = None		,	snake_case:				bool = False		,	snake_case:				bool = False		,	snake_case:				Optional[Union[str, bool]] = None		,	snake_case:				str = "main"		,	**snake_case:				Dict		,	)  ->      "PretrainedConfig":
											snake_case_					:Optional[Any]		=     cache_dir
											snake_case_					:List[Any]		=     force_download
											snake_case_					:List[str]		=     local_files_only
											snake_case_					:Dict		=     revision

											cls._set_token_in_kwargs(snake_case		,	snake_case		)

											snake_case_,			snake_case_					:int		=     cls.get_config_dict(snake_case		,	**snake_case		)

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

@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 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     List[Any]		=	"""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 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     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 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Optional[int]		=	"""fine_acoustics"""






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

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

class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Union[str, Any]		=	"""bark"""
					_A						:     List[str]		=	True






					def __init__(			self:				Any		,	snake_case:				Dict = None		,	snake_case:				Dict = None		,	snake_case:				Dict = None		,	snake_case:				Dict = None		,	snake_case:				Union[str, Any]=0.0_2		,	**snake_case:				Tuple		,	)  ->      str:
											if semantic_config is None:
																	snake_case_					:List[str]		=     {}
																	logger.info("""semantic_config is None. initializing the semantic model with default values."""		)

											if coarse_acoustics_config is None:
																	snake_case_					:int		=     {}
																	logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""		)

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

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

											snake_case_					:List[str]		=     BarkSemanticConfig(**snake_case		)
											snake_case_					:Dict		=     BarkCoarseConfig(**snake_case		)
											snake_case_					:Optional[int]		=     BarkFineConfig(**snake_case		)
											snake_case_					:Dict		=     AutoConfig.for_model(**snake_case		)

											snake_case_					:Union[str, Any]		=     initializer_range

											super().__init__(**snake_case		)






					@classmethod
					def   lowerCAmelCase_							(			cls:				Tuple		,	snake_case:				BarkSemanticConfig		,	snake_case:				BarkCoarseConfig		,	snake_case:				BarkFineConfig		,	snake_case:				AutoConfig		,	**snake_case:				Any		,	)  ->      Tuple:
											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()		,	**snake_case		,	)






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Any:
											snake_case_					:Optional[int]		=     copy.deepcopy(self.__dict__		)

											snake_case_					:Dict		=     self.semantic_config.to_dict()
											snake_case_					:Tuple		=     self.coarse_acoustics_config.to_dict()
											snake_case_					:Tuple		=     self.fine_acoustics_config.to_dict()
											snake_case_					:Tuple		=     self.codec_config.to_dict()

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





"""simple docstring"""
import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__a           =							10_00
class 							lowerCamelCase    :






					'''simple docstring'''





					def __init__(			self:				Dict		,	snake_case:				List[str]		,	snake_case:				Optional[int]=1.2		,	snake_case:				List[Any]="g"		,	snake_case:				Dict=0.5		,	snake_case:				Dict="-"		,	snake_case:				Tuple="test_out.jpg"		,	snake_case:				List[str]=True		,	snake_case:				List[Any]=False		,	snake_case:				List[str]=None		,	snake_case:				int=None		,	snake_case:				Tuple=0.7		,	)  ->      Optional[int]:
											if isinstance(snake_case		,	torch.Tensor		):
																	snake_case_					:Union[str, Any]		=     img.numpy().astype("""np.uint8"""		)
											if isinstance(snake_case		,	snake_case		):
																	snake_case_					:Optional[Any]		=     img_tensorize(snake_case		)
											assert isinstance(snake_case		,	np.ndarray		)

											snake_case_,			snake_case_					:Dict		=     img.shape[1], img.shape[0]
											snake_case_					:List[Any]		=     mplfigure.Figure(frameon=snake_case		)
											snake_case_					:Any		=     fig.get_dpi()
											snake_case_					:Tuple		=     (width * scale + 1E-2) / dpi
											snake_case_					:Any		=     (height * scale + 1E-2) / dpi
											fig.set_size_inches(snake_case		,	snake_case		)
											snake_case_					:str		=     fig.add_axes([0.0, 0.0, 1.0, 1.0]		)
											ax.axis("""off"""		)
											ax.set_xlim(0.0		,	snake_case		)
											ax.set_ylim(snake_case		)

											snake_case_					:str		=     saveas
											snake_case_					:str		=     rgb
											snake_case_					:Any		=     pynb
											snake_case_					:List[str]		=     img
											snake_case_					:Dict		=     edgecolor
											snake_case_					:Any		=     0.5
											snake_case_					:Dict		=     linestyle
											snake_case_					:Optional[Any]		=     int(np.sqrt(min(snake_case		,	snake_case		)		) * scale // 3		)
											snake_case_					:Optional[Any]		=     width
											snake_case_					:List[str]		=     height
											snake_case_					:int		=     scale
											snake_case_					:Tuple		=     fig
											snake_case_					:Any		=     ax
											snake_case_					:Union[str, Any]		=     pad
											snake_case_					:Dict		=     idaobj
											snake_case_					:Any		=     idaattr
											snake_case_					:Any		=     FigureCanvasAgg(snake_case		)






					def   lowerCAmelCase_							(			self:				int		,	snake_case:				Dict		,	snake_case:				Any=None		)  ->      List[str]:
											if color is None:
																	snake_case_					:Any		=     self.edgecolor
											((snake_case_),			(snake_case_),			(snake_case_),			(snake_case_))					:List[Any]		=     box
											snake_case_					:Union[str, Any]		=     xa - xa
											snake_case_					:Dict		=     ya - ya
											self.ax.add_patch(
											    mpl.patches.Rectangle(
											        (xa, ya)		,	snake_case		,	snake_case		,	fill=snake_case		,	edgecolor=snake_case		,	linewidth=self.font_size // 3		,	alpha=self.alpha		,	linestyle=self.linestyle		,	)		)






					def   lowerCAmelCase_							(			self:				Dict		,	snake_case:				Union[str, Any]		,	snake_case:				Union[str, Any]=None		,	snake_case:				Any=None		,	snake_case:				List[str]=None		,	snake_case:				int=None		)  ->      Any:
											if len(boxes.shape		) > 2:
																	snake_case_					:str		=     boxes[0]
											if len(obj_ids.shape		) > 1:
																	snake_case_					:Optional[int]		=     obj_ids[0]
											if len(obj_scores.shape		) > 1:
																	snake_case_					:Union[str, Any]		=     obj_scores[0]
											if len(attr_ids.shape		) > 1:
																	snake_case_					:Union[str, Any]		=     attr_ids[0]
											if len(attr_scores.shape		) > 1:
																	snake_case_					:Optional[int]		=     attr_scores[0]
											if isinstance(snake_case		,	torch.Tensor		):
																	snake_case_					:str		=     boxes.numpy()
											if isinstance(snake_case		,	snake_case		):
																	snake_case_					:List[str]		=     np.array(snake_case		)
											assert isinstance(snake_case		,	np.ndarray		)
											snake_case_					:Optional[int]		=     np.prod(boxes[:, 2:] - boxes[:, :2]		,	axis=1		)
											snake_case_					:Optional[int]		=     np.argsort(-areas		).tolist()
											snake_case_					:int		=     boxes[sorted_idxs] if boxes is not None else None
											snake_case_					:Optional[int]		=     obj_ids[sorted_idxs] if obj_ids is not None else None
											snake_case_					:int		=     obj_scores[sorted_idxs] if obj_scores is not None else None
											snake_case_					:str		=     attr_ids[sorted_idxs] if attr_ids is not None else None
											snake_case_					:Any		=     attr_scores[sorted_idxs] if attr_scores is not None else None

											snake_case_					:Union[str, Any]		=     [self._random_color(maximum=1		) for _ in range(len(snake_case		)		)]
											snake_case_					:List[Any]		=     [assigned_colors[idx] for idx in sorted_idxs]
											if obj_ids is not None:
																	snake_case_					:Optional[int]		=     self._create_text_labels_attr(snake_case		,	snake_case		,	snake_case		,	snake_case		)
																	for i in range(len(snake_case		)		):
																							snake_case_					:Any		=     assigned_colors[i]
																							self.add_box(boxes[i]		,	snake_case		)
																							self.draw_labels(labels[i]		,	boxes[i]		,	snake_case		)






					def   lowerCAmelCase_							(			self:				Any		,	snake_case:				Dict		,	snake_case:				str		,	snake_case:				Any		)  ->      Union[str, Any]:
											snake_case_,			snake_case_,			snake_case_,			snake_case_					:Optional[int]		=     box
											snake_case_					:Dict		=     (xa, ya)
											snake_case_					:Union[str, Any]		=     (ya - ya) * (xa - xa)
											snake_case_					:Optional[Any]		=     _SMALL_OBJ * self.scale
											if instance_area < small or ya - ya < 40 * self.scale:
																	if ya >= self.height - 5:
																							snake_case_					:str		=     (xa, ya)
																	else:
																							snake_case_					:Any		=     (xa, ya)

											snake_case_					:Optional[Any]		=     (ya - ya) / np.sqrt(self.height * self.width		)
											snake_case_					:Dict		=     self._change_color_brightness(snake_case		,	brightness_factor=0.7		)
											snake_case_					:List[Any]		=     np.clip((height_ratio - 0.0_2) / 0.0_8 + 1		,	1.2		,	2		)
											font_size *= 0.7_5 * self.font_size

											self.draw_text(
											    text=snake_case		,	position=snake_case		,	color=snake_case		,	)






					def   lowerCAmelCase_							(			self:				List[str]		,	snake_case:				Union[str, Any]		,	snake_case:				Optional[int]		,	snake_case:				Union[str, Any]="g"		,	snake_case:				Tuple="left"		,	)  ->      str:
											snake_case_					:Optional[Any]		=     0
											snake_case_					:Union[str, Any]		=     self.font_size
											snake_case_					:Any		=     np.maximum(list(mplc.to_rgb(snake_case		)		)		,	0.2		)
											snake_case_					:Optional[Any]		=     max(0.8		,	np.max(snake_case		)		)
											snake_case_					:List[str]		=     {
											    """facecolor""": """black""",
											    """alpha""": self.alpha,
											    """pad""": self.pad,
											    """edgecolor""": """none""",
											}
											snake_case_,			snake_case_					:Optional[int]		=     position
											self.ax.text(
											    snake_case		,	snake_case		,	snake_case		,	size=font_size * self.scale		,	family="""sans-serif"""		,	bbox=snake_case		,	verticalalignment="""top"""		,	horizontalalignment=snake_case		,	color=snake_case		,	zorder=10		,	rotation=snake_case		,	)






					def   lowerCAmelCase_							(			self:				Union[str, Any]		,	snake_case:				Tuple=None		)  ->      Dict:
											if saveas is None:
																	snake_case_					:int		=     self.saveas
											if saveas.lower().endswith(""".jpg"""		) or saveas.lower().endswith(""".png"""		):
																	cva.imwrite(
																	    snake_case		,	self._get_buffer()[:, :, ::-1]		,	)
											else:
																	self.fig.savefig(snake_case		)






					def   lowerCAmelCase_							(			self:				str		,	snake_case:				List[Any]		,	snake_case:				Dict		,	snake_case:				Any		,	snake_case:				Tuple		)  ->      Dict:
											snake_case_					:int		=     [self.idaobj[i] for i in classes]
											snake_case_					:List[str]		=     [self.idaattr[i] for i in attr_classes]
											snake_case_					:List[str]		=     [
											    f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
											    for label, score, attr, attr_score in zip(snake_case		,	snake_case		,	snake_case		,	snake_case		)
											]
											return labels






					def   lowerCAmelCase_							(			self:				Dict		,	snake_case:				Any		,	snake_case:				Dict		)  ->      Tuple:
											snake_case_					:Union[str, Any]		=     [self.idaobj[i] for i in classes]
											if scores is not None:
																	if labels is None:
																							snake_case_					:List[Any]		=     ["""{:.0f}%""".format(s * 100		) for s in scores]
																	else:
																							snake_case_					:Tuple		=     ["""{} {:.0f}%""".format(snake_case		,	s * 100		) for li, s in zip(snake_case		,	snake_case		)]
											return labels






					def   lowerCAmelCase_							(			self:				str		,	snake_case:				str=255		)  ->      List[str]:
											snake_case_					:Optional[Any]		=     np.random.randint(0		,	len(_COLORS		)		)
											snake_case_					:int		=     _COLORS[idx] * maximum
											if not self.rgb:
																	snake_case_					:List[Any]		=     ret[::-1]
											return ret






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Union[str, Any]:
											if not self.pynb:
																	snake_case_,			snake_case_					:str		=     self.canvas.print_to_buffer()
																	if (width, height) != (self.width, self.height):
																							snake_case_					:Optional[int]		=     cva.resize(self.img		,	(width, height)		)
																	else:
																							snake_case_					:List[Any]		=     self.img
											else:
																	snake_case_					:List[Any]		=     io.BytesIO()  # works for cairo backend
																	self.canvas.print_rgba(snake_case		)
																	snake_case_,			snake_case_					:Tuple		=     self.width, self.height
																	snake_case_					:Any		=     buf.getvalue()
																	snake_case_					:List[str]		=     self.img

											snake_case_					:Optional[int]		=     np.frombuffer(snake_case		,	dtype="""uint8"""		)
											snake_case_					:Optional[Any]		=     buffer.reshape(snake_case		,	snake_case		,	4		)
											snake_case_,			snake_case_					:Tuple		=     np.split(snake_case		,	[3]		,	axis=2		)

											try:
																	import numexpr as ne  # fuse them with numexpr

																	snake_case_					:Any		=     ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""		)
											except ImportError:
																	snake_case_					:Union[str, Any]		=     alpha.astype("""float32"""		) / 2_5_5.0
																	snake_case_					:int		=     img * (1 - alpha) + rgb * alpha

											return visualized_image.astype("""uint8"""		)






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	snake_case:				int		,	snake_case:				Optional[Any]		)  ->      Tuple:
											assert brightness_factor >= -1.0 and brightness_factor <= 1.0
											snake_case_					:int		=     mplc.to_rgb(snake_case		)
											snake_case_					:List[Any]		=     colorsys.rgb_to_hls(*mplc.to_rgb(snake_case		)		)
											snake_case_					:Optional[Any]		=     polygon_color[1] + (brightness_factor * polygon_color[1])
											snake_case_					:List[str]		=     0.0 if modified_lightness < 0.0 else modified_lightness
											snake_case_					:Dict		=     1.0 if modified_lightness > 1.0 else modified_lightness
											snake_case_					:int		=     colorsys.hls_to_rgb(polygon_color[0]		,	snake_case		,	polygon_color[2]		)
											return modified_color

__a           =							(
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
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            0.8_57,
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            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)
