

'''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


_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						(__SCREAMING_SNAKE_CASE):
			__A   :     Dict          ="bark_module"
			__A   :     List[Any]          =["past_key_values"]

			__A   :     List[Any]          ={
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			    "vocab_size": "input_vocab_size",
			    "window_size": "block_size",
			}






			def __init__(   self       ,_snake_case=10_24       ,_snake_case=1_00_48       ,_snake_case=1_00_48       ,_snake_case=12       ,_snake_case=12       ,_snake_case=7_68       ,_snake_case=0.0       ,_snake_case=True       ,_snake_case=0.02       ,_snake_case=True       ,**_snake_case       ,):
				UpperCAmelCase_			:	Optional[int]      =   block_size
				UpperCAmelCase_			:	int      =   input_vocab_size
				UpperCAmelCase_			:	List[str]      =   output_vocab_size
				UpperCAmelCase_			:	Union[str, Any]      =   num_layers
				UpperCAmelCase_			:	Any      =   num_heads
				UpperCAmelCase_			:	Tuple      =   hidden_size
				UpperCAmelCase_			:	Optional[Any]      =   dropout
				UpperCAmelCase_			:	str      =   bias
				UpperCAmelCase_			:	int      =   use_cache
				UpperCAmelCase_			:	Union[str, Any]      =   initializer_range

				super().__init__(**_snake_case			)






			@classmethod
			def 						UpperCamelCase__				(   cls       ,_snake_case       ,_snake_case = None       ,_snake_case = False       ,_snake_case = False       ,_snake_case = None       ,_snake_case = "main"       ,**_snake_case       ,):
				UpperCAmelCase_			:	Tuple      =   cache_dir
				UpperCAmelCase_			:	Dict      =   force_download
				UpperCAmelCase_			:	Optional[int]      =   local_files_only
				UpperCAmelCase_			:	int      =   revision

				cls._set_token_in_kwargs(_snake_case       ,_snake_case			)

				UpperCAmelCase_ , UpperCAmelCase_			:	Optional[Any]      =   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":
					UpperCAmelCase_			:	Any      =   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")       ,       "\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						(__SCREAMING_SNAKE_CASE):
			__A   :     Union[str, Any]          ="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						(__SCREAMING_SNAKE_CASE):
			__A   :     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  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     List[str]          ="fine_acoustics"






			def __init__(   self       ,_snake_case=True       ,_snake_case=8       ,_snake_case=1       ,**_snake_case			):
				UpperCAmelCase_			:	str      =   n_codes_total
				UpperCAmelCase_			:	Any      =   n_codes_given

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




class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     List[Any]          ="bark"
			__A   :     Any          =True






			def __init__(   self       ,_snake_case = None       ,_snake_case = None       ,_snake_case = None       ,_snake_case = None       ,_snake_case=0.02       ,**_snake_case       ,):
				if semantic_config is None:
					UpperCAmelCase_			:	Dict      =   {}
					logger.info("semantic_config is None. initializing the semantic model with default values."			)

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

				UpperCAmelCase_			:	Dict      =   BarkSemanticConfig(**_snake_case			)
				UpperCAmelCase_			:	List[str]      =   BarkCoarseConfig(**_snake_case			)
				UpperCAmelCase_			:	Any      =   BarkFineConfig(**_snake_case			)
				UpperCAmelCase_			:	Dict      =   AutoConfig.for_model(**_snake_case			)

				UpperCAmelCase_			:	Tuple      =   initializer_range

				super().__init__(**_snake_case			)






			@classmethod
			def 						UpperCamelCase__				(   cls       ,_snake_case       ,_snake_case       ,_snake_case       ,_snake_case       ,**_snake_case       ,):
				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 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	str      =   copy.deepcopy(self.__dict__			)

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

				UpperCAmelCase_			:	Tuple      =   self.__class__.model_type
				return output







'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[int]          =(KDPMaAncestralDiscreteScheduler,)
			__A   :     List[Any]          =10






			def 						UpperCamelCase__				(   self       ,**_snake_case			):
				UpperCAmelCase_			:	List[Any]      =   {
				    "num_train_timesteps": 11_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**_snake_case			)
				return config






			def 						UpperCamelCase__				(   self			):
				for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,[0.0002, 0.002, 0.02]			):
					self.check_over_configs(beta_start=_snake_case       ,beta_end=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	str      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	str      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	str      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	Optional[Any]      =   sample.to(_snake_case			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Tuple      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Union[str, Any]      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Optional[Any]      =   output.prev_sample

				UpperCAmelCase_			:	Dict      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	List[Any]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3877			) < 1E-2
				assert abs(result_mean.item() - 18.0331			) < 5E-3






			def 						UpperCamelCase__				(   self			):
				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Any      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Any      =   self.get_scheduler_config(prediction_type="v_prediction"			)
				UpperCAmelCase_			:	Dict      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Union[str, Any]      =   self.dummy_model()
				UpperCAmelCase_			:	List[str]      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	int      =   sample.to(_snake_case			)

				UpperCAmelCase_			:	str      =   torch.manual_seed(0			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Dict      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	str      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Any      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Tuple      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 328.9970			) < 1E-2
				assert abs(result_mean.item() - 0.4284			) < 1E-3






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Optional[Any]      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Union[str, Any]      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps       ,device=_snake_case			)
				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	List[str]      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					UpperCAmelCase_			:	Union[str, Any]      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Tuple      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	int      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	List[str]      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Optional[int]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3818			) < 1E-1
				assert abs(result_mean.item() - 18.0331			) < 1E-3




