
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__    :					Dict					=						logging.get_logger(__name__)


lowercase__    :					List[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__    :					Dict					=						"\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 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       '''bark_module'''
						lowerCAmelCase_							=       ['''past_key_values''']

						lowerCAmelCase_							=       {
						    '''num_attention_heads''': '''num_heads''',
						    '''num_hidden_layers''': '''num_layers''',
						    '''vocab_size''': '''input_vocab_size''',
						    '''window_size''': '''block_size''',
						}






						def __init__(						self							:					List[str]	,     __lowercase							:					Optional[int]=10_24	,     __lowercase							:					Tuple=1_00_48	,     __lowercase							:					str=1_00_48	,     __lowercase							:					Dict=12	,     __lowercase							:					List[str]=12	,     __lowercase							:					Dict=7_68	,     __lowercase							:					Optional[int]=0.0	,     __lowercase							:					str=True	,     __lowercase							:					Union[str, Any]=0.02	,     __lowercase							:					Any=True	,     **__lowercase							:					Union[str, 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 	snake_case__						(						cls							:					str	,     __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							:					Union[str, Any]	,     ):






							"""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'''      )	,       '''
    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 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       '''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 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       '''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 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       '''fine_acoustics'''






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






							"""simple docstring"""






							snake_case_		   =					n_codes_total
							snake_case_		   =					n_codes_given

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







class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       '''bark'''
						lowerCAmelCase_							=       True






						def __init__(						self							:					int	,     __lowercase							:					Dict = None	,     __lowercase							:					Dict = None	,     __lowercase							:					Dict = None	,     __lowercase							:					Dict = None	,     __lowercase							:					List[Any]=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 	snake_case__						(						cls							:					List[Any]	,     __lowercase							:					BarkSemanticConfig	,     __lowercase							:					BarkCoarseConfig	,     __lowercase							:					BarkFineConfig	,     __lowercase							:					AutoConfig	,     **__lowercase							:					Dict	,     ):






							"""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 	snake_case__						(						self							:					Dict						):






							"""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 torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_							=       10






						def 	snake_case__						(						self							:					Tuple	,     **__lowercase							:					int						):






							"""simple docstring"""






							snake_case_		   =					{
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config






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






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]						):
								self.check_over_configs(beta_start=__lowercase	,     beta_end=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






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






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






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config(prediction_type="v_prediction"						)
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 108.4439						) < 1E-2
							assert abs(result_mean.item() - 0.1412						) < 1E-3






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps	,     device=__lowercase						)
							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for t in scheduler.timesteps:
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3
