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__      		=					'\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 							a_					(		_lowerCAmelCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   """bark_module"""
 UpperCAmelCase_      					=   ["""past_key_values"""]

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




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







        '''simple docstring'''



        lowerCAmelCase__	      =   block_size
        lowerCAmelCase__	      =   input_vocab_size
        lowerCAmelCase__	      =   output_vocab_size
        lowerCAmelCase__	      =   num_layers
        lowerCAmelCase__	      =   num_heads
        lowerCAmelCase__	      =   hidden_size
        lowerCAmelCase__	      =   dropout
        lowerCAmelCase__	      =   bias
        lowerCAmelCase__	      =   use_cache
        lowerCAmelCase__	      =   initializer_range

        super().__init__(**lowercase__)




 @classmethod
 def 						__snake_case		(     cls   :    List[Any]     ,       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'''



        lowerCAmelCase__	      =   cache_dir
        lowerCAmelCase__	      =   force_download
        lowerCAmelCase__	      =   local_files_only
        lowerCAmelCase__	      =   revision

        cls._set_token_in_kwargs(lowercase__     ,       lowercase__)

        lowerCAmelCase__	      =   cls.get_config_dict(lowercase__     ,       **lowercase__)

        # get the config dict if we are loading from Bark
        if config_dict.get('model_type') == "bark":
               lowerCAmelCase__	      =   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 							a_					(		_lowerCAmelCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   """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 							a_					(		_lowerCAmelCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   """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 							a_					(		_lowerCAmelCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   """fine_acoustics"""




 def __init__(     self   :    Dict     ,       lowercase__   :    Tuple=True     ,       lowercase__   :    Dict=8     ,       lowercase__   :    str=1     ,       **lowercase__   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   n_codes_total
        lowerCAmelCase__	      =   n_codes_given

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






class 							a_					(		_lowerCAmelCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   """bark"""
 UpperCAmelCase_      					=   True




 def __init__(     self   :    Any     ,       lowercase__   :    Dict = None     ,       lowercase__   :    Dict = None     ,       lowercase__   :    Dict = None     ,       lowercase__   :    Dict = None     ,       lowercase__   :    Union[str, Any]=0.02     ,       **lowercase__   :    Tuple     ,       ):







        '''simple docstring'''



        if semantic_config is None:
               lowerCAmelCase__	      =   {}
               logger.info('semantic_config is None. initializing the semantic model with default values.')

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

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

        if codec_config is None:
               lowerCAmelCase__	      =   {}
               logger.info('codec_config is None. initializing the codec model with default values.')

        lowerCAmelCase__	      =   BarkSemanticConfig(**lowercase__)
        lowerCAmelCase__	      =   BarkCoarseConfig(**lowercase__)
        lowerCAmelCase__	      =   BarkFineConfig(**lowercase__)
        lowerCAmelCase__	      =   AutoConfig.for_model(**lowercase__)

        lowerCAmelCase__	      =   initializer_range

        super().__init__(**lowercase__)




 @classmethod
 def 						__snake_case		(     cls   :    Tuple     ,       lowercase__   :    BarkSemanticConfig     ,       lowercase__   :    BarkCoarseConfig     ,       lowercase__   :    BarkFineConfig     ,       lowercase__   :    AutoConfig     ,       **lowercase__   :    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 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   copy.deepcopy(self.__dict__)

        lowerCAmelCase__	      =   self.semantic_config.to_dict()
        lowerCAmelCase__	      =   self.coarse_acoustics_config.to_dict()
        lowerCAmelCase__	      =   self.fine_acoustics_config.to_dict()
        lowerCAmelCase__	      =   self.codec_config.to_dict()

        lowerCAmelCase__	      =   self.__class__.model_type
        return output





"""simple docstring"""
import doctest

import projectq
from projectq.ops import H, Measure
def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:Union[str, Any]		=     quantum_engine.allocate_qubit()
						H | qubit
						Measure | qubit
						return int(_lowercase      )


if __name__ == "__main__":
						doctest.testmod()

						# initialises a new quantum backend
						__a           =							projectq.MainEngine()

						# Generate a list of 10 random numbers
						__a           =							[get_random_number(quantum_engine) for _ in range(10)]

						# Flushes the quantum engine from memory
						quantum_engine.flush()

						print("Random numbers", random_numbers_list)
