


'''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


__lowercase:		Dict    						=  logging.get_logger(__name__)


__lowercase:		Dict    						=  {
    '''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:		Any    						=  '''
    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 			UpperCAmelCase			(							_a):
 _lowerCamelCase			:       str            =       """bark_module"""
 _lowerCamelCase			:       int            =       ["""past_key_values"""]

 _lowerCamelCase			:       Optional[int]            =       {
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
     """vocab_size""": """input_vocab_size""",
     """window_size""": """block_size""",
 }

 def __init__(			self			:						List[Any],       a_			:						List[Any]=1024,       a_			:						int=1_0048,       a_			:						List[Any]=1_0048,       a_			:						str=12,       a_			:						Dict=12,       a_			:						Tuple=768,       a_			:						Dict=0.0,       a_			:						List[str]=True,       a_			:						Tuple=0.02,       a_			:						List[str]=True,       **a_			:						str,       ):





    """simple docstring"""



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

    super().__init__(**__lowerCamelCase       )

 @classmethod
 def 		lowercase_    (			cls			:						int,       a_			:						Union[str, os.PathLike],       a_			:						Optional[Union[str, os.PathLike]] = None,       a_			:						bool = False,       a_			:						bool = False,       a_			:						Optional[Union[str, bool]] = None,       a_			:						str = "main",       **a_			:						List[Any],       ):





    """simple docstring"""



    UpperCamelCase__			    =    cache_dir
    UpperCamelCase__			    =    force_download
    UpperCamelCase__			    =    local_files_only
    UpperCamelCase__			    =    revision

    cls._set_token_in_kwargs(__lowerCamelCase,       __lowerCamelCase       )

    UpperCamelCase__			    =    cls.get_config_dict(__lowerCamelCase,       **__lowerCamelCase       )

    # get the config dict if we are loading from Bark
    if config_dict.get("model_type"       ) == "bark":
       UpperCamelCase__			    =    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 			UpperCAmelCase			(							_a):
 _lowerCamelCase			:       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 			UpperCAmelCase			(							_a):
 _lowerCamelCase			:       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 			UpperCAmelCase			(							_a):
 _lowerCamelCase			:       List[Any]            =       """fine_acoustics"""

 def __init__(			self			:						Any,       a_			:						str=True,       a_			:						Optional[int]=8,       a_			:						Dict=1,       **a_			:						str       ):





    """simple docstring"""



    UpperCamelCase__			    =    n_codes_total
    UpperCamelCase__			    =    n_codes_given

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







class 			UpperCAmelCase			(							_a):
 _lowerCamelCase			:       Union[str, Any]            =       """bark"""
 _lowerCamelCase			:       Union[str, Any]            =       True

 def __init__(			self			:						int,       a_			:						Dict = None,       a_			:						Dict = None,       a_			:						Dict = None,       a_			:						Dict = None,       a_			:						str=0.02,       **a_			:						List[str],       ):





    """simple docstring"""



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

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

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

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

    UpperCamelCase__			    =    BarkSemanticConfig(**__lowerCamelCase       )
    UpperCamelCase__			    =    BarkCoarseConfig(**__lowerCamelCase       )
    UpperCamelCase__			    =    BarkFineConfig(**__lowerCamelCase       )
    UpperCamelCase__			    =    AutoConfig.for_model(**__lowerCamelCase       )

    UpperCamelCase__			    =    initializer_range

    super().__init__(**__lowerCamelCase       )

 @classmethod
 def 		lowercase_    (			cls			:						int,       a_			:						BarkSemanticConfig,       a_			:						BarkCoarseConfig,       a_			:						BarkFineConfig,       a_			:						AutoConfig,       **a_			:						int,       ):





    """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 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    copy.deepcopy(self.__dict__       )

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

    UpperCamelCase__			    =    self.__class__.model_type
    return output







import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    float = np.pi / 2      ,     __magic_name__   :    float = np.pi / 2      ,     __magic_name__   :    float = np.pi / 2   )						->     qiskit.result.counts.Counts:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      QuantumRegister(3      ,     """qr"""   )  # Define the number of quantum bits
	UpperCamelCase	:Optional[Any]		=      ClassicalRegister(1      ,     """cr"""   )  # Define the number of classical bits

	UpperCamelCase	:List[Any]		=      QuantumCircuit(__magic_name__      ,     __magic_name__   )  # Define the quantum circuit.

	# Build the circuit
	quantum_circuit.u(__magic_name__      ,     __magic_name__      ,     __magic_name__      ,     0   )  # Quantum State to teleport
	quantum_circuit.h(1   )  # add hadamard gate
	quantum_circuit.cx(
	    1      ,     2   )  # add control gate with qubit 1 as control and 2 as target.
	quantum_circuit.cx(0      ,     1   )
	quantum_circuit.h(0   )
	quantum_circuit.cz(0      ,     2   )  # add control z gate.
	quantum_circuit.cx(1      ,     2   )

	quantum_circuit.measure([2]      ,     [0]   )  # measure the qubit.

	# Simulate the circuit using qasm simulator
	UpperCamelCase	:Union[str, Any]		=      Aer.get_backend("""aer_simulator"""   )
	UpperCamelCase	:Dict		=      execute(__magic_name__      ,     __magic_name__      ,     shots=1000   )

	return job.result().get_counts(__magic_name__   )


if __name__ == "__main__":
	print(
	    '''Total count for teleported state is: '''
	    F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
	)
