





'''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      :  Optional[int]      							=logging.get_logger(__name__)


lowerCAmelCase      :  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''',
}

lowerCAmelCase      :  List[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    a_     (       _lowerCAmelCase							):
     __A  				=    "bark_module"
     __A  				=    ["past_key_values"]

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





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





        """simple docstring"""




        lowercase_    :Tuple					    =				block_size
        lowercase_    :Tuple					    =				input_vocab_size
        lowercase_    :Tuple					    =				output_vocab_size
        lowercase_    :str					    =				num_layers
        lowercase_    :int					    =				num_heads
        lowercase_    :List[str]					    =				hidden_size
        lowercase_    :Optional[int]					    =				dropout
        lowercase_    :Optional[Any]					    =				bias
        lowercase_    :Any					    =				use_cache
        lowercase_    :Tuple					    =				initializer_range

        super().__init__(**lowercase		)







     @classmethod
     def 							lowercase__   (				cls	:      List[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"""




        lowercase_    :Any					    =				cache_dir
        lowercase_    :List[str]					    =				force_download
        lowercase_    :int					    =				local_files_only
        lowercase_    :int					    =				revision

        cls._set_token_in_kwargs(lowercase			,   lowercase		)

        lowercase_    :List[str]					    =				cls.get_config_dict(lowercase			,   **lowercase		)

        # get the config dict if we are loading from Bark
        if config_dict.get("model_type"		) == "bark":
           lowercase_    :List[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(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							):
     __A  				=    "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							):
     __A  				=    "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							):
     __A  				=    "fine_acoustics"





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





        """simple docstring"""




        lowercase_    :Any					    =				n_codes_total
        lowercase_    :str					    =				n_codes_given

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







class    a_     (       _lowerCAmelCase							):
     __A  				=    "bark"
     __A  				=    True





     def __init__(				self	:      Tuple			,   lowercase	:      Dict = None			,   lowercase	:      Dict = None			,   lowercase	:      Dict = None			,   lowercase	:      Dict = None			,   lowercase	:      Optional[int]=0.02			,   **lowercase	:      List[Any]			,   ):





        """simple docstring"""




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

        if coarse_acoustics_config is None:
           lowercase_    :List[str]					    =				{}
           logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."		)

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

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

        lowercase_    :Optional[int]					    =				BarkSemanticConfig(**lowercase		)
        lowercase_    :str					    =				BarkCoarseConfig(**lowercase		)
        lowercase_    :List[Any]					    =				BarkFineConfig(**lowercase		)
        lowercase_    :List[str]					    =				AutoConfig.for_model(**lowercase		)

        lowercase_    :List[Any]					    =				initializer_range

        super().__init__(**lowercase		)





     @classmethod
     def 							lowercase__   (				cls	:      int			,   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 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    :Tuple					    =				copy.deepcopy(self.__dict__		)

        lowercase_    :Dict					    =				self.semantic_config.to_dict()
        lowercase_    :Dict					    =				self.coarse_acoustics_config.to_dict()
        lowercase_    :int					    =				self.fine_acoustics_config.to_dict()
        lowercase_    :List[Any]					    =				self.codec_config.to_dict()

        lowercase_    :Optional[int]					    =				self.__class__.model_type
        return output









'''simple docstring'''


import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Optional[int]				):
   return EnvironmentCommand()


class    a_     (       _lowerCAmelCase							):





     @staticmethod
     def 							lowercase__   (				lowercase	:      ArgumentParser		):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				parser.add_parser("env"			,   help="Print relevant system environment info."		)
        download_parser.set_defaults(func=lowercase		)





     def 							lowercase__   (				self	:      List[str]		):





        """simple docstring"""




        lowercase_    :Tuple					    =				{
            "`datasets` version": version,
            "Platform": platform.platform(),
            "Python version": platform.python_version(),
            "Huggingface_hub version": huggingface_hub.__version__,
            "PyArrow version": pyarrow.__version__,
            "Pandas version": pandas.__version__,
        }

        print("\nCopy-and-paste the text below in your GitHub issue.\n"		)
        print(self.format_dict(lowercase		)		)

        return info







     @staticmethod
     def 							lowercase__   (				lowercase	:      Dict		):





        """simple docstring"""




        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"


