"""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    =				logging.get_logger(__name__)


__lowercase    =				{
    '''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    =				'''
    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 		_lowercase (						__a      ):



						"""simple docstring"""




						lowercase__       =  '''bark_module'''
						lowercase__       =  ['''past_key_values''']

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





						def __init__(				self							:    Dict     , UpperCamelCase__							:    Dict=1024     , UpperCamelCase__							:    Dict=10048     , UpperCamelCase__							:    Optional[int]=10048     , UpperCamelCase__							:    Tuple=12     , UpperCamelCase__							:    Optional[int]=12     , UpperCamelCase__							:    int=768     , UpperCamelCase__							:    str=0.0     , UpperCamelCase__							:    Union[str, Any]=True     , UpperCamelCase__							:    Tuple=0.02     , UpperCamelCase__							:    Dict=True     , **UpperCamelCase__							:    List[str]     , )				->				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__(**UpperCamelCase__				)





						@classmethod
						def   UpperCAmelCase_ (				cls							:    Union[str, Any]     , UpperCamelCase__							:    Union[str, os.PathLike]     , UpperCamelCase__							:    Optional[Union[str, os.PathLike]] = None     , UpperCamelCase__							:    bool = False     , UpperCamelCase__							:    bool = False     , UpperCamelCase__							:    Optional[Union[str, bool]] = None     , UpperCamelCase__							:    str = "main"     , **UpperCamelCase__							:    List[str]     , )				->				"PretrainedConfig":
													'''simple docstring'''



													__UpperCamelCase							=cache_dir
													__UpperCamelCase							=force_download
													__UpperCamelCase							=local_files_only
													__UpperCamelCase							=revision

													cls._set_token_in_kwargs(UpperCamelCase__     , UpperCamelCase__				)

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

													# 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(UpperCamelCase__     , **UpperCamelCase__				)






@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 		_lowercase (						__a      ):



						"""simple docstring"""




						lowercase__       =  '''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 		_lowercase (						__a      ):



						"""simple docstring"""




						lowercase__       =  '''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 		_lowercase (						__a      ):



						"""simple docstring"""




						lowercase__       =  '''fine_acoustics'''





						def __init__(				self							:    Dict     , UpperCamelCase__							:    Union[str, Any]=True     , UpperCamelCase__							:    List[str]=8     , UpperCamelCase__							:    Tuple=1     , **UpperCamelCase__							:    str				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=n_codes_total
													__UpperCamelCase							=n_codes_given

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






class 		_lowercase (						__a      ):



						"""simple docstring"""




						lowercase__       =  '''bark'''
						lowercase__       =  True





						def __init__(				self							:    Dict     , UpperCamelCase__							:    Dict = None     , UpperCamelCase__							:    Dict = None     , UpperCamelCase__							:    Dict = None     , UpperCamelCase__							:    Dict = None     , UpperCamelCase__							:    int=0.02     , **UpperCamelCase__							:    Optional[Any]     , )				->				int:
													'''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(**UpperCamelCase__				)
													__UpperCamelCase							=BarkCoarseConfig(**UpperCamelCase__				)
													__UpperCamelCase							=BarkFineConfig(**UpperCamelCase__				)
													__UpperCamelCase							=AutoConfig.for_model(**UpperCamelCase__				)

													__UpperCamelCase							=initializer_range

													super().__init__(**UpperCamelCase__				)





						@classmethod
						def   UpperCAmelCase_ (				cls							:    Any     , UpperCamelCase__							:    BarkSemanticConfig     , UpperCamelCase__							:    BarkCoarseConfig     , UpperCamelCase__							:    BarkFineConfig     , UpperCamelCase__							:    AutoConfig     , **UpperCamelCase__							:    Dict     , )				->				Optional[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()     , **UpperCamelCase__     , )





						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				Any:
													'''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




"""simple docstring"""

import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info


def    lowerCAmelCase			(__UpperCamelCase							:    Optional[Any]			):
							"""simple docstring"""


							class 		_lowercase :



													"""simple docstring"""




													def __init__(				self							:    int     , UpperCamelCase__							:    Union[str, Any]				)				->				None:
																				'''simple docstring'''



																				assert isinstance(UpperCamelCase__     , (bytes, str)				)
																				__UpperCamelCase							=content





													def   UpperCAmelCase_ (				self							:    Dict				)				->				Any:
																				'''simple docstring'''



																				return json.loads(self.content				)

							def mock_response(*__UpperCamelCase							:    Any							,					**__UpperCamelCase							:    Union[str, Any]			):
														assert args[0] == AUTHENTICATED_USER_ENDPOINT
														assert "Authorization" in kwargs["headers"]
														assert kwargs["headers"]["Authorization"].startswith('''token '''			)
														assert "Accept" in kwargs["headers"]
														return FakeResponse(b'''{"login":"test","id":1}'''			)

							monkeypatch.setattr(__UpperCamelCase							,					'''get'''							,					__UpperCamelCase			)
							__UpperCamelCase							=fetch_github_info('''token'''			)
							assert result["login"] == "test"
							assert result["id"] == 1



