
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_				:							Tuple       =    logging.get_logger(__name__)


lowerCamelCase_				:							Optional[int]       =    {
    "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[str]       =    "\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  _lowerCamelCase			(lowerCamelCase					):
     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							,       SCREAMING_SNAKE_CASE_=1_024							,       SCREAMING_SNAKE_CASE_=10_048							,       SCREAMING_SNAKE_CASE_=10_048							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=768							,       SCREAMING_SNAKE_CASE_=0.0							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=0.0_2							,       SCREAMING_SNAKE_CASE_=True							,       **SCREAMING_SNAKE_CASE_							,       ):
            __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__(**SCREAMING_SNAKE_CASE_    )

     @classmethod
     def 						__lowerCamelCase				(  cls							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = "main"							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   cache_dir
            __snake_case						       =   force_download
            __snake_case						       =   local_files_only
            __snake_case						       =   revision

            cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            __snake_case       ,       __snake_case						       =   cls.get_config_dict(SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

            # 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(SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )







@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  _lowerCamelCase			(lowerCamelCase					):
     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  _lowerCamelCase			(lowerCamelCase					):
     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  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""fine_acoustics"""

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=8							,       SCREAMING_SNAKE_CASE_=1							,       **SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   n_codes_total
            __snake_case						       =   n_codes_given

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







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""bark"""
     lowercase__				  =			True

     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_=0.0_2							,       **SCREAMING_SNAKE_CASE_							,       ):
            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(**SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   BarkCoarseConfig(**SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   BarkFineConfig(**SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   AutoConfig.for_model(**SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   initializer_range

            super().__init__(**SCREAMING_SNAKE_CASE_    )

     @classmethod
     def 						__lowerCamelCase				(  cls							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_							,       ):
            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()							,       **SCREAMING_SNAKE_CASE_							,       )

     def 						__lowerCamelCase				(  self    ):
            __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


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""deberta"""

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=50_265							,       SCREAMING_SNAKE_CASE_=768							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=3_072							,       SCREAMING_SNAKE_CASE_="gelu"							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=0.0_2							,       SCREAMING_SNAKE_CASE_=1E-7							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=-1							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_="gelu"							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   hidden_size
            __snake_case						       =   num_hidden_layers
            __snake_case						       =   num_attention_heads
            __snake_case						       =   intermediate_size
            __snake_case						       =   hidden_act
            __snake_case						       =   hidden_dropout_prob
            __snake_case						       =   attention_probs_dropout_prob
            __snake_case						       =   max_position_embeddings
            __snake_case						       =   type_vocab_size
            __snake_case						       =   initializer_range
            __snake_case						       =   relative_attention
            __snake_case						       =   max_relative_positions
            __snake_case						       =   pad_token_id
            __snake_case						       =   position_biased_input

            # Backwards compatibility
            if type(SCREAMING_SNAKE_CASE_    ) == str:
                   __snake_case						       =   [x.strip() for x in pos_att_type.lower().split('|'    )]

            __snake_case						       =   pos_att_type
            __snake_case						       =   vocab_size
            __snake_case						       =   layer_norm_eps

            __snake_case						       =   kwargs.get('pooler_hidden_size'							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   pooler_dropout
            __snake_case						       =   pooler_hidden_act







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.task == "multiple-choice":
                   __snake_case						       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]    )
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]    )

     @property
     def 						__lowerCamelCase				(  self    ):
            return 12

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 3							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = None							,       ):
            __snake_case						       =   super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE_							,       framework=SCREAMING_SNAKE_CASE_    )
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs
