





"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


lowerCAmelCase:	int							      =logging.get_logger(__name__)

lowerCAmelCase:	Any							      ={
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}
class lowerCamelCase__  (    A_				):
   __UpperCAmelCase        =					"""musicgen_decoder"""
   __UpperCAmelCase        =					["""past_key_values"""]



   def __init__(       self      ,   snake_case=2_0_4_8      ,   snake_case=2_0_4_8      ,   snake_case=2_4      ,   snake_case=4_0_9_6      ,   snake_case=1_6      ,   snake_case=0.0      ,   snake_case=True      ,   snake_case="gelu"      ,   snake_case=1_0_2_4      ,   snake_case=0.1      ,   snake_case=0.0      ,   snake_case=0.0      ,   snake_case=0.02      ,   snake_case=False      ,   snake_case=4      ,   snake_case=2_0_4_8      ,   snake_case=2_0_4_8      ,   snake_case=None      ,   snake_case=False      ,   **snake_case      ,   ) ->    Union[str, Any]:







      """simple docstring"""
      lowercase    :			List[Any]  =       vocab_size
      lowercase    :			List[Any]  =       max_position_embeddings
      lowercase    :			Optional[Any]  =       hidden_size
      lowercase    :			Dict  =       ffn_dim
      lowercase    :			Union[str, Any]  =       num_hidden_layers
      lowercase    :			str  =       num_attention_heads
      lowercase    :			int  =       dropout
      lowercase    :			Optional[Any]  =       attention_dropout
      lowercase    :			Any  =       activation_dropout
      lowercase    :			int  =       activation_function
      lowercase    :			Tuple  =       initializer_factor
      lowercase    :			Tuple  =       layerdrop
      lowercase    :			Tuple  =       use_cache
      lowercase    :			List[Any]  =       scale_embedding  # scale factor will be sqrt(d_model) if True
      lowercase    :			Any  =       num_codebooks
      super().__init__(
          pad_token_id=snake_case      ,   bos_token_id=snake_case      ,   eos_token_id=snake_case      ,   tie_word_embeddings=snake_case      ,   **snake_case      ,   )




class lowerCamelCase__  (    A_				):
   __UpperCAmelCase        =					"""musicgen"""
   __UpperCAmelCase        =					True



   def __init__(       self      ,   **snake_case	) ->    List[str]:







      """simple docstring"""
      super().__init__(**snake_case	)
      if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
         raise ValueError("""Config has to be initialized with text_encoder, audio_encoder and decoder config"""	)

      lowercase    :			Union[str, Any]  =       kwargs.pop("""text_encoder"""	)
      lowercase    :			List[str]  =       text_encoder_config.pop("""model_type"""	)

      lowercase    :			List[Any]  =       kwargs.pop("""audio_encoder"""	)
      lowercase    :			List[str]  =       audio_encoder_config.pop("""model_type"""	)

      lowercase    :			Union[str, Any]  =       kwargs.pop("""decoder"""	)

      lowercase    :			Dict  =       AutoConfig.for_model(snake_case      ,   **snake_case	)
      lowercase    :			List[Any]  =       AutoConfig.for_model(snake_case      ,   **snake_case	)
      lowercase    :			int  =       MusicgenDecoderConfig(**snake_case	)
      lowercase    :			Optional[int]  =       True



   @classmethod
   def    _UpperCAmelCase	(       cls      ,   snake_case      ,   snake_case      ,   snake_case      ,   **snake_case      ,   ) ->    Optional[int]:







      """simple docstring"""
      return cls(
          text_encoder=text_encoder_config.to_dict()      ,   audio_encoder=audio_encoder_config.to_dict()      ,   decoder=decoder_config.to_dict()      ,   **snake_case      ,   )





   def    _UpperCAmelCase	(       self	) ->    int:







      """simple docstring"""
      lowercase    :			Optional[Any]  =       copy.deepcopy(self.__dict__	)
      lowercase    :			List[str]  =       self.text_encoder.to_dict()
      lowercase    :			Tuple  =       self.audio_encoder.to_dict()
      lowercase    :			Dict  =       self.decoder.to_dict()
      lowercase    :			Dict  =       self.__class__.model_type
      return output







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase:	Union[str, Any]							      ={
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	Dict							      =["DebertaV2TokenizerFast"]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	Optional[Any]							      =[
       "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFDebertaV2ForMaskedLM",
       "TFDebertaV2ForQuestionAnswering",
       "TFDebertaV2ForSequenceClassification",
       "TFDebertaV2ForTokenClassification",
       "TFDebertaV2Model",
       "TFDebertaV2PreTrainedModel",
   ]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	str							      =[
       "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DebertaV2ForMaskedLM",
       "DebertaV2ForMultipleChoice",
       "DebertaV2ForQuestionAnswering",
       "DebertaV2ForSequenceClassification",
       "DebertaV2ForTokenClassification",
       "DebertaV2Model",
       "DebertaV2PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_deberta_va import (
       DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DebertaVaConfig,
       DebertaVaOnnxConfig,
   )
   from .tokenization_deberta_va import DebertaVaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta_va import (
          TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
          TFDebertaVaPreTrainedModel,
      )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta_va import (
          DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaVaForMaskedLM,
          DebertaVaForMultipleChoice,
          DebertaVaForQuestionAnswering,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaModel,
          DebertaVaPreTrainedModel,
      )

else:
   import sys

   lowerCAmelCase:	Optional[Any]							      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


