'''simple docstring'''
import copy

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


_A		:			str	      =logging.get_logger(__name__)

_A		:			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 (  snake_case				):
      UpperCAmelCase_  :	Any     =					"musicgen_decoder"
      UpperCAmelCase_  :	Any     =					["past_key_values"]







      def __init__(     self     ,						lowercase=2048     ,						lowercase=2048     ,						lowercase=24     ,						lowercase=4096     ,						lowercase=16     ,						lowercase=0.0     ,						lowercase=True     ,						lowercase="gelu"     ,						lowercase=1024     ,						lowercase=0.1     ,						lowercase=0.0     ,						lowercase=0.0     ,						lowercase=0.02     ,						lowercase=False     ,						lowercase=4     ,						lowercase=2048     ,						lowercase=2048     ,						lowercase=None     ,						lowercase=False     ,						**lowercase     ,						):
        lowercase							:       Union[str, Any]											=      vocab_size
        lowercase							:       Tuple											=      max_position_embeddings
        lowercase							:       List[str]											=      hidden_size
        lowercase							:       Optional[Any]											=      ffn_dim
        lowercase							:       Tuple											=      num_hidden_layers
        lowercase							:       List[Any]											=      num_attention_heads
        lowercase							:       Dict											=      dropout
        lowercase							:       Any											=      attention_dropout
        lowercase							:       List[Any]											=      activation_dropout
        lowercase							:       Tuple											=      activation_function
        lowercase							:       int											=      initializer_factor
        lowercase							:       Tuple											=      layerdrop
        lowercase							:       List[str]											=      use_cache
        lowercase							:       Tuple											=      scale_embedding  # scale factor will be sqrt(d_model) if True
        lowercase							:       Union[str, Any]											=      num_codebooks
        super().__init__(
            pad_token_id=lowercase     ,						bos_token_id=lowercase     ,						eos_token_id=lowercase     ,						tie_word_embeddings=lowercase     ,						**lowercase     ,						)





class _lowerCAmelCase (  snake_case				):
      UpperCAmelCase_  :	Dict     =					"musicgen"
      UpperCAmelCase_  :	List[str]     =					True







      def __init__(     self     ,						**lowercase    ):
        super().__init__(**lowercase    )
        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							:       str											=      kwargs.pop('''text_encoder'''    )
        lowercase							:       Optional[int]											=      text_encoder_config.pop('''model_type'''    )

        lowercase							:       int											=      kwargs.pop('''audio_encoder'''    )
        lowercase							:       Tuple											=      audio_encoder_config.pop('''model_type'''    )

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

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







      @classmethod
      def     lowerCamelCase__			(     cls     ,						lowercase     ,						lowercase     ,						lowercase     ,						**lowercase     ,						):
        return cls(
            text_encoder=text_encoder_config.to_dict()     ,						audio_encoder=audio_encoder_config.to_dict()     ,						decoder=decoder_config.to_dict()     ,						**lowercase     ,						)







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Optional[int]											=      copy.deepcopy(self.__dict__    )
        lowercase							:       List[Any]											=      self.text_encoder.to_dict()
        lowercase							:       List[Any]											=      self.audio_encoder.to_dict()
        lowercase							:       Optional[Any]											=      self.decoder.to_dict()
        lowercase							:       Any											=      self.__class__.model_type
        return output
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A		:			int	      ={'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:			str	      =[
     '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CvtForImageClassification''',
     '''CvtModel''',
     '''CvtPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:			Optional[Any]	      =[
     '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFCvtForImageClassification''',
     '''TFCvtModel''',
     '''TFCvtPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_cvt import (
      CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
      CvtForImageClassification,
      CvtModel,
      CvtPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_cvt import (
      TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCvtForImageClassification,
      TFCvtModel,
      TFCvtPreTrainedModel,
  )


else:
 import sys

 _A		:			Tuple	      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)