


"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							List[str]	=       """audio-spectrogram-transformer"""






    def __init__(					self				:    List[Any]				, __UpperCAmelCase				:    str=768				, __UpperCAmelCase				:    Dict=12				, __UpperCAmelCase				:    Optional[int]=12				, __UpperCAmelCase				:    Any=3072				, __UpperCAmelCase				:    List[Any]="gelu"				, __UpperCAmelCase				:    Tuple=0.0				, __UpperCAmelCase				:    Any=0.0				, __UpperCAmelCase				:    List[str]=0.02				, __UpperCAmelCase				:    int=1e-12				, __UpperCAmelCase				:    str=16				, __UpperCAmelCase				:    Optional[Any]=True				, __UpperCAmelCase				:    Any=10				, __UpperCAmelCase				:    List[Any]=10				, __UpperCAmelCase				:    List[str]=1024				, __UpperCAmelCase				:    str=128				, **__UpperCAmelCase				:    Dict				, ):
      super().__init__(**__UpperCAmelCase)

      a						:		Any      							=			hidden_size
      a						:		Tuple      							=			num_hidden_layers
      a						:		Any      							=			num_attention_heads
      a						:		Optional[Any]      							=			intermediate_size
      a						:		str      							=			hidden_act
      a						:		Tuple      							=			hidden_dropout_prob
      a						:		Optional[int]      							=			attention_probs_dropout_prob
      a						:		Optional[int]      							=			initializer_range
      a						:		Any      							=			layer_norm_eps
      a						:		Optional[int]      							=			patch_size
      a						:		Optional[Any]      							=			qkv_bias
      a						:		Optional[Any]      							=			frequency_stride
      a						:		Optional[Any]      							=			time_stride
      a						:		Tuple      							=			max_length
      a						:		Optional[Any]      							=			num_mel_bins










"""simple docstring"""







from typing import TYPE_CHECKING

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


__lowercase    					=      {
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """RagModel""",
     """RagPreTrainedModel""",
     """RagSequenceForGeneration""",
     """RagTokenForGeneration""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """TFRagModel""",
     """TFRagPreTrainedModel""",
     """TFRagSequenceForGeneration""",
     """TFRagTokenForGeneration""",
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

 __lowercase    					=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






