






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


A_       :Any       =						logging.get_logger(__name__)

A_       :int       =						{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class 				__A     (			a ):





				"""simple docstring"""







				UpperCamelCase__ :      Optional[int]			 ="""vit_msn"""


				def __init__( self							,  lowerCamelCase__=768							,  lowerCamelCase__=12							,  lowerCamelCase__=12							,  lowerCamelCase__=3072							,  lowerCamelCase__="gelu"							,  lowerCamelCase__=0.0							,  lowerCamelCase__=0.0							,  lowerCamelCase__=0.02							,  lowerCamelCase__=1E-06							,  lowerCamelCase__=224							,  lowerCamelCase__=16							,  lowerCamelCase__=3							,  lowerCamelCase__=True							,  **lowerCamelCase__							,  ):




									"""simple docstring"""

									super().__init__(**lowerCamelCase__    )

									__UpperCamelCase      :					int       		=hidden_size
									__UpperCamelCase      :					List[Any]       		=num_hidden_layers
									__UpperCamelCase      :					Union[str, Any]       		=num_attention_heads
									__UpperCamelCase      :					List[str]       		=intermediate_size
									__UpperCamelCase      :					Union[str, Any]       		=hidden_act
									__UpperCamelCase      :					str       		=hidden_dropout_prob
									__UpperCamelCase      :					Union[str, Any]       		=attention_probs_dropout_prob
									__UpperCamelCase      :					Union[str, Any]       		=initializer_range
									__UpperCamelCase      :					Tuple       		=layer_norm_eps
									__UpperCamelCase      :					Optional[Any]       		=image_size
									__UpperCamelCase      :					Optional[int]       		=patch_size
									__UpperCamelCase      :					Any       		=num_channels
									__UpperCamelCase      :					str       		=qkv_bias








"""simple docstring"""





from typing import TYPE_CHECKING

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


__A											=		{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecAudioForAudioFrameClassification""",
      """Data2VecAudioForCTC""",
      """Data2VecAudioForSequenceClassification""",
      """Data2VecAudioForXVector""",
      """Data2VecAudioModel""",
      """Data2VecAudioPreTrainedModel""",
  ]
  __A											=		[
      """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecTextForCausalLM""",
      """Data2VecTextForMaskedLM""",
      """Data2VecTextForMultipleChoice""",
      """Data2VecTextForQuestionAnswering""",
      """Data2VecTextForSequenceClassification""",
      """Data2VecTextForTokenClassification""",
      """Data2VecTextModel""",
      """Data2VecTextPreTrainedModel""",
  ]
  __A											=		[
      """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecVisionForImageClassification""",
      """Data2VecVisionForMaskedImageModeling""",
      """Data2VecVisionForSemanticSegmentation""",
      """Data2VecVisionModel""",
      """Data2VecVisionPreTrainedModel""",
  ]

if is_tf_available():
  __A											=		[
      """TFData2VecVisionForImageClassification""",
      """TFData2VecVisionForSemanticSegmentation""",
      """TFData2VecVisionModel""",
      """TFData2VecVisionPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
  from .configuration_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecTextConfig,
      DataaVecTextOnnxConfig,
  )
  from .configuration_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecVisionConfig,
      DataaVecVisionOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dataavec_audio import (
        DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecAudioForAudioFrameClassification,
        DataaVecAudioForCTC,
        DataaVecAudioForSequenceClassification,
        DataaVecAudioForXVector,
        DataaVecAudioModel,
        DataaVecAudioPreTrainedModel,
    )
    from .modeling_dataavec_text import (
        DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecTextForCausalLM,
        DataaVecTextForMaskedLM,
        DataaVecTextForMultipleChoice,
        DataaVecTextForQuestionAnswering,
        DataaVecTextForSequenceClassification,
        DataaVecTextForTokenClassification,
        DataaVecTextModel,
        DataaVecTextPreTrainedModel,
    )
    from .modeling_dataavec_vision import (
        DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecVisionForImageClassification,
        DataaVecVisionForMaskedImageModeling,
        DataaVecVisionForSemanticSegmentation,
        DataaVecVisionModel,
        DataaVecVisionPreTrainedModel,
    )
  if is_tf_available():
    from .modeling_tf_dataavec_vision import (
        TFDataaVecVisionForImageClassification,
        TFDataaVecVisionForSemanticSegmentation,
        TFDataaVecVisionModel,
        TFDataaVecVisionPreTrainedModel,
    )

else:
  import sys

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