






'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCamelCase   :							int         =   {
    """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:
 __UpperCamelCase   :							List[Any]         =   [
     """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Data2VecAudioForAudioFrameClassification""",
     """Data2VecAudioForCTC""",
     """Data2VecAudioForSequenceClassification""",
     """Data2VecAudioForXVector""",
     """Data2VecAudioModel""",
     """Data2VecAudioPreTrainedModel""",
 ]
 __UpperCamelCase   :							Optional[int]         =   [
     """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Data2VecTextForCausalLM""",
     """Data2VecTextForMaskedLM""",
     """Data2VecTextForMultipleChoice""",
     """Data2VecTextForQuestionAnswering""",
     """Data2VecTextForSequenceClassification""",
     """Data2VecTextForTokenClassification""",
     """Data2VecTextModel""",
     """Data2VecTextPreTrainedModel""",
 ]
 __UpperCamelCase   :							str         =   [
     """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Data2VecVisionForImageClassification""",
     """Data2VecVisionForMaskedImageModeling""",
     """Data2VecVisionForSemanticSegmentation""",
     """Data2VecVisionModel""",
     """Data2VecVisionPreTrainedModel""",
 ]

if is_tf_available():
 __UpperCamelCase   :							Optional[Any]         =   [
     """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

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






"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__magic_name__       :					Any						=			logging.get_logger(__name__)

__magic_name__       :					int						=			{
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}






class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				str										=  """convnextv2"""







			def __init__(      self						,   _A=3						,   _A=4						,   _A=4						,   _A=None						,   _A=None						,   _A="gelu"						,   _A=0.02						,   _A=1e-1_2						,   _A=0.0						,   _A=2_2_4						,   _A=None						,   _A=None						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(**_A    )

									UpperCamelCase				:		Optional[Any]				   =			num_channels
									UpperCamelCase				:		int				   =			patch_size
									UpperCamelCase				:		Dict				   =			num_stages
									UpperCamelCase				:		Tuple				   =			[9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
									UpperCamelCase				:		str				   =			[3, 3, 9, 3] if depths is None else depths
									UpperCamelCase				:		List[str]				   =			hidden_act
									UpperCamelCase				:		List[str]				   =			initializer_range
									UpperCamelCase				:		List[Any]				   =			layer_norm_eps
									UpperCamelCase				:		Any				   =			drop_path_rate
									UpperCamelCase				:		Any				   =			image_size
									UpperCamelCase				:		Union[str, Any]				   =			["""stem"""] + [f"""stage{idx}""" for idx in range(1						,   len(self.depths    ) + 1    )]
									UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			get_aligned_output_features_output_indices(
									    out_features=_A						,   out_indices=_A						,   stage_names=self.stage_names    )



