





'''simple docstring'''






from typing import TYPE_CHECKING

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


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

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










from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a:     Dict   					=					logging.get_logger(__name__)

__a:     Optional[int]   					=					{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  '''efficientnet'''


  def __init__(      self		:			Dict       ,				lowerCamelCase		:			int = 3       ,				lowerCamelCase		:			int = 600       ,				lowerCamelCase		:			float = 2.0       ,				lowerCamelCase		:			float = 3.1       ,				lowerCamelCase		:			int = 8       ,				lowerCamelCase		:			List[int] = [3, 3, 5, 3, 5, 5, 3]       ,				lowerCamelCase		:			List[int] = [32, 16, 24, 40, 80, 112, 192]       ,				lowerCamelCase		:			List[int] = [16, 24, 40, 80, 112, 192, 320]       ,				lowerCamelCase		:			List[int] = []       ,				lowerCamelCase		:			List[int] = [1, 2, 2, 2, 1, 2, 1]       ,				lowerCamelCase		:			List[int] = [1, 2, 2, 3, 3, 4, 1]       ,				lowerCamelCase		:			List[int] = [1, 6, 6, 6, 6, 6, 6]       ,				lowerCamelCase		:			float = 0.25       ,				lowerCamelCase		:			str = "swish"       ,				lowerCamelCase		:			int = 2560       ,				lowerCamelCase		:			str = "mean"       ,				lowerCamelCase		:			float = 0.02       ,				lowerCamelCase		:			float = 0.001       ,				lowerCamelCase		:			float = 0.99       ,				lowerCamelCase		:			float = 0.5       ,				lowerCamelCase		:			float = 0.2       ,				**lowerCamelCase		:			List[str]       ,				)						->	Union[str, Any]:


         """simple docstring"""
         super().__init__(**lowerCamelCase							)

         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    width_coefficient
         _UpperCAmelCase							=    depth_coefficient
         _UpperCAmelCase							=    depth_divisor
         _UpperCAmelCase							=    kernel_sizes
         _UpperCAmelCase							=    in_channels
         _UpperCAmelCase							=    out_channels
         _UpperCAmelCase							=    depthwise_padding
         _UpperCAmelCase							=    strides
         _UpperCAmelCase							=    num_block_repeats
         _UpperCAmelCase							=    expand_ratios
         _UpperCAmelCase							=    squeeze_expansion_ratio
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dim
         _UpperCAmelCase							=    pooling_type
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    batch_norm_eps
         _UpperCAmelCase							=    batch_norm_momentum
         _UpperCAmelCase							=    dropout_rate
         _UpperCAmelCase							=    drop_connect_rate
         _UpperCAmelCase							=    sum(lowerCamelCase							) * 4



class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  version.parse('''1.11'''     )


  @property
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Mapping[str, Mapping[int, str]]:


         """simple docstring"""
         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]							)





  @property
  def  lowerCamelCase       (      self		:			Dict							)						->	float:


         """simple docstring"""
         return 1E-5