



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase      			=	{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase      			=	[
					    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''RobertaPreLayerNormForCausalLM''',
					    '''RobertaPreLayerNormForMaskedLM''',
					    '''RobertaPreLayerNormForMultipleChoice''',
					    '''RobertaPreLayerNormForQuestionAnswering''',
					    '''RobertaPreLayerNormForSequenceClassification''',
					    '''RobertaPreLayerNormForTokenClassification''',
					    '''RobertaPreLayerNormModel''',
					    '''RobertaPreLayerNormPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase      			=	[
					    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFRobertaPreLayerNormForCausalLM''',
					    '''TFRobertaPreLayerNormForMaskedLM''',
					    '''TFRobertaPreLayerNormForMultipleChoice''',
					    '''TFRobertaPreLayerNormForQuestionAnswering''',
					    '''TFRobertaPreLayerNormForSequenceClassification''',
					    '''TFRobertaPreLayerNormForTokenClassification''',
					    '''TFRobertaPreLayerNormMainLayer''',
					    '''TFRobertaPreLayerNormModel''',
					    '''TFRobertaPreLayerNormPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase      			=	[
					    '''FlaxRobertaPreLayerNormForCausalLM''',
					    '''FlaxRobertaPreLayerNormForMaskedLM''',
					    '''FlaxRobertaPreLayerNormForMultipleChoice''',
					    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
					    '''FlaxRobertaPreLayerNormForSequenceClassification''',
					    '''FlaxRobertaPreLayerNormForTokenClassification''',
					    '''FlaxRobertaPreLayerNormModel''',
					    '''FlaxRobertaPreLayerNormPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					lowercase      			=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				List[str]	=			"""mobilenet_v2"""


 def __init__(    self  : Optional[int]			,							a__  : Dict=3			,							a__  : str=224			,							a__  : int=1.0			,							a__  : Dict=8			,							a__  : Dict=8			,							a__  : Dict=6			,							a__  : Optional[Any]=32			,							a__  : List[str]=True			,							a__  : int=True			,							a__  : List[str]="relu6"			,							a__  : List[str]=True			,							a__  : List[Any]=0.8			,							a__  : Tuple=0.02			,							a__  : Tuple=0.001			,							a__  : Optional[Any]=255			,							**a__  : str			,							):
  '''simple docstring'''

  super().__init__(**a__    )

  if depth_multiplier <= 0:
   raise ValueError("depth_multiplier must be greater than zero."    )

  lowerCAmelCase__     :   Any     		=	num_channels
  lowerCAmelCase__     :   Union[str, Any]     		=	image_size
  lowerCAmelCase__     :   Tuple     		=	depth_multiplier
  lowerCAmelCase__     :   Optional[int]     		=	depth_divisible_by
  lowerCAmelCase__     :   Union[str, Any]     		=	min_depth
  lowerCAmelCase__     :   List[str]     		=	expand_ratio
  lowerCAmelCase__     :   Union[str, Any]     		=	output_stride
  lowerCAmelCase__     :   Optional[int]     		=	first_layer_is_expansion
  lowerCAmelCase__     :   Tuple     		=	finegrained_output
  lowerCAmelCase__     :   Optional[int]     		=	hidden_act
  lowerCAmelCase__     :   Optional[Any]     		=	tf_padding
  lowerCAmelCase__     :   Union[str, Any]     		=	classifier_dropout_prob
  lowerCAmelCase__     :   Optional[int]     		=	initializer_range
  lowerCAmelCase__     :   str     		=	layer_norm_eps
  lowerCAmelCase__     :   int     		=	semantic_loss_ignore_index




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Optional[int]	=			version.parse("""1.11"""  )


 @property
 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  return OrderedDict([("pixel_values", {0: "batch"})]    )


 @property
 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  if self.task == "image-classification":
   return OrderedDict([("logits", {0: "batch"})]    )
  else:
   return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]    )


 @property
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  return 1e-4





