






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


_UpperCamelCase    :   Dict   			=						logging.get_logger(__name__)

_UpperCamelCase    :   Any   			=						{
    '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 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''mobilenet_v2'''





    def __init__(							self     : Optional[Any]    ,		A     : int=3    ,		A     : str=2_2_4    ,		A     : Optional[Any]=1.0    ,		A     : Any=8    ,		A     : Optional[Any]=8    ,		A     : int=6    ,		A     : Optional[int]=3_2    ,		A     : List[str]=True    ,		A     : List[Any]=True    ,		A     : List[str]="relu6"    ,		A     : Optional[int]=True    ,		A     : Optional[int]=0.8    ,		A     : Optional[Any]=0.02    ,		A     : int=0.0_01    ,		A     : Optional[int]=2_5_5    ,		**A     : Any    ,		):


      '''simple docstring'''




      super().__init__(**A      )

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

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






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					version.parse('''1.11'''	)





    @property
    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




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





    @property
    def     lowerCamelCase__				(							self     : Tuple      ):


      '''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     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      return 1E-4












"""simple docstring"""


from typing import TYPE_CHECKING

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


_UpperCamelCase    :   Optional[int]   			=						{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

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

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Optional[int]   			=						[
       '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

   _UpperCamelCase    :   int   			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




