



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


a__      :				int   	=			logging.get_logger(__name__)

a__      :				Tuple   	=			{
    "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       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Optional[Any]       				=    "mobilenet_v2"


   def __init__(						self  :			Dict			,				lowerCAmelCase  :			Optional[int]=3			,				lowerCAmelCase  :			Union[str, Any]=2_24			,				lowerCAmelCase  :			Optional[Any]=1.0			,				lowerCAmelCase  :			Optional[int]=8			,				lowerCAmelCase  :			Dict=8			,				lowerCAmelCase  :			int=6			,				lowerCAmelCase  :			Union[str, Any]=32			,				lowerCAmelCase  :			int=True			,				lowerCAmelCase  :			int=True			,				lowerCAmelCase  :			int="relu6"			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			List[Any]=0.8			,				lowerCAmelCase  :			Any=0.02			,				lowerCAmelCase  :			str=0.0_01			,				lowerCAmelCase  :			List[Any]=2_55			,				**lowerCAmelCase  :			List[str]			,				)	->				Optional[Any]:




         """simple docstring"""

         super().__init__(**lowerCAmelCase)

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

         lowercase__     =  num_channels
         lowercase__     =  image_size
         lowercase__     =  depth_multiplier
         lowercase__     =  depth_divisible_by
         lowercase__     =  min_depth
         lowercase__     =  expand_ratio
         lowercase__     =  output_stride
         lowercase__     =  first_layer_is_expansion
         lowercase__     =  finegrained_output
         lowercase__     =  hidden_act
         lowercase__     =  tf_padding
         lowercase__     =  classifier_dropout_prob
         lowercase__     =  initializer_range
         lowercase__     =  layer_norm_eps
         lowercase__     =  semantic_loss_ignore_index



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[Any]       				=    version.parse("1.11"	)


   @property
   def 			UpperCAmelCase		(						self  :			Tuple)	->				Mapping[str, Mapping[int, str]]:




         """simple docstring"""

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


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




         """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 			UpperCAmelCase		(						self  :			Any)	->				float:




         """simple docstring"""

         return 1E-4





from typing import TYPE_CHECKING

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


a__      :				Optional[int]   	=			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			["BlenderbotTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Dict   	=			[
           "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlenderbotForCausalLM",
           "BlenderbotForConditionalGeneration",
           "BlenderbotModel",
           "BlenderbotPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				List[str]   	=			[
           "TFBlenderbotForConditionalGeneration",
           "TFBlenderbotModel",
           "TFBlenderbotPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				int   	=			[
           "FlaxBlenderbotForConditionalGeneration",
           "FlaxBlenderbotModel",
           "FlaxBlenderbotPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_blenderbot import (
           BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BlenderbotConfig,
           BlenderbotOnnxConfig,
       )
       from .tokenization_blenderbot import BlenderbotTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blenderbot import (
                  BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlenderbotForCausalLM,
                  BlenderbotForConditionalGeneration,
                  BlenderbotModel,
                  BlenderbotPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blenderbot import (
                  TFBlenderbotForConditionalGeneration,
                  TFBlenderbotModel,
                  TFBlenderbotPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_blenderbot import (
                  FlaxBlenderbotForConditionalGeneration,
                  FlaxBlenderbotModel,
                  FlaxBlenderbotPreTrainedModel,
              )

else:
       import sys

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