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_    					=       logging.get_logger(__name__)

a_    					=       {
    '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 		_lowercase						(					snake_case_       ):
 lowercase				     =   'mobilenet_v2'

 def __init__( self	:							List[str]   ,      snake_case	:							List[Any]=3   ,      snake_case	:							Optional[Any]=2_2_4   ,      snake_case	:							int=1.0   ,      snake_case	:							Dict=8   ,      snake_case	:							Union[str, Any]=8   ,      snake_case	:							str=6   ,      snake_case	:							List[str]=3_2   ,      snake_case	:							Tuple=True   ,      snake_case	:							Union[str, Any]=True   ,      snake_case	:							List[str]="relu6"   ,      snake_case	:							List[Any]=True   ,      snake_case	:							Tuple=0.8   ,      snake_case	:							Optional[Any]=0.02   ,      snake_case	:							List[str]=0.001   ,      snake_case	:							List[str]=2_5_5   ,      **snake_case	:							Optional[Any]   ,      )   ->						Dict:

  """simple docstring"""





  super().__init__(**snake_case			)

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

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





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   version.parse('1.11'       )

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

  """simple docstring"""





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

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						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 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						float:

  """simple docstring"""





  return 1e-4




from torch import nn





class 		_lowercase						(					nn.Module       ):

 def __init__( self	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_					:						List[Any] =					class_size
  UpperCamelCase_					:						List[Any] =					embed_size
  # self.mlp1 = nn.Linear(embed_size, embed_size)
  # self.mlp2 = (nn.Linear(embed_size, class_size))
  UpperCamelCase_					:						int =					nn.Linear(snake_case   ,      snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.mlp(snake_case			)
  return logits



