

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__ :     Optional[Any]				    =    logging.get_logger(__name__)

UpperCAmelCase__ :     Union[str, 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        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Optional[int]			       ="""mobilenet_v2"""


  def __init__(			self    : Any				,				UpperCAmelCase__    : Optional[int]=3				,				UpperCAmelCase__    : Optional[Any]=2_2_4				,				UpperCAmelCase__    : Union[str, Any]=1.0				,				UpperCAmelCase__    : Union[str, Any]=8				,				UpperCAmelCase__    : str=8				,				UpperCAmelCase__    : Optional[Any]=6				,				UpperCAmelCase__    : List[Any]=3_2				,				UpperCAmelCase__    : str=True				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : Dict="relu6"				,				UpperCAmelCase__    : Union[str, Any]=True				,				UpperCAmelCase__    : List[Any]=0.8				,				UpperCAmelCase__    : List[Any]=0.02				,				UpperCAmelCase__    : Optional[Any]=0.0_01				,				UpperCAmelCase__    : str=2_5_5				,				**UpperCAmelCase__    : Dict				,				)					->Any:
        """simple docstring"""







        super().__init__(**UpperCAmelCase__		)

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

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







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Optional[int]			       =version.parse("""1.11"""       )


  @property
  def  _lowercase      (			self    : Tuple		)					->Mapping[str, Mapping[int, str]]:
        """simple docstring"""







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


  @property
  def  _lowercase      (			self    : Dict		)					->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  _lowercase      (			self    : Dict		)					->float:
        """simple docstring"""







        return 1e-4







def 					__lowercase      (     _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   """"""
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups






def 					__lowercase      (     _A			)     ->						dict[str, str]:
      SCREAMING_SNAKE_CASE :      int       =   [chr(i + 65			) for i in range(26			)]
      # Remove duplicate characters from key
      SCREAMING_SNAKE_CASE :      List[str]       =   remove_duplicates(key.upper()			)
      SCREAMING_SNAKE_CASE :      Dict       =   len(_A			)
      # First fill cipher with key characters
      SCREAMING_SNAKE_CASE :      int       =   {alphabet[i]: char for i, char in enumerate(_A			)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(_A			)						,    26			):
            SCREAMING_SNAKE_CASE :      List[Any]       =   alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  SCREAMING_SNAKE_CASE :      str       =   alphabet[i - offset]
            SCREAMING_SNAKE_CASE :      List[Any]       =   char
      return cipher_alphabet






def 					__lowercase      (     _A						,    _A			)     ->						str:
      return "".join(cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     _A						,    _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     )     ->						None:
      SCREAMING_SNAKE_CASE :      List[Any]       =   input("""Enter message to encode or decode: """			).strip()
      SCREAMING_SNAKE_CASE :      str       =   input("""Enter keyword: """			).strip()
      SCREAMING_SNAKE_CASE :      Dict       =   input("""Encipher or decipher? E/D:"""			).strip()[0].lower()
      try:
            SCREAMING_SNAKE_CASE :      int       =   {"""e""": encipher, """d""": decipher}[option]
      except KeyError:
            raise KeyError("""invalid input option"""			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   create_cipher_map(_A			)
      print(func(_A						,    _A			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




