

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			)     ->						list[int]:
      SCREAMING_SNAKE_CASE :      int       =   [0 for i in range(len(_A			)			)]

      # initialize interval's left pointer and right pointer
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   0, 0

      for i in range(1						,    len(_A			)			):
            # case when current index is inside the interval
            if i <= right_pointer:
                  SCREAMING_SNAKE_CASE :      Any       =   min(right_pointer - i + 1						,    z_result[i - left_pointer]			)
                  SCREAMING_SNAKE_CASE :      Optional[Any]       =   min_edge

            while go_next(_A						,    _A						,    _A			):
                  z_result[i] += 1

            # if new index's result gives us more right interval,
            # we've to update left_pointer and right_pointer
            if i + z_result[i] - 1 > right_pointer:
                  SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[Any]       =   i, i + z_result[i] - 1

      return z_result






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						bool:
      return i + z_result[i] < len(_A			) and s[z_result[i]] == s[i + z_result[i]]






def 					__lowercase      (     _A						,    _A			)     ->						int:
      SCREAMING_SNAKE_CASE :      Any       =   0
      # concatenate 'pattern' and 'input_str' and call z_function
      # with concatenated string
      SCREAMING_SNAKE_CASE :      List[Any]       =   z_function(pattern + input_str			)

      for val in z_result:
            # if value is greater then length of the pattern string
            # that means this index is starting position of substring
            # which is equal to pattern string
            if val >= len(_A			):
                  answer += 1

      return answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()




