


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							:							List[Any]    							=					logging.get_logger(__name__)

__UpperCamelCase							:							Tuple    							=					{
    '''apple/mobilevitv2-1.0''': '''https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json''',
}

class    __magic_name__						(  lowercase__):
     A:		Optional[int]								=   """mobilevitv2"""


     def __init__(   self				:  str				,						lowerCamelCase__				:  Optional[Any]=3				,						lowerCamelCase__				:  Any=256				,						lowerCamelCase__				:  Optional[int]=2				,						lowerCamelCase__				:  str=2.0				,						lowerCamelCase__				:  Tuple="swish"				,						lowerCamelCase__				:  str=3				,						lowerCamelCase__				:  int=32				,						lowerCamelCase__				:  int=0.1				,						lowerCamelCase__				:  List[Any]=0.02				,						lowerCamelCase__				:  Union[str, Any]=1E-5				,						lowerCamelCase__				:  str=512				,						lowerCamelCase__				:  Union[str, Any]=[6, 12, 18]				,						lowerCamelCase__				:  Optional[Any]=0.1				,						lowerCamelCase__				:  List[Any]=255				,						lowerCamelCase__				:  int=[2, 4, 3]				,						lowerCamelCase__				:  Any=[128, 192, 256]				,						lowerCamelCase__				:  int=1.0				,						lowerCamelCase__				:  Union[str, Any]=2				,						lowerCamelCase__				:  Optional[int]=0.0				,						lowerCamelCase__				:  Union[str, Any]=0.0				,						**lowerCamelCase__				:  List[Any]				,						)	->   Tuple:





      '''simple docstring'''






      super().__init__(**lowerCamelCase__      )

      UpperCamelCase__       :     int 							= num_channels
      UpperCamelCase__       :     Tuple 							= image_size
      UpperCamelCase__       :     Optional[Any] 							= patch_size
      UpperCamelCase__       :     Dict 							= expand_ratio
      UpperCamelCase__       :     List[Any] 							= hidden_act
      UpperCamelCase__       :     List[Any] 							= conv_kernel_size
      UpperCamelCase__       :     str 							= output_stride
      UpperCamelCase__       :     str 							= initializer_range
      UpperCamelCase__       :     Optional[int] 							= layer_norm_eps
      UpperCamelCase__       :     Optional[int] 							= n_attn_blocks
      UpperCamelCase__       :     Tuple 							= base_attn_unit_dims
      UpperCamelCase__       :     List[Any] 							= width_multiplier
      UpperCamelCase__       :     Dict 							= ffn_multiplier
      UpperCamelCase__       :     List[str] 							= ffn_dropout
      UpperCamelCase__       :     Dict 							= attn_dropout
      UpperCamelCase__       :     Any 							= classifier_dropout_prob

      # decode head attributes for semantic segmentation
      UpperCamelCase__       :     Dict 							= aspp_out_channels
      UpperCamelCase__       :     int 							= atrous_rates
      UpperCamelCase__       :     Union[str, Any] 							= aspp_dropout_prob
      UpperCamelCase__       :     Dict 							= semantic_loss_ignore_index



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


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





      '''simple docstring'''






      return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]      )


     @property
     def   UpperCAmelCase__						(   self				:  Optional[int]      )	->   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				:  str      )	->   float:





      '''simple docstring'''






      return 1E-4








from __future__ import annotations

import sys
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    if n < 0:
        raise ValueError("""Negative arguments are not supported"""		)
    return _fib(UpperCAmelCase_		)[0]
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    if n == 0:  # (F(0), F(1))
        return (0, 1)

    # F(2n) = F(n)[2F(n+1) − F(n)]
    # F(2n+1) = F(n+1)^2+F(n)^2
    a__			,		a__  			=							_fib(n // 2		)
    a__  			=							a * (b * 2 - a)
    a__  			=							a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_    :       Dict       =							int(sys.argv[1])
 print(F"fibonacci({n}) is {fibonacci(n)}")






