import random



class 						snake_case_ :






   def __init__(   self       :  List[str]			,		lowercase_       :  list[list[float]]			,		lowercase_       :  list[int]			,		lowercase_       :  float = 0.01			,		lowercase_       :  int = 10_00			,		lowercase_       :  float = -1			,		)      ->		None:
       lowercase__				:     Optional[Any]					      =       sample
       if len(self.sample						) == 0:
           raise ValueError("Sample data can not be empty"						)
       lowercase__				:     int					      =       target
       if len(self.target						) == 0:
           raise ValueError("Target data can not be empty"						)
       if len(self.sample						) != len(self.target						):
           raise ValueError("Sample data and Target data do not have matching lengths"						)
       lowercase__				:     Union[str, Any]					      =       learning_rate
       lowercase__				:     int					      =       epoch_number
       lowercase__				:     Optional[Any]					      =       bias
       lowercase__				:     List[str]					      =       len(lowercase_						)
       lowercase__				:     int					      =       len(sample[0]						)  # number of columns in dataset
       lowercase__				:     list					      =       []






   def    __UpperCamelCase  (   self       :  Tuple						)      ->		None:
       for sample in self.sample:
           sample.insert(0			,		self.bias						)

       for _ in range(self.col_sample						):
           self.weight.append(random.random()						)

       self.weight.insert(0			,		self.bias						)

       lowercase__				:     str					      =       0

       while True:
           lowercase__				:     Optional[int]					      =       False
           for i in range(self.number_sample						):
               lowercase__				:     List[str]					      =       0
               for j in range(self.col_sample + 1						):
                   lowercase__				:     Optional[int]					      =       u + self.weight[j] * self.sample[i][j]
               lowercase__				:     Tuple					      =       self.sign(lowercase_						)
               if y != self.target[i]:
                   for j in range(self.col_sample + 1						):
                       lowercase__				:     List[Any]					      =       (
                           self.weight[j]
                           + self.learning_rate
                           * (self.target[i] - y)
                           * self.sample[i][j]
                       )
                   lowercase__				:     Union[str, Any]					      =       True
            # print('Epoch: \n',epoch_count)
           lowercase__				:     List[str]					      =       epoch_count + 1
           # if you want control the epoch or just by error
           if not has_misclassified:
               print(("\nEpoch:\n", epoch_count)						)
               print("------------------------\n"						)
               # if epoch_count > self.epoch_number or not error:
               break






   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  list[float]						)      ->		None:
       if len(self.sample						) == 0:
           raise ValueError("Sample data can not be empty"						)
       sample.insert(0			,		self.bias						)
       lowercase__				:     str					      =       0
       for i in range(self.col_sample + 1						):
           lowercase__				:     List[Any]					      =       u + self.weight[i] * sample[i]

       lowercase__				:     List[Any]					      =       self.sign(lowercase_						)

       if y == -1:
           print(("Sample: ", sample)						)
           print("classification: P1"						)
       else:
           print(("Sample: ", sample)						)
           print("classification: P2"						)







   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  float						)      ->		int:
       return 1 if u >= 0 else -1



UpperCamelCase													=		[
    [-0.6_508, 0.1_097, 4.0_009],
    [-1.4_492, 0.8_896, 4.4_005],
    [2.0_850, 0.6_876, 12.0_710],
    [0.2_626, 1.1_476, 7.7_985],
    [0.6_418, 1.0_234, 7.0_427],
    [0.2_569, 0.6_730, 8.3_265],
    [1.1_155, 0.6_043, 7.4_446],
    [0.0_914, 0.3_399, 7.0_677],
    [0.0_121, 0.5_256, 4.6_316],
    [-0.0_429, 0.4_660, 5.4_323],
    [0.4_340, 0.6_870, 8.2_287],
    [0.2_735, 1.0_287, 7.1_934],
    [0.4_839, 0.4_851, 7.4_850],
    [0.4_089, -0.1_267, 5.5_019],
    [1.4_391, 0.1_614, 8.5_843],
    [-0.9_115, -0.1_973, 2.1_962],
    [0.3_654, 1.0_475, 7.4_858],
    [0.2_144, 0.7_515, 7.1_699],
    [0.2_013, 1.0_014, 6.5_489],
    [0.6_483, 0.2_183, 5.8_991],
    [-0.1_147, 0.2_242, 7.2_435],
    [-0.7_970, 0.8_795, 3.8_762],
    [-1.0_625, 0.6_366, 2.4_707],
    [0.5_307, 0.1_285, 5.6_883],
    [-1.2_200, 0.7_777, 1.7_252],
    [0.3_957, 0.1_076, 5.6_623],
    [-0.1_013, 0.5_989, 7.1_812],
    [2.4_482, 0.9_455, 11.2_095],
    [2.0_149, 0.6_192, 10.9_263],
    [0.2_012, 0.2_611, 5.4_631],
]

UpperCamelCase													=		[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCamelCase													=		Perceptron(
        sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
    )
    network.training()
    print('''Finished training perceptron''')
    print('''Enter values to predict or q to exit''')
    while True:
        UpperCamelCase													=		[]
        for i in range(len(samples[0])):
            UpperCamelCase													=		input('''value: ''').strip()
            if user_input == "q":
                break
            UpperCamelCase													=		float(user_input)
            sample.insert(i, observation)
        network.sort(sample)




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													=		logging.get_logger(__name__)

UpperCamelCase													=		{
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}



class 						snake_case_ (	__A ):
   __A			:	Any        =       "mobilevit"






   def __init__(   self       :  Union[str, Any]			,		lowercase_       :  List[Any]=3			,		lowercase_       :  List[Any]=2_56			,		lowercase_       :  Union[str, Any]=2			,		lowercase_       :  int=[1_44, 1_92, 2_40]			,		lowercase_       :  List[str]=[16, 32, 64, 96, 1_28, 1_60, 6_40]			,		lowercase_       :  Tuple=4			,		lowercase_       :  Optional[int]=2.0			,		lowercase_       :  int=4.0			,		lowercase_       :  str="silu"			,		lowercase_       :  Optional[Any]=3			,		lowercase_       :  Dict=32			,		lowercase_       :  Union[str, Any]=0.1			,		lowercase_       :  str=0.0			,		lowercase_       :  str=0.1			,		lowercase_       :  List[str]=0.02			,		lowercase_       :  Union[str, Any]=1E-5			,		lowercase_       :  Tuple=True			,		lowercase_       :  Optional[Any]=2_56			,		lowercase_       :  List[str]=[6, 12, 18]			,		lowercase_       :  Any=0.1			,		lowercase_       :  List[str]=2_55			,		**lowercase_       :  Tuple			,		)      ->		Optional[Any]:
       super().__init__(**lowercase_						)

       lowercase__				:     List[str]					      =       num_channels
       lowercase__				:     Optional[int]					      =       image_size
       lowercase__				:     Any					      =       patch_size
       lowercase__				:     int					      =       hidden_sizes
       lowercase__				:     str					      =       neck_hidden_sizes
       lowercase__				:     Optional[int]					      =       num_attention_heads
       lowercase__				:     Tuple					      =       mlp_ratio
       lowercase__				:     Tuple					      =       expand_ratio
       lowercase__				:     List[str]					      =       hidden_act
       lowercase__				:     Optional[int]					      =       conv_kernel_size
       lowercase__				:     Union[str, Any]					      =       output_stride
       lowercase__				:     Optional[int]					      =       hidden_dropout_prob
       lowercase__				:     List[str]					      =       attention_probs_dropout_prob
       lowercase__				:     Union[str, Any]					      =       classifier_dropout_prob
       lowercase__				:     Union[str, Any]					      =       initializer_range
       lowercase__				:     List[str]					      =       layer_norm_eps
       lowercase__				:     Dict					      =       qkv_bias

       # decode head attributes for semantic segmentation
       lowercase__				:     List[Any]					      =       aspp_out_channels
       lowercase__				:     List[Any]					      =       atrous_rates
       lowercase__				:     str					      =       aspp_dropout_prob
       lowercase__				:     int					      =       semantic_loss_ignore_index



class 						snake_case_ (	__A ):
   __A			:	int        =       version.parse("1.11" )






   @property
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Mapping[str, Mapping[int, str]]:
       return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]						)






   @property
   def    __UpperCamelCase  (   self       :  str						)      ->		Mapping[str, Mapping[int, str]]:
       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       :  Union[str, Any]						)      ->		float:
       return 1E-4



