

import random



class 					A__:
						"""simple docstring"""






						def __init__(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase = 0.0_1     ,			_lowercase = 1_000     ,			_lowercase = -1     ,			)    ->   None:
									a_	:	Optional[Any]          =   sample
									if len(self.sample						) == 0:
												raise ValueError("""Sample data can not be empty"""						)
									a_	:	Optional[Any]          =   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"""						)
									a_	:	List[str]          =   learning_rate
									a_	:	Optional[Any]          =   epoch_number
									a_	:	Union[str, Any]          =   bias
									a_	:	Optional[Any]          =   len(_lowercase						)
									a_	:	List[Any]          =   len(sample[0]						)  # number of columns in dataset
									a_	:	list          =   []





						def  UpperCamelCase__				(						self						)    ->   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						)

									a_	:	Dict          =   0

									while True:
												a_	:	Optional[Any]          =   False
												for i in range(self.number_sample						):
															a_	:	List[str]          =   0
															for j in range(self.col_sample + 1						):
																		a_	:	str          =   u + self.weight[j] * self.sample[i][j]
															a_	:	str          =   self.sign(_lowercase						)
															if y != self.target[i]:
																		for j in range(self.col_sample + 1						):
																					a_	:	Union[str, Any]          =   (
																					    self.weight[j]
																					    + self.learning_rate
																					    * (self.target[i] - y)
																					    * self.sample[i][j]
																					)
																		a_	:	Any          =   True
            # print('Epoch: \n',epoch_count)
												a_	:	List[Any]          =   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     ,			_lowercase						)    ->   None:
									if len(self.sample						) == 0:
												raise ValueError("""Sample data can not be empty"""						)
									sample.insert(0     ,			self.bias						)
									a_	:	int          =   0
									for i in range(self.col_sample + 1						):
												a_	:	Union[str, Any]          =   u + self.weight[i] * sample[i]

									a_	:	Optional[Any]          =   self.sign(_lowercase						)

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





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   int:
									return 1 if u >= 0 else -1







__snake_case							:			Optional[int]      		= [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

__snake_case							:			str      		= [
    -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()

	__snake_case							:			List[str]      		= Perceptron(
	    sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
	)
	network.training()
	print("""Finished training perceptron""")
	print("""Enter values to predict or q to exit""")
	while True:
		__snake_case							:			list      		= []
		for i in range(len(samples[0])):
			__snake_case							:			List[str]      		= input("""value: """).strip()
			if user_input == "q":
				break
			__snake_case							:			Union[str, Any]      		= float(user_input)
			sample.insert(i, observation)
		network.sort(sample)





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__snake_case							:			int      		= logging.get_logger(__name__)

__snake_case							:			Dict      		= {
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class 					A__(a_       ):
						"""simple docstring"""






						_A						:							Dict									= '''vit-hybrid'''





						def __init__(						self     ,			_lowercase=None     ,			_lowercase=768     ,			_lowercase=12     ,			_lowercase=12     ,			_lowercase=3_072     ,			_lowercase="gelu"     ,			_lowercase=0.0     ,			_lowercase=0.0     ,			_lowercase=0.0_2     ,			_lowercase=1e-12     ,			_lowercase=224     ,			_lowercase=1     ,			_lowercase=3     ,			_lowercase=[1, 1_024, 24, 24]     ,			_lowercase=True     ,			**_lowercase     ,			)    ->   Tuple:
									super().__init__(**_lowercase						)

									if backbone_config is None:
												logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""						)
												a_	:	Union[str, Any]          =   {
												    """global_padding""": """same""",
												    """layer_type""": """bottleneck""",
												    """depths""": [3, 4, 9],
												    """out_features""": ["""stage3"""],
												    """embedding_dynamic_padding""": True,
												}

									if isinstance(_lowercase     ,			_lowercase						):
												if "model_type" in backbone_config:
															a_	:	Any          =   CONFIG_MAPPING[backbone_config["""model_type"""]]
												else:
															logger.info(
															    """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""						)
															a_	:	Optional[int]          =   BitConfig
												a_	:	Dict          =   backbone_config_class(**_lowercase						)

									a_	:	List[str]          =   backbone_featmap_shape
									a_	:	int          =   backbone_config
									a_	:	Union[str, Any]          =   hidden_size
									a_	:	Optional[int]          =   num_hidden_layers
									a_	:	Dict          =   num_attention_heads
									a_	:	Optional[int]          =   intermediate_size
									a_	:	Dict          =   hidden_act
									a_	:	Optional[Any]          =   hidden_dropout_prob
									a_	:	List[str]          =   attention_probs_dropout_prob
									a_	:	Tuple          =   initializer_range
									a_	:	Any          =   layer_norm_eps
									a_	:	List[Any]          =   image_size
									a_	:	Optional[Any]          =   patch_size
									a_	:	Optional[Any]          =   num_channels
									a_	:	str          =   qkv_bias





						def  UpperCamelCase__				(						self						)    ->   Dict[str, any]:
									a_	:	List[str]          =   copy.deepcopy(self.__dict__						)
									a_	:	Optional[int]          =   self.backbone_config.to_dict()
									a_	:	List[str]          =   self.__class__.model_type
									return output


