import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowerCamelCase    :      Union[str, Any]	 =						[(1,)]





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				bool(os.getenv("RUN_SLOW"					,       0     )     )
					if run_slow:
										assert True
					else:
										assert False, "should have been skipped"





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				bool(os.getenv("RUN_SLOW"					,       0     )     )
					if run_slow and torch_device == "cuda":
										assert True
					else:
										assert False, "should have been skipped"


@require_torch
class   lowercase			(   unittest.TestCase   ):




							@slow
							@require_torch_gpu
							def 		__snake_case(  self      :    str					)    -> int:



												'''simple docstring'''

												check_slow_torch_cuda()




							@require_torch_gpu
							@slow
							def 		__snake_case(  self      :    Optional[int]					)    -> int:



												'''simple docstring'''

												check_slow_torch_cuda()





							@parameterized.expand(_UpperCamelCase					)
							@slow
							def 		__snake_case(  self      :    Tuple		,							_UpperCamelCase      :    List[str]=None					)    -> str:



												'''simple docstring'''

												check_slow()





@slow
@require_torch_gpu
def 		__lowerCamelCase							():
					check_slow_torch_cuda()





@require_torch_gpu
@slow
def 		__lowerCamelCase							():
					check_slow_torch_cuda()





@slow
@pytest.mark.parametrize("param"					,       [1]     )
def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]     ):
					check_slow()





@pytest.mark.parametrize("param"					,       [1]     )
@slow
def 		__lowerCamelCase							(UpperCAmelCase__   :							Dict     ):
					check_slow()





import random


class   lowercase			:




							def __init__(  self      :    List[Any]		,							_UpperCamelCase      :    list[list[float]]		,							_UpperCamelCase      :    list[int]		,							_UpperCamelCase      :    float = 0.0_1		,							_UpperCamelCase      :    int = 1_000		,							_UpperCamelCase      :    float = -1		,							)    -> None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				sample
												if len(self.sample					) == 0:
																	raise ValueError("Sample data can not be empty"					)
												SCREAMING_SNAKE_CASE				=				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"					)
												SCREAMING_SNAKE_CASE				=				learning_rate
												SCREAMING_SNAKE_CASE				=				epoch_number
												SCREAMING_SNAKE_CASE				=				bias
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				len(sample[0]					)  # number of columns in dataset
												SCREAMING_SNAKE_CASE				=				[]




							def 		__snake_case(  self      :    Any					)    -> None:



												'''simple docstring'''

												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					)

												SCREAMING_SNAKE_CASE				=				0

												while True:
																	SCREAMING_SNAKE_CASE				=				False
																	for i in range(self.number_sample					):
																						SCREAMING_SNAKE_CASE				=				0
																						for j in range(self.col_sample + 1					):
																											SCREAMING_SNAKE_CASE				=				u + self.weight[j] * self.sample[i][j]
																						SCREAMING_SNAKE_CASE				=				self.sign(_UpperCamelCase					)
																						if y != self.target[i]:
																											for j in range(self.col_sample + 1					):
																																SCREAMING_SNAKE_CASE				=				(
																																    self.weight[j]
																																    + self.learning_rate
																																    * (self.target[i] - y)
																																    * self.sample[i][j]
																																)
																											SCREAMING_SNAKE_CASE				=				True
            # print('Epoch: \n',epoch_count)
																	SCREAMING_SNAKE_CASE				=				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 		__snake_case(  self      :    Union[str, Any]		,							_UpperCamelCase      :    list[float]					)    -> None:



												'''simple docstring'''

												if len(self.sample					) == 0:
																	raise ValueError("Sample data can not be empty"					)
												sample.insert(0		,							self.bias					)
												SCREAMING_SNAKE_CASE				=				0
												for i in range(self.col_sample + 1					):
																	SCREAMING_SNAKE_CASE				=				u + self.weight[i] * sample[i]

												SCREAMING_SNAKE_CASE				=				self.sign(_UpperCamelCase					)

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





							def 		__snake_case(  self      :    Dict		,							_UpperCamelCase      :    float					)    -> int:



												'''simple docstring'''

												return 1 if u >= 0 else -1



_lowerCamelCase    :      Union[str, Any]	 =						[
    [-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],
]

_lowerCamelCase    :      Optional[Any]	 =						[
    -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()

							_lowerCamelCase    :      Tuple	 =						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:
														_lowerCamelCase    :      list	 =						[]
														for i in range(len(samples[0])):
																					_lowerCamelCase    :      Dict	 =						input('''value: ''').strip()
																					if user_input == "q":
																												break
																					_lowerCamelCase    :      Any	 =						float(user_input)
																					sample.insert(i, observation)
														network.sort(sample)




