



import random



class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = 0.01	, lowerCamelCase_ = 1000	, lowerCamelCase_ = -1	, ):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""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	)

						a        =     0

						while True:
											a        =     False
											for i in range(self.number_sample	):
																a        =     0
																for j in range(self.col_sample + 1	):
																					a        =     u + self.weight[j] * self.sample[i][j]
																a        =     self.sign(lowerCamelCase_	)
																if y != self.target[i]:
																					for j in range(self.col_sample + 1	):
																										a        =     (
																										    self.weight[j]
																										    + self.learning_rate
																										    * (self.target[i] - y)
																										    * self.sample[i][j]
																										)
																					a        =     True
            # print('Epoch: \n',epoch_count)
											a        =     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	, lowerCamelCase_	):
						"""simple docstring"""

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

						a        =     self.sign(lowerCamelCase_	)

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




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						return 1 if u >= 0 else -1




_lowercase:				Optional[int]       =	[
    [-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],
]

_lowercase:				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()

				_lowercase:				Optional[Any]       =	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:
								_lowercase:				list       =	[]
								for i in range(len(samples[0])):
												_lowercase:				Optional[int]       =	input("value: ").strip()
												if user_input == "q":
																break
												_lowercase:				Union[str, Any]       =	float(user_input)
												sample.insert(i, observation)
								network.sort(sample)





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.get_auto_remove_tmp_dir()
						a        =     F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						a        =     get_gpu_count()
						a        =     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
						a        =     [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase_	, env=self.get_env()	)

						a        =     os.path.join(lowerCamelCase_	, "test_bleu.json"	)
						a        =     load_json(lowerCamelCase_	)
						# print(metrics)
						self.assertGreaterEqual(metrics["bleu"]	, 25	)
