

"""simple docstring"""

import random


class 			_snake_case     :







						def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.0_1     ,							UpperCAmelCase_ = 1_0_0_0     ,							UpperCAmelCase_ = -1     ,							)			->						int:
											__lowercase												=			sample
											if len(self.sample							) == 0:
																raise ValueError('''Sample data can not be empty'''							)
											__lowercase												=			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												=			learning_rate
											__lowercase												=			epoch_number
											__lowercase												=			bias
											__lowercase												=			len(__SCREAMING_SNAKE_CASE							)
											__lowercase												=			len(sample[0]							)  # number of columns in dataset
											__lowercase												=			[]







						def        _UpperCamelCase    (self							)			->						Any:

											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												=			0

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

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

											__lowercase												=			self.sign(__SCREAMING_SNAKE_CASE							)

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







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						Optional[int]:
											return 1 if u >= 0 else -1



a__ :	Tuple						= [
    [-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],
]

a__ :	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()

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



"""simple docstring"""

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


a__ :	List[Any]						= logging.getLogger(__name__)


@dataclass
class 			_snake_case     :
						__snake_case 			=     field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
						__snake_case 			=     field(default=UpperCamelCase						,			metadata={'help': 'Set this flag to use fast tokenization.'}       )
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						__snake_case 			=     field(
						    default=UpperCamelCase						,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,			)





def 				__UpperCamelCase   (    )				->			Union[str, Any]:




					"""simple docstring"""



					__lowercase												=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)

					if len(sys.argv			) == 2 and sys.argv[1].endswith('''.json'''			):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										__lowercase						, __lowercase						, __lowercase												=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
					else:
										__lowercase						, __lowercase						, __lowercase												=			parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir			)
					    and os.listdir(training_args.output_dir			)
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
										    ''' --overwrite_output_dir to overcome.'''			)

					# Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,	datefmt='''%m/%d/%Y %H:%M:%S''' ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,	)
					logger.warning(
					    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' ,	training_args.local_rank ,	training_args.device ,	training_args.n_gpu ,	bool(training_args.local_rank != -1			) ,	training_args.fpaa ,	)
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank			):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info('''Training/evaluation parameters %s''' ,	a__			)

					# Prepare Question-Answering task
					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					__lowercase												=			AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	)
					__lowercase												=			AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	use_fast=a__ ,	)
					__lowercase												=			AutoModelForQuestionAnswering.from_pretrained(
					    model_args.model_name_or_path ,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path			) ,	config=a__ ,	cache_dir=model_args.cache_dir ,	)

					# Get datasets
					__lowercase												=			hasattr(model.config ,	'''lang2id'''			)
					__lowercase												=			(
					    SquadDataset(
					        a__ ,	tokenizer=a__ ,	is_language_sensitive=a__ ,	cache_dir=model_args.cache_dir			)
					    if training_args.do_train
					    else None
					)
					__lowercase												=			(
					    SquadDataset(
					        a__ ,	tokenizer=a__ ,	mode='''dev''' ,	is_language_sensitive=a__ ,	cache_dir=model_args.cache_dir ,	)
					    if training_args.do_eval
					    else None
					)

					# Data collator
					__lowercase												=			DataCollatorWithPadding(a__ ,	pad_to_multiple_of=8			) if training_args.fpaa else None

					# Initialize our Trainer
					__lowercase												=			Trainer(
					    model=a__ ,	args=a__ ,	train_dataset=a__ ,	eval_dataset=a__ ,	data_collator=a__ ,	)

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir			)





def 				__UpperCamelCase   (    a__: Any			)				->			Any:




					"""simple docstring"""



					main()


if __name__ == "__main__":
							main()


