

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A__							=				logging.getLogger(__name__)

@dataclass
class 					__UpperCamelCase					:
	_lowercase	: str												=	field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
	_lowercase	: Optional[str]												=	field(
	    default=_UpperCamelCase	,    metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
	_lowercase	: Optional[str]												=	field(
	    default=_UpperCamelCase	,    metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}      )
	_lowercase	: Optional[str]												=	field(
	    default=_UpperCamelCase	,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,    )




@dataclass
class 					__UpperCamelCase					:
	_lowercase	: str												=	field(
	    metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()      )}      )
	_lowercase	: str												=	field(
	    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}      )
	_lowercase	: int												=	field(
	    default=1_2_8	,    metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }	,    )
	_lowercase	: bool												=	field(
	    default=_UpperCamelCase	,    metadata={"help": "Overwrite the cached training and evaluation sets"}      )




def        _lowercase  (					a_					:       List[InputFeatures]					)					->						Optional[int]:




					'''simple docstring'''







					__magic_name__				=     default_data_collator(__A					)
					__magic_name__				=     batch.pop('pairID'       ,__A					)
					return batch




def        _lowercase  (					)					->						List[Any]:




					'''simple docstring'''







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

					# Set seed
					set_seed(training_args.seed					)

					try:
										__magic_name__				=     hans_tasks_num_labels[data_args.task_name]
					except KeyError:
										raise ValueError('Task not found: %s' % (data_args.task_name)					)

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

					__magic_name__				=     AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,num_labels=__A       ,finetuning_task=data_args.task_name       ,cache_dir=model_args.cache_dir       ,)
					__magic_name__				=     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       ,)
					__magic_name__				=     AutoModelForSequenceClassification.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
					__magic_name__				=     (
					    HansDataset(
					        data_dir=data_args.data_dir       ,tokenizer=__A       ,task=data_args.task_name       ,max_seq_length=data_args.max_seq_length       ,overwrite_cache=data_args.overwrite_cache       ,)
					    if training_args.do_train
					    else None
					)
					__magic_name__				=     (
					    HansDataset(
					        data_dir=data_args.data_dir       ,tokenizer=__A       ,task=data_args.task_name       ,max_seq_length=data_args.max_seq_length       ,overwrite_cache=data_args.overwrite_cache       ,evaluate=__A       ,)
					    if training_args.do_eval
					    else None
					)

					# Initialize our Trainer
					__magic_name__				=     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					)

    # Evaluation
					if training_args.do_eval:
										logger.info('*** Evaluate ***'					)

										__magic_name__				=     trainer.predict(__A					)
										__magic_name__				=     output.predictions
										__magic_name__				=     np.argmax(__A       ,axis=1					)

										__magic_name__				=     [ex.pairID for ex in eval_dataset]
										__magic_name__				=     os.path.join(training_args.output_dir       ,'hans_predictions.txt'					)
										__magic_name__				=     eval_dataset.get_labels()
										if trainer.is_world_master():
															with open(__A       ,'w'					) as writer:
																				writer.write('pairID,gold_label\n'					)
																				for pid, pred in zip(__A       ,__A					):
																									writer.write('ex' + str(__A					) + ',' + label_list[int(__A					)] + '\n'					)

										trainer._log(output.metrics					)




def        _lowercase  (					a_					:       List[Any]					)					->						Optional[Any]:




					'''simple docstring'''







					main()


if __name__ == "__main__":
						main()












"""simple docstring"""



import random




class 							__lowercase:






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = 0.01     ,    _SCREAMING_SNAKE_CASE = 10_00     ,    _SCREAMING_SNAKE_CASE = -1     ,    )      ->	None:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	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)

											lowerCamelCase__					=0

											while True:
																lowerCamelCase__					=False
																for i in range(self.number_sample):
																					lowerCamelCase__					=0
																					for j in range(self.col_sample + 1):
																										lowerCamelCase__					=u + self.weight[j] * self.sample[i][j]
																					lowerCamelCase__					=self.sign(_SCREAMING_SNAKE_CASE)
																					if y != self.target[i]:
																										for j in range(self.col_sample + 1):
																															lowerCamelCase__					=(
																															    self.weight[j]
																															    + self.learning_rate
																															    * (self.target[i] - y)
																															    * self.sample[i][j]
																															)
																										lowerCamelCase__					=True
            # print('Epoch: \n',epoch_count)
																lowerCamelCase__					=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 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	None:




											"""simple docstring"""





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

											lowerCamelCase__					=self.sign(_SCREAMING_SNAKE_CASE)

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






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	int:




											"""simple docstring"""





											return 1 if u >= 0 else -1



_snake_case							:   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							:   List[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							:   int	  =       Perceptron(
						    sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, 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							:   Optional[Any]	  =       float(user_input)
																		sample.insert(i, observation)
												network.sort(sample)