

import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__A      		=     logging.getLogger(__name__)



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     str:



		"""simple docstring"""






		_snake_case						=							np.argmax(_UpperCamelCase			,	axis=1					)
		return np.sum(outputs == labels					)



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



		"""simple docstring"""






		with open(_UpperCamelCase			,	encoding='''utf_8'''					) as f:
				_snake_case						=							csv.reader(_UpperCamelCase					)
				_snake_case						=							[]
				next(_UpperCamelCase					)  # skip the first line
				for line in tqdm(_UpperCamelCase					):
						output.append((''' '''.join(line[1:5]					), line[5], line[6], int(line[-1]					) - 1)					)
		return output



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     int:



		"""simple docstring"""






		_snake_case						=							[]
		for dataset in encoded_datasets:
				_snake_case						=							len(_UpperCamelCase					)
				_snake_case						=							np.zeros((n_batch, 2, input_len)			,	dtype=np.intaa					)
				_snake_case						=							np.zeros((n_batch, 2)			,	dtype=np.intaa					)
				_snake_case						=							np.full((n_batch, 2, input_len)			,	fill_value=-100			,	dtype=np.intaa					)
				_snake_case						=							np.zeros((n_batch,)			,	dtype=np.intaa					)
				for (
				    i,
				    (story, conta, conta, mc_label),
				) in enumerate(_UpperCamelCase					):
						_snake_case						=							[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						_snake_case						=							[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						_snake_case						=							with_conta
						_snake_case						=							with_conta
						_snake_case						=							len(_UpperCamelCase					) - 1
						_snake_case						=							len(_UpperCamelCase					) - 1
						_snake_case						=							with_conta
						_snake_case						=							with_conta
						_snake_case						=							mc_label
				_snake_case						=							(input_ids, mc_token_ids, lm_labels, mc_labels)
				tensor_datasets.append(tuple(torch.tensor(_UpperCamelCase					) for t in all_inputs					)					)
		return tensor_datasets



def 				snake_case_()							->     Tuple:



		"""simple docstring"""






		_snake_case						=							argparse.ArgumentParser()
		parser.add_argument('''--model_name'''			,	type=_UpperCamelCase			,	default='''openai-gpt'''			,	help='''pretrained model name'''					)
		parser.add_argument('''--do_train'''			,	action='''store_true'''			,	help='''Whether to run training.'''					)
		parser.add_argument('''--do_eval'''			,	action='''store_true'''			,	help='''Whether to run eval on the dev set.'''					)
		parser.add_argument(
		    '''--output_dir'''			,	default=_UpperCamelCase			,	type=_UpperCamelCase			,	required=_UpperCamelCase			,	help='''The output directory where the model predictions and checkpoints will be written.'''			,	)
		parser.add_argument('''--train_dataset'''			,	type=_UpperCamelCase			,	default=''''''					)
		parser.add_argument('''--eval_dataset'''			,	type=_UpperCamelCase			,	default=''''''					)
		parser.add_argument('''--seed'''			,	type=_UpperCamelCase			,	default=42					)
		parser.add_argument('''--num_train_epochs'''			,	type=_UpperCamelCase			,	default=3					)
		parser.add_argument('''--train_batch_size'''			,	type=_UpperCamelCase			,	default=8					)
		parser.add_argument('''--eval_batch_size'''			,	type=_UpperCamelCase			,	default=16					)
		parser.add_argument('''--adam_epsilon'''			,	default=1E-8			,	type=_UpperCamelCase			,	help='''Epsilon for Adam optimizer.'''					)
		parser.add_argument('''--max_grad_norm'''			,	type=_UpperCamelCase			,	default=1					)
		parser.add_argument(
		    '''--max_steps'''			,	default=-1			,	type=_UpperCamelCase			,	help=(
		        '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
		    )			,	)
		parser.add_argument(
		    '''--gradient_accumulation_steps'''			,	type=_UpperCamelCase			,	default=1			,	help='''Number of updates steps to accumulate before                        performing a backward/update pass.'''			,	)
		parser.add_argument('''--learning_rate'''			,	type=_UpperCamelCase			,	default=6.25E-5					)
		parser.add_argument('''--warmup_steps'''			,	default=0			,	type=_UpperCamelCase			,	help='''Linear warmup over warmup_steps.'''					)
		parser.add_argument('''--lr_schedule'''			,	type=_UpperCamelCase			,	default='''warmup_linear'''					)
		parser.add_argument('''--weight_decay'''			,	type=_UpperCamelCase			,	default=0.01					)
		parser.add_argument('''--lm_coef'''			,	type=_UpperCamelCase			,	default=0.9					)
		parser.add_argument('''--n_valid'''			,	type=_UpperCamelCase			,	default=374					)

		parser.add_argument('''--server_ip'''			,	type=_UpperCamelCase			,	default=''''''			,	help='''Can be used for distant debugging.'''					)
		parser.add_argument('''--server_port'''			,	type=_UpperCamelCase			,	default=''''''			,	help='''Can be used for distant debugging.'''					)
		_snake_case						=							parser.parse_args()
		print(_UpperCamelCase					)

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print('''Waiting for debugger attach'''					)
				ptvsd.enable_attach(address=(args.server_ip, args.server_port)			,	redirect_output=_UpperCamelCase					)
				ptvsd.wait_for_attach()

		random.seed(args.seed					)
		np.random.seed(args.seed					)
		torch.manual_seed(args.seed					)
		torch.cuda.manual_seed_all(args.seed					)

		_snake_case						=							torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''					)
		_snake_case						=							torch.cuda.device_count()
		logger.info('''device: {}, n_gpu {}'''.format(_UpperCamelCase			,	_UpperCamelCase					)					)

		if not args.do_train and not args.do_eval:
				raise ValueError('''At least one of `do_train` or `do_eval` must be True.'''					)

		if not os.path.exists(args.output_dir					):
				os.makedirs(args.output_dir					)

		# Load tokenizer and model
		# This loading functions also add new tokens and embeddings called `special tokens`
		# These new embeddings will be fine-tuned on the RocStories dataset
		_snake_case						=							['''_start_''', '''_delimiter_''', '''_classify_''']
		_snake_case						=							OpenAIGPTTokenizer.from_pretrained(args.model_name					)
		tokenizer.add_tokens(_UpperCamelCase					)
		_snake_case						=							tokenizer.convert_tokens_to_ids(_UpperCamelCase					)
		_snake_case						=							OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name					)
		model.resize_token_embeddings(len(_UpperCamelCase					)					)
		model.to(_UpperCamelCase					)

		# Load and encode the datasets
		def tokenize_and_encode(_UpperCamelCase					):
				if isinstance(_UpperCamelCase			,	_UpperCamelCase					):
						return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_UpperCamelCase					)					)
				elif isinstance(_UpperCamelCase			,	_UpperCamelCase					):
						return obj
				return [tokenize_and_encode(_UpperCamelCase					) for o in obj]

		logger.info('''Encoding dataset...'''					)
		_snake_case						=							load_rocstories_dataset(args.train_dataset					)
		_snake_case						=							load_rocstories_dataset(args.eval_dataset					)
		_snake_case						=							(train_dataset, eval_dataset)
		_snake_case						=							tokenize_and_encode(_UpperCamelCase					)

		# Compute the max input length for the Transformer
		_snake_case						=							model.config.n_positions // 2 - 2
		_snake_case						=							max(
		    len(story[:max_length]					) + max(len(conta[:max_length]					)			,	len(conta[:max_length]					)					) + 3
		    for dataset in encoded_datasets
		    for story, conta, conta, _ in dataset					)
		_snake_case						=							min(_UpperCamelCase			,	model.config.n_positions					)  # Max size of input for the pre-trained model

		# Prepare inputs tensors and dataloaders
		_snake_case						=							pre_process_datasets(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	*_UpperCamelCase					)
		_snake_case,					_snake_case						=							tensor_datasets[0], tensor_datasets[1]

		_snake_case						=							TensorDataset(*_UpperCamelCase					)
		_snake_case						=							RandomSampler(_UpperCamelCase					)
		_snake_case						=							DataLoader(_UpperCamelCase			,	sampler=_UpperCamelCase			,	batch_size=args.train_batch_size					)

		_snake_case						=							TensorDataset(*_UpperCamelCase					)
		_snake_case						=							SequentialSampler(_UpperCamelCase					)
		_snake_case						=							DataLoader(_UpperCamelCase			,	sampler=_UpperCamelCase			,	batch_size=args.eval_batch_size					)

		# Prepare optimizer
		if args.do_train:
				if args.max_steps > 0:
						_snake_case						=							args.max_steps
						_snake_case						=							args.max_steps // (len(_UpperCamelCase					) // args.gradient_accumulation_steps) + 1
				else:
						_snake_case						=							len(_UpperCamelCase					) // args.gradient_accumulation_steps * args.num_train_epochs

				_snake_case						=							list(model.named_parameters()					)
				_snake_case						=							['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
				_snake_case						=							[
				    {
				        '''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay					)],
				        '''weight_decay''': args.weight_decay,
				    },
				    {'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay					)], '''weight_decay''': 0.0},
				]
				_snake_case						=							AdamW(_UpperCamelCase			,	lr=args.learning_rate			,	eps=args.adam_epsilon					)
				_snake_case						=							get_linear_schedule_with_warmup(
				    _UpperCamelCase			,	num_warmup_steps=args.warmup_steps			,	num_training_steps=_UpperCamelCase					)

		if args.do_train:
				_snake_case,					_snake_case,					_snake_case						=							0, 0, None
				model.train()
				for _ in trange(int(args.num_train_epochs					)			,	desc='''Epoch'''					):
						_snake_case						=							0
						_snake_case						=							0
						_snake_case						=							tqdm(_UpperCamelCase			,	desc='''Training'''					)
						for step, batch in enumerate(_UpperCamelCase					):
								_snake_case						=							tuple(t.to(_UpperCamelCase					) for t in batch					)
								_snake_case,					_snake_case,					_snake_case,					_snake_case						=							batch
								_snake_case						=							model(_UpperCamelCase			,	mc_token_ids=_UpperCamelCase			,	lm_labels=_UpperCamelCase			,	mc_labels=_UpperCamelCase					)
								_snake_case						=							args.lm_coef * losses[0] + losses[1]
								loss.backward()
								optimizer.step()
								scheduler.step()
								optimizer.zero_grad()
								tr_loss += loss.item()
								_snake_case						=							(
								    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
								)
								nb_tr_steps += 1
								_snake_case						=							'''Training loss: {:.2e} lr: {:.2e}'''.format(_UpperCamelCase			,	scheduler.get_lr()[0]					)

				# Save a trained model
		if args.do_train:
				# Save a trained model, configuration and tokenizer
				_snake_case						=							model.module if hasattr(_UpperCamelCase			,	'''module'''					) else model  # Only save the model itself

				# If we save using the predefined names, we can load using `from_pretrained`
				_snake_case						=							os.path.join(args.output_dir			,	_UpperCamelCase					)
				_snake_case						=							os.path.join(args.output_dir			,	_UpperCamelCase					)

				torch.save(model_to_save.state_dict()			,	_UpperCamelCase					)
				model_to_save.config.to_json_file(_UpperCamelCase					)
				tokenizer.save_vocabulary(args.output_dir					)

				# Load a trained model and vocabulary that you have fine-tuned
				_snake_case						=							OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir					)
				_snake_case						=							OpenAIGPTTokenizer.from_pretrained(args.output_dir					)
				model.to(_UpperCamelCase					)

		if args.do_eval:
				model.eval()
				_snake_case,					_snake_case						=							0, 0
				_snake_case,					_snake_case						=							0, 0
				for batch in tqdm(_UpperCamelCase			,	desc='''Evaluating'''					):
						_snake_case						=							tuple(t.to(_UpperCamelCase					) for t in batch					)
						_snake_case,					_snake_case,					_snake_case,					_snake_case						=							batch
						with torch.no_grad():
								_snake_case,					_snake_case,					_snake_case,					_snake_case						=							model(
								    _UpperCamelCase			,	mc_token_ids=_UpperCamelCase			,	lm_labels=_UpperCamelCase			,	mc_labels=_UpperCamelCase					)

						_snake_case						=							mc_logits.detach().cpu().numpy()
						_snake_case						=							mc_labels.to('''cpu'''					).numpy()
						_snake_case						=							accuracy(_UpperCamelCase			,	_UpperCamelCase					)

						eval_loss += mc_loss.mean().item()
						eval_accuracy += tmp_eval_accuracy

						nb_eval_examples += input_ids.size(0					)
						nb_eval_steps += 1

				_snake_case						=							eval_loss / nb_eval_steps
				_snake_case						=							eval_accuracy / nb_eval_examples
				_snake_case						=							tr_loss / nb_tr_steps if args.do_train else None
				_snake_case						=							{'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

				_snake_case						=							os.path.join(args.output_dir			,	'''eval_results.txt'''					)
				with open(_UpperCamelCase			,	'''w'''					) as writer:
						logger.info('''***** Eval results *****'''					)
						for key in sorted(result.keys()					):
								logger.info('''  %s = %s'''			,	_UpperCamelCase			,	str(result[key]					)					)
								writer.write('''%s = %s\n''' % (key, str(result[key]					))					)


if __name__ == "__main__":
	main()




from __future__ import annotations



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     bool:



  """simple docstring"""






  _snake_case						=							get_failure_array(_UpperCamelCase					)

  # 2) Step through text searching for pattern
  _snake_case,					_snake_case						=							0, 0  # index into text, pattern
  while i < len(_UpperCamelCase					):
    if pattern[j] == text[i]:
      if j == (len(_UpperCamelCase					) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _snake_case						=							failure[j - 1]
      continue
    i += 1
  return False



def 				snake_case_(_UpperCamelCase					)							->     list[int]:



  """simple docstring"""






  _snake_case						=							[0]
  _snake_case						=							0
  _snake_case						=							1
  while j < len(_UpperCamelCase					):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _snake_case						=							failure[i - 1]
      continue
    j += 1
    failure.append(_UpperCamelCase					)
  return failure


if __name__ == "__main__":
 # Test 1)
 __A      		=     '''abc1abc12'''
 __A      		=     '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 __A      		=     '''alskfjaldsk23adsfabcabc'''
 assert kmp(pattern, texta) and not kmp(pattern, texta)

 # Test 2)
 __A      		=     '''ABABX'''
 __A      		=     '''ABABZABABYABABX'''
 assert kmp(pattern, text)

 # Test 3)
 __A      		=     '''AAAB'''
 __A      		=     '''ABAAAAAB'''
 assert kmp(pattern, text)

 # Test 4)
 __A      		=     '''abcdabcy'''
 __A      		=     '''abcxabcdabxabcdabcdabcy'''
 assert kmp(pattern, text)

 # Test 5)
 __A      		=     '''aabaabaaa'''
 assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]

