'''simple docstring'''


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
)
__lowerCAmelCase	:       int						       =	logging.getLogger(__name__)





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Optional[int]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			np.argmax(UpperCamelCase__    ,					axis=1       )
	return np.sum(outputs == labels       )





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	with open(UpperCamelCase__    ,					encoding='''utf_8'''       ) as f:
		__UpperCAmelCase							=			csv.reader(UpperCamelCase__       )
		__UpperCAmelCase							=			[]
		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       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Optional[int]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	for dataset in encoded_datasets:
		__UpperCAmelCase							=			len(UpperCamelCase__       )
		__UpperCAmelCase							=			np.zeros((n_batch, 2, input_len)    ,					dtype=np.intaa       )
		__UpperCAmelCase							=			np.zeros((n_batch, 2)    ,					dtype=np.intaa       )
		__UpperCAmelCase							=			np.full((n_batch, 2, input_len)    ,					fill_value=-1_0_0    ,					dtype=np.intaa       )
		__UpperCAmelCase							=			np.zeros((n_batch,)    ,					dtype=np.intaa       )
		for (
		    i,
		    (story, conta, conta, mc_label),
		) in enumerate(UpperCamelCase__       ):
			__UpperCAmelCase							=			[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__UpperCAmelCase							=			[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__UpperCAmelCase							=			with_conta
			__UpperCAmelCase							=			with_conta
			__UpperCAmelCase							=			len(UpperCamelCase__       ) - 1
			__UpperCAmelCase							=			len(UpperCamelCase__       ) - 1
			__UpperCAmelCase							=			with_conta
			__UpperCAmelCase							=			with_conta
			__UpperCAmelCase							=			mc_label
		__UpperCAmelCase							=			(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       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			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=4_2       )
	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=1_6       )
	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=6.2_5E-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=3_7_4       )

	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.'''       )
	__UpperCAmelCase							=			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       )

	__UpperCAmelCase							=			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''       )
	__UpperCAmelCase							=			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
	__UpperCAmelCase							=			['''_start_''', '''_delimiter_''', '''_classify_''']
	__UpperCAmelCase							=			OpenAIGPTTokenizer.from_pretrained(args.model_name       )
	tokenizer.add_tokens(UpperCamelCase__       )
	__UpperCAmelCase							=			tokenizer.convert_tokens_to_ids(UpperCamelCase__       )
	__UpperCAmelCase							=			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__				:   Union[str, Any]       ):
		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...'''       )
	__UpperCAmelCase							=			load_rocstories_dataset(args.train_dataset       )
	__UpperCAmelCase							=			load_rocstories_dataset(args.eval_dataset       )
	__UpperCAmelCase							=			(train_dataset, eval_dataset)
	__UpperCAmelCase							=			tokenize_and_encode(UpperCamelCase__       )

	# Compute the max input length for the Transformer
	__UpperCAmelCase							=			model.config.n_positions // 2 - 2
	__UpperCAmelCase							=			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       )
	__UpperCAmelCase							=			min(UpperCamelCase__    ,					model.config.n_positions       )  # Max size of input for the pre-trained model

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

	__UpperCAmelCase							=			TensorDataset(*UpperCamelCase__       )
	__UpperCAmelCase							=			RandomSampler(UpperCamelCase__       )
	__UpperCAmelCase							=			DataLoader(UpperCamelCase__    ,					sampler=UpperCamelCase__    ,					batch_size=args.train_batch_size       )

	__UpperCAmelCase							=			TensorDataset(*UpperCamelCase__       )
	__UpperCAmelCase							=			SequentialSampler(UpperCamelCase__       )
	__UpperCAmelCase							=			DataLoader(UpperCamelCase__    ,					sampler=UpperCamelCase__    ,					batch_size=args.eval_batch_size       )

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

		__UpperCAmelCase							=			list(model.named_parameters()       )
		__UpperCAmelCase							=			['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
		__UpperCAmelCase							=			[
		    {
		        '''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},
		]
		__UpperCAmelCase							=			AdamW(UpperCamelCase__    ,					lr=args.learning_rate    ,					eps=args.adam_epsilon       )
		__UpperCAmelCase							=			get_linear_schedule_with_warmup(
		    UpperCamelCase__    ,					num_warmup_steps=args.warmup_steps    ,					num_training_steps=UpperCamelCase__       )

	if args.do_train:
		__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			0, 0, None
		model.train()
		for _ in trange(int(args.num_train_epochs       )    ,					desc='''Epoch'''       ):
			__UpperCAmelCase							=			0
			__UpperCAmelCase							=			0
			__UpperCAmelCase							=			tqdm(UpperCamelCase__    ,					desc='''Training'''       )
			for step, batch in enumerate(UpperCamelCase__       ):
				__UpperCAmelCase							=			tuple(t.to(UpperCamelCase__       ) for t in batch       )
				__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			batch
				__UpperCAmelCase							=			model(UpperCamelCase__    ,					mc_token_ids=UpperCamelCase__    ,					lm_labels=UpperCamelCase__    ,					mc_labels=UpperCamelCase__       )
				__UpperCAmelCase							=			args.lm_coef * losses[0] + losses[1]
				loss.backward()
				optimizer.step()
				scheduler.step()
				optimizer.zero_grad()
				tr_loss += loss.item()
				__UpperCAmelCase							=			(
				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
				)
				nb_tr_steps += 1
				__UpperCAmelCase							=			'''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
		__UpperCAmelCase							=			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`
		__UpperCAmelCase							=			os.path.join(args.output_dir    ,					UpperCamelCase__       )
		__UpperCAmelCase							=			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
		__UpperCAmelCase							=			OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir       )
		__UpperCAmelCase							=			OpenAIGPTTokenizer.from_pretrained(args.output_dir       )
		model.to(UpperCamelCase__       )

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

			__UpperCAmelCase							=			mc_logits.detach().cpu().numpy()
			__UpperCAmelCase							=			mc_labels.to('''cpu'''       ).numpy()
			__UpperCAmelCase							=			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

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

		__UpperCAmelCase							=			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()

'''simple docstring'''


import requests
from bsa import BeautifulSoup





def       lowerCAmelCase					(		UpperCamelCase__				:   str = "AAPL"       ):


	"""simple docstring"""
	__UpperCAmelCase							=			f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
	__UpperCAmelCase							=			BeautifulSoup(requests.get(UpperCamelCase__       ).text    ,					'''html.parser'''       )
	__UpperCAmelCase							=			'''My(6px) Pos(r) smartphone_Mt(6px)'''
	return soup.find('''div'''    ,					class_=class_       ).find('''span'''       ).text


if __name__ == "__main__":
					for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
										print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
