
import requests
from bsa import BeautifulSoup

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str] = "AAPL"       ) ->       Optional[Any]:

    """simple docstring"""







    A__  :	List[str]         =							F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
    A__  :	Union[str, Any]         =							BeautifulSoup(requests.get(__UpperCamelCase       ).text							,						'''html.parser'''       )
    A__  :	str         =							"""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}""")

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_SCREAMING_SNAKE_CASE    :      str          =			1_6
_SCREAMING_SNAKE_CASE    :      Tuple          =			3_2

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Accelerator							,						__UpperCamelCase :						int = 16       ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	List[str]         =							AutoTokenizer.from_pretrained('''bert-base-cased'''       )
				A__  :	Optional[int]         =							load_dataset('''glue'''							,						'''mrpc'''       )

				def tokenize_function(__UpperCamelCase :						Union[str, Any]       ):
								# max_length=None => use the model max length (it's actually the default)
								A__  :	int         =							tokenizer(examples['''sentence1''']							,						examples['''sentence2''']							,						truncation=__UpperCamelCase							,						max_length=__UpperCamelCase       )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								A__  :	Optional[int]         =							datasets.map(
								    __UpperCamelCase							,						batched=__UpperCamelCase							,						remove_columns=['''idx''', '''sentence1''', '''sentence2''']							,						)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				A__  :	List[Any]         =							tokenized_datasets.rename_column('''label'''							,						'''labels'''       )

				def collate_fn(__UpperCamelCase :						Any       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__  :	Optional[Any]         =							1_28 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												A__  :	int         =							16
								elif accelerator.mixed_precision != "no":
												A__  :	Any         =							8
								else:
												A__  :	Union[str, Any]         =							None

								return tokenizer.pad(
								    __UpperCamelCase							,						padding='''longest'''							,						max_length=__UpperCamelCase							,						pad_to_multiple_of=__UpperCamelCase							,						return_tensors='''pt'''							,						)

				# Instantiate dataloaders.
				A__  :	Optional[int]         =							DataLoader(
				    tokenized_datasets['''train''']							,						shuffle=__UpperCamelCase							,						collate_fn=__UpperCamelCase							,						batch_size=__UpperCamelCase       )
				A__  :	Tuple         =							DataLoader(
				    tokenized_datasets['''validation''']							,						shuffle=__UpperCamelCase							,						collate_fn=__UpperCamelCase							,						batch_size=__UpperCamelCase       )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					_SCREAMING_SNAKE_CASE    :      Dict          =			mocked_dataloaders  # noqa: F811

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						List[Any]       ) ->       Optional[Any]:

				"""simple docstring"""







				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,						__UpperCamelCase       ) == "1":
								A__  :	List[str]         =							2
				# Initialize accelerator
				A__  :	Optional[Any]         =							Accelerator(cpu=args.cpu							,						mixed_precision=args.mixed_precision       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				A__  :	Tuple         =							config['''lr''']
				A__  :	Dict         =							int(config['''num_epochs''']       )
				A__  :	int         =							int(config['''seed''']       )
				A__  :	Optional[Any]         =							int(config['''batch_size''']       )

				A__  :	int         =							evaluate.load('''glue'''							,						'''mrpc'''       )

				# If the batch size is too big we use gradient accumulation
				A__  :	Union[str, Any]         =							1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								A__  :	List[Any]         =							batch_size // MAX_GPU_BATCH_SIZE
								A__  :	Dict         =							MAX_GPU_BATCH_SIZE

				set_seed(__UpperCamelCase       )
				A__       ,							A__  :	int         =							get_dataloaders(__UpperCamelCase							,						__UpperCamelCase       )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A__  :	Optional[int]         =							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''							,						return_dict=__UpperCamelCase       )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				A__  :	Tuple         =							model.to(accelerator.device       )

				# Instantiate optimizer
				A__  :	Optional[int]         =							AdamW(params=model.parameters()							,						lr=__UpperCamelCase       )

				# Instantiate scheduler
				A__  :	Any         =							get_linear_schedule_with_warmup(
				    optimizer=__UpperCamelCase							,						num_warmup_steps=1_00							,						num_training_steps=(len(__UpperCamelCase       ) * num_epochs) // gradient_accumulation_steps							,						)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				A__       ,							A__       ,							A__       ,							A__       ,							A__  :	Dict         =							accelerator.prepare(
				    __UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

				# Now we train the model
				for epoch in range(__UpperCamelCase       ):
								model.train()
								for step, batch in enumerate(__UpperCamelCase       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												A__  :	Dict         =							model(**__UpperCamelCase       )
												A__  :	Dict         =							outputs.loss
												A__  :	List[str]         =							loss / gradient_accumulation_steps
												accelerator.backward(__UpperCamelCase       )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								A__  :	Optional[int]         =							0
								for step, batch in enumerate(__UpperCamelCase       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												with torch.no_grad():
																A__  :	Union[str, Any]         =							model(**__UpperCamelCase       )
												A__  :	int         =							outputs.logits.argmax(dim=-1       )
												A__       ,							A__  :	Optional[Any]         =							accelerator.gather((predictions, batch['''labels'''])       )
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(__UpperCamelCase       ) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				A__  :	Tuple         =							predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																				A__  :	int         =							references[: len(eval_dataloader.dataset       ) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=__UpperCamelCase							,						references=__UpperCamelCase							,						)

								A__  :	Union[str, Any]         =							metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"							,						__UpperCamelCase       )

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

				"""simple docstring"""







				A__  :	Tuple         =							argparse.ArgumentParser(description='''Simple example of training script.'''       )
				parser.add_argument(
				    '''--mixed_precision'''							,						type=__UpperCamelCase							,						default=__UpperCamelCase							,						choices=['''no''', '''fp16''', '''bf16''', '''fp8''']							,						help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''							,						)
				parser.add_argument('''--cpu'''							,						action='''store_true'''							,						help='''If passed, will train on the CPU.'''       )
				A__  :	Dict         =							parser.parse_args()
				A__  :	Any         =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__UpperCamelCase							,						__UpperCamelCase       )


if __name__ == "__main__":
					main()