






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
# and perform gradient accumulation
#
# 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 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__      :			Dict							=		16
SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		32

def      __lowercase					( snake_case,  snake_case = 1_6 ):







				"""simple docstring"""






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

				def tokenize_function(snake_case ):
								# max_length=None => use the model max length (it's actually the default)
								__magic_name__		:Dict        =      tokenizer(examples['''sentence1'''],  examples['''sentence2'''],  truncation=snake_case,  max_length=snake_case )
								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():
								__magic_name__		:Any        =      datasets.map(
								    snake_case,  batched=snake_case,  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
				__magic_name__		:Optional[Any]        =      tokenized_datasets.rename_column('''label''',  '''labels''' )

				def collate_fn(snake_case ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__magic_name__		:str        =      1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__magic_name__		:Optional[Any]        =      1_6
								elif accelerator.mixed_precision != "no":
												__magic_name__		:List[Any]        =      8
								else:
												__magic_name__		:Optional[int]        =      None

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

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

				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__      :			Optional[int]							=		mocked_dataloaders  # noqa: F811

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if os.environ.get('''TESTING_MOCKED_DATALOADERS''',  snake_case ) == "1":
								__magic_name__		:Any        =      2
				# New Code #
				__magic_name__		:int        =      int(args.gradient_accumulation_steps )
				# Initialize accelerator
				__magic_name__		:Tuple        =      Accelerator(
				    cpu=args.cpu,  mixed_precision=args.mixed_precision,  gradient_accumulation_steps=snake_case )
				if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
								raise NotImplementedError(
								    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`''' )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__magic_name__		:Union[str, Any]        =      config['''lr''']
				__magic_name__		:Tuple        =      int(config['''num_epochs'''] )
				__magic_name__		:Optional[int]        =      int(config['''seed'''] )
				__magic_name__		:Union[str, Any]        =      int(config['''batch_size'''] )

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

				set_seed(snake_case )
				__magic_name__     ,				__magic_name__		:List[Any]        =      get_dataloaders(snake_case,  snake_case )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__magic_name__		:List[str]        =      AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''',  return_dict=snake_case )

				# 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).
				__magic_name__		:str        =      model.to(accelerator.device )

				# Instantiate optimizer
				__magic_name__		:str        =      AdamW(params=model.parameters(),  lr=snake_case )

				# Instantiate scheduler
				__magic_name__		:List[Any]        =      get_linear_schedule_with_warmup(
				    optimizer=snake_case,  num_warmup_steps=1_0_0,  num_training_steps=(len(snake_case ) * num_epochs),  )

				# 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.
				__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__		:str        =      accelerator.prepare(
				    snake_case,  snake_case,  snake_case,  snake_case,  snake_case )

				# Now we train the model
				for epoch in range(snake_case ):
								model.train()
								for step, batch in enumerate(snake_case ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device )
												# New code #
												# We use the new `accumulate` context manager to perform gradient accumulation
												# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
												with accelerator.accumulate(snake_case ):
																__magic_name__		:str        =      model(**snake_case )
																__magic_name__		:Union[str, Any]        =      output.loss
																accelerator.backward(snake_case )
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(snake_case ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device )
												with torch.no_grad():
																__magic_name__		:Any        =      model(**snake_case )
												__magic_name__		:Union[str, Any]        =      outputs.logits.argmax(dim=-1 )
												__magic_name__     ,				__magic_name__		:Union[str, Any]        =      accelerator.gather_for_metrics((predictions, batch['''labels''']) )
												metric.add_batch(
												    predictions=snake_case,  references=snake_case,  )

								__magic_name__		:Dict        =      metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:''',  snake_case )

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      argparse.ArgumentParser(description='''Simple example of training script.''' )
				parser.add_argument(
				    '''--mixed_precision''',  type=snake_case,  default=snake_case,  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.''',  )
				# New Code #
				parser.add_argument(
				    '''--gradient_accumulation_steps''',  type=snake_case,  default=1,  help='''The number of minibatches to be ran before gradients are accumulated.''',  )
				parser.add_argument('''--cpu''',  action='''store_true''',  help='''If passed, will train on the CPU.''' )
				__magic_name__		:int        =      parser.parse_args()
				__magic_name__		:Optional[int]        =      {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
				training_function(snake_case,  snake_case )


if __name__ == "__main__":
					main()














SCREAMING_SNAKE_CASE__      :			str							=		"""Alexander Joslin"""

import operator as op

from .stack import Stack

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

				__magic_name__		:Stack[int]        =      Stack()
				__magic_name__		:Stack[str]        =      Stack()

				for i in equation:
								if i.isdigit():
												# RULE 1
												operand_stack.push(int(snake_case ) )
								elif i in operators:
												# RULE 2
												operator_stack.push(snake_case )
								elif i == ")":
												# RULE 4
												__magic_name__		:Optional[int]        =      operator_stack.peek()
												operator_stack.pop()
												__magic_name__		:List[str]        =      operand_stack.peek()
												operand_stack.pop()
												__magic_name__		:Optional[Any]        =      operand_stack.peek()
												operand_stack.pop()

												__magic_name__		:Optional[int]        =      operators[opr](snake_case,  snake_case )
												operand_stack.push(snake_case )

    # RULE 5
				return operand_stack.peek()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Any							=		"""(5 + ((4 * 2) * (2 + 3)))"""
					# answer = 45
					print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")






