






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
#
########################################################################


__UpperCamelCase      :      Optional[Any]     						=						16
__UpperCamelCase      :      int     						=						32





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase = 16					):




	'''simple docstring'''







	__lowercase             =  AutoTokenizer.from_pretrained("""bert-base-cased"""					)
	__lowercase             =  load_dataset("""glue"""				,  """mrpc"""					)

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

	def collate_fn(lowerCamelCase					):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__lowercase             =  128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			__lowercase             =  16
		elif accelerator.mixed_precision != "no":
			__lowercase             =  8
		else:
			__lowercase             =  None

		return tokenizer.pad(
		    lowerCamelCase				,  padding="""longest"""				,  max_length=lowerCamelCase				,  pad_to_multiple_of=lowerCamelCase				,  return_tensors="""pt"""				,  )

	# Instantiate dataloaders.
	__lowercase             =  DataLoader(
	    tokenized_datasets["""train"""]				,  shuffle=lowerCamelCase				,  collate_fn=lowerCamelCase				,  batch_size=lowerCamelCase					)
	__lowercase             =  DataLoader(
	    tokenized_datasets["""validation"""]				,  shuffle=lowerCamelCase				,  collate_fn=lowerCamelCase				,  batch_size=lowerCamelCase					)

	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

						__UpperCamelCase      :      List[str]     						=						mocked_dataloaders  # noqa: F811





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,  lowerCamelCase					) == "1":
		__lowercase             =  2
	# Initialize accelerator
	__lowercase             =  Accelerator(cpu=args.cpu				,  mixed_precision=args.mixed_precision					)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__lowercase             =  config["""lr"""]
	__lowercase             =  int(config["""num_epochs"""]					)
	__lowercase             =  int(config["""seed"""]					)
	__lowercase             =  int(config["""batch_size"""]					)

	__lowercase             =  evaluate.load("""glue"""				,  """mrpc"""					)

	# If the batch size is too big we use gradient accumulation
	__lowercase             =  1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		__lowercase             =  batch_size // MAX_GPU_BATCH_SIZE
		__lowercase             =  MAX_GPU_BATCH_SIZE

	set_seed(lowerCamelCase					)
	__lowercase       ,						__lowercase             =  get_dataloaders(lowerCamelCase				,  lowerCamelCase					)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__lowercase             =  AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,  return_dict=lowerCamelCase					)

	# 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).
	__lowercase             =  model.to(accelerator.device					)

	# Instantiate optimizer
	__lowercase             =  AdamW(params=model.parameters()				,  lr=lowerCamelCase					)

	# Instantiate scheduler
	__lowercase             =  get_linear_schedule_with_warmup(
	    optimizer=lowerCamelCase				,  num_warmup_steps=100				,  num_training_steps=(len(lowerCamelCase					) * 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.
	__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  accelerator.prepare(
	    lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)

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

		model.eval()
		__lowercase             =  0
		for step, batch in enumerate(lowerCamelCase					):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device					)
			with torch.no_grad():
				__lowercase             =  model(**lowerCamelCase					)
			__lowercase             =  outputs.logits.argmax(dim=-1					)
			__lowercase       ,						__lowercase             =  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(lowerCamelCase					) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					__lowercase             =  predictions[: len(eval_dataloader.dataset					) - samples_seen]
					__lowercase             =  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=lowerCamelCase				,  references=lowerCamelCase				,  )

		__lowercase             =  metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'epoch {epoch}:'				,  lowerCamelCase					)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  argparse.ArgumentParser(description="""Simple example of training script."""					)
	parser.add_argument(
	    """--mixed_precision"""				,  type=lowerCamelCase				,  default=lowerCamelCase				,  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."""					)
	__lowercase             =  parser.parse_args()
	__lowercase             =  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(lowerCamelCase				,  lowerCamelCase					)


if __name__ == "__main__":
						main()









from __future__ import annotations





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if len(lowerCamelCase					) < 2:
		raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""					)
	if any(i <= 0 for i in nums					):
		raise ValueError("""All values must be greater than 0"""					)
	__lowercase             =  nums.copy()
	copy_nums.sort()
	return copy_nums[-1] < sum(copy_nums[:-1]					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

