

'''simple docstring'''
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
#
########################################################################


_lowerCamelCase      					=     16
_lowerCamelCase      					=     32


def  a__       (    _SCREAMING_SNAKE_CASE   :       Accelerator	,				_SCREAMING_SNAKE_CASE   :       int = 16				)     ->					int:


	"""simple docstring"""





	UpperCAmelCase_     :      Union[str, Any]						=  AutoTokenizer.from_pretrained("bert-base-cased"				)
	UpperCAmelCase_     :      List[Any]						=  load_dataset("glue"	,				"mrpc"				)

	def tokenize_function(_SCREAMING_SNAKE_CASE   :       Optional[int]				):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_     :      Union[str, Any]						=  tokenizer(examples["sentence1"]	,				examples["sentence2"]	,				truncation=_SCREAMING_SNAKE_CASE	,				max_length=_SCREAMING_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():
		UpperCAmelCase_     :      Optional[Any]						=  datasets.map(
		    _SCREAMING_SNAKE_CASE	,				batched=_SCREAMING_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
	UpperCAmelCase_     :      int						=  tokenized_datasets.rename_column("label"	,				"labels"				)

	def collate_fn(_SCREAMING_SNAKE_CASE   :       Optional[int]				):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		UpperCAmelCase_     :      int						=  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":
			UpperCAmelCase_     :      List[Any]						=  16
		elif accelerator.mixed_precision != "no":
			UpperCAmelCase_     :      Optional[int]						=  8
		else:
			UpperCAmelCase_     :      str						=  None

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

	# Instantiate dataloaders.
	UpperCAmelCase_     :      Optional[Any]						=  DataLoader(
	    tokenized_datasets["train"]	,				shuffle=_SCREAMING_SNAKE_CASE	,				collate_fn=_SCREAMING_SNAKE_CASE	,				batch_size=_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Any						=  DataLoader(
	    tokenized_datasets["validation"]	,				shuffle=_SCREAMING_SNAKE_CASE	,				collate_fn=_SCREAMING_SNAKE_CASE	,				batch_size=_SCREAMING_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

					_lowerCamelCase      					=     mocked_dataloaders  # noqa: F811


def  a__       (    _SCREAMING_SNAKE_CASE   :       str	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]				)     ->					Dict:


	"""simple docstring"""





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

	UpperCAmelCase_     :      Union[str, Any]						=  evaluate.load("glue"	,				"mrpc"				)

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

	set_seed(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[int]						=  get_dataloaders(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCAmelCase_     :      Tuple						=  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"	,				return_dict=_SCREAMING_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).
	UpperCAmelCase_     :      Any						=  model.to(accelerator.device				)

	# Instantiate optimizer
	UpperCAmelCase_     :      Optional[Any]						=  AdamW(params=model.parameters()	,				lr=_SCREAMING_SNAKE_CASE				)

	# Instantiate scheduler
	UpperCAmelCase_     :      Union[str, Any]						=  get_linear_schedule_with_warmup(
	    optimizer=_SCREAMING_SNAKE_CASE	,				num_warmup_steps=1_00	,				num_training_steps=(len(_SCREAMING_SNAKE_CASE				) * 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.
	UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      str						=  accelerator.prepare(
	    _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

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

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

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


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


	"""simple docstring"""





	UpperCAmelCase_     :      Any						=  argparse.ArgumentParser(description="Simple example of training script."				)
	parser.add_argument(
	    "--mixed_precision"	,				type=_SCREAMING_SNAKE_CASE	,				default=_SCREAMING_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."	,				)
	parser.add_argument("--cpu"	,				action="store_true"	,				help="If passed, will train on the CPU."				)
	UpperCAmelCase_     :      str						=  parser.parse_args()
	UpperCAmelCase_     :      Union[str, Any]						=  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					main()





import random




def    lowerCAmelCase_  (		lowercase:				int	,       lowercase:				float	,       lowercase:				bool = False		)     ->					dict:







 '''simple docstring'''
 _UpperCamelCase:  dict    						=			{i: [] for i in range(lowercase		)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(lowercase		)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(lowercase		):
  for j in range(i + 1	,       lowercase		):
   if random.random() < probability:
    graph[i].append(lowercase		)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(lowercase		)
 return graph




def    lowerCAmelCase_  (		lowercase:				int		)     ->					dict:







 '''simple docstring'''
 return {
     i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
 }


if __name__ == "__main__":
  import doctest

  doctest.testmod()