






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      :    Any   		=   16
__lowerCamelCase      :    str   		=   32







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							List[str] = 16     ):
	snake_case__				:   int     =						AutoTokenizer.from_pretrained("bert-base-cased"     )
	snake_case__				:   Optional[int]     =						load_dataset("glue"  ,						"mrpc"     )

	def tokenize_function(snake_case_      :							List[str]     ):
		# max_length=None => use the model max length (it's actually the default)
		snake_case__				:   Optional[Any]     =						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():
		snake_case__				:   Any     =						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
	snake_case__				:   Dict     =						tokenized_datasets.rename_column("label"  ,						"labels"     )

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

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

	# Instantiate dataloaders.
	snake_case__				:   Union[str, Any]     =						DataLoader(
	    tokenized_datasets["train"]  ,						shuffle=_lowerCAmelCase  ,						collate_fn=_lowerCAmelCase  ,						batch_size=_lowerCAmelCase     )
	snake_case__				:   Union[str, Any]     =						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

							__lowerCamelCase      :    str   		=   mocked_dataloaders  # noqa: F811







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int  ,						snake_case_      :							Optional[Any]     ):
	# For testing only
	if os.environ.get("TESTING_MOCKED_DATALOADERS"  ,						_lowerCAmelCase     ) == "1":
		snake_case__				:   List[Any]     =						2
	# Initialize accelerator
	snake_case__				:   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
	snake_case__				:   Tuple     =						config["lr"]
	snake_case__				:   Optional[int]     =						int(config["num_epochs"]     )
	snake_case__				:   Any     =						int(config["seed"]     )
	snake_case__				:   Dict     =						int(config["batch_size"]     )

	snake_case__				:   Tuple     =						evaluate.load("glue"  ,						"mrpc"     )

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

	set_seed(_lowerCAmelCase     )
	snake_case__				:   Optional[Any]     =						get_dataloaders(_lowerCAmelCase  ,						_lowerCAmelCase     )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	snake_case__				:   Optional[int]     =						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).
	snake_case__				:   Tuple     =						model.to(accelerator.device     )

	# Instantiate optimizer
	snake_case__				:   List[str]     =						AdamW(params=model.parameters()  ,						lr=_lowerCAmelCase     )

	# Instantiate scheduler
	snake_case__				:   Any     =						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.
	snake_case__				:   Optional[Any]     =						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     )
			snake_case__				:   Any     =						model(**_lowerCAmelCase     )
			snake_case__				:   Optional[Any]     =						outputs.loss
			snake_case__				:   List[Any]     =						loss / gradient_accumulation_steps
			accelerator.backward(_lowerCAmelCase     )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		snake_case__				:   List[str]     =						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():
				snake_case__				:   str     =						model(**_lowerCAmelCase     )
			snake_case__				:   List[str]     =						outputs.logits.argmax(dim=-1     )
			snake_case__				:   Union[str, 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(_lowerCAmelCase     ) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					snake_case__				:   Union[str, Any]     =						predictions[: len(eval_dataloader.dataset     ) - samples_seen]
					snake_case__				:   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=_lowerCAmelCase  ,						references=_lowerCAmelCase  ,						)

		snake_case__				:   List[str]     =						metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''  ,						_lowerCAmelCase     )







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Any     =						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."     )
	snake_case__				:   Dict     =						parser.parse_args()
	snake_case__				:   Any     =						{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(_lowerCAmelCase  ,						_lowerCAmelCase     )


if __name__ == "__main__":
							main()











import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate



@dataclass(frozen=UpperCamelCase_		)
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	field(default="text-classification"				,     metadata={"include_in_asdict_even_if_is_default": True}		)
					a_			=	Features({"text": Value("string"		)}		)
					a_			=	Features({"labels": ClassLabel}		)
					a_			=	"text"
					a_			=	"labels"



					def  _lowercase  (      self  :						Tuple							,	__A  :						List[Any] ):
						if self.label_column not in features:
							raise ValueError(f'''Column {self.label_column} is not present in features.''' )
						if not isinstance(features[self.label_column]							,	__A ):
							raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
						snake_case__				:   Any     =						copy.deepcopy(self )
						snake_case__				:   Optional[Any]     =						self.label_schema.copy()
						snake_case__				:   List[str]     =						features[self.label_column]
						snake_case__				:   Dict     =						label_schema
						return task_template






					@property
					def  _lowercase  (      self  :						Tuple ):
						return {
						    self.text_column: "text",
						    self.label_column: "labels",
						}



