
'''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
#
########################################################################


lowercase    :							Dict      =					16
lowercase    :							Dict      =					32







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A = 16	)       ->	str:
	_snake_case   						=		AutoTokenizer.from_pretrained('bert-base-cased'	)
	_snake_case   						=		load_dataset('glue'			,				'mrpc'	)

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

	def collate_fn(__A	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		_snake_case   						=		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   						=		16
		elif accelerator.mixed_precision != "no":
			_snake_case   						=		8
		else:
			_snake_case   						=		None

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

	# Instantiate dataloaders.
	_snake_case   						=		DataLoader(
	    tokenized_datasets['train']			,				shuffle=__A			,				collate_fn=__A			,				batch_size=__A	)
	_snake_case   						=		DataLoader(
	    tokenized_datasets['validation']			,				shuffle=__A			,				collate_fn=__A			,				batch_size=__A	)

	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

					lowercase    :							Any      =					mocked_dataloaders  # noqa: F811







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Any:
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'			,				__A	) == "1":
		_snake_case   						=		2
	# Initialize accelerator
	_snake_case   						=		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   						=		config['lr']
	_snake_case   						=		int(config['num_epochs']	)
	_snake_case   						=		int(config['seed']	)
	_snake_case   						=		int(config['batch_size']	)

	_snake_case   						=		evaluate.load('glue'			,				'mrpc'	)

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

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

	# 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   						=		model.to(accelerator.device	)

	# Instantiate optimizer
	_snake_case   						=		AdamW(params=model.parameters()			,				lr=__A	)

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

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

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

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







def 				SCREAMING_SNAKE_CASE__	(   )       ->	Optional[int]:
	_snake_case   						=		argparse.ArgumentParser(description='Simple example of training script.'	)
	parser.add_argument(
	    '--mixed_precision'			,				type=__A			,				default=__A			,				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   						=		parser.parse_args()
	_snake_case   						=		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(__A			,				__A	)


if __name__ == "__main__":
					main()



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase    :							Tuple      =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							int      =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Tuple      =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

					lowercase    :							Union[str, Any]      =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

