





import argparse

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


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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase = 16				)						->  Tuple:
				lowerCamelCase	 =AutoTokenizer.from_pretrained("""bert-base-cased"""				)
				lowerCamelCase	 =load_dataset("""glue"""			,      """mrpc"""				)

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

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

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

				# Instantiate dataloaders.
				lowerCamelCase	 =DataLoader(
				    tokenized_datasets["""train"""]			,      shuffle=_UpperCAmelCase			,      collate_fn=_UpperCAmelCase			,      batch_size=_UpperCAmelCase			,      drop_last=_UpperCAmelCase				)
				lowerCamelCase	 =DataLoader(
				    tokenized_datasets["""validation"""]			,      shuffle=_UpperCAmelCase			,      collate_fn=_UpperCAmelCase			,      batch_size=_UpperCAmelCase			,      drop_last=(accelerator.mixed_precision == """fp8""")			,      )

				return train_dataloader, eval_dataloader





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				# Initialize accelerator
				lowerCamelCase	 =Accelerator(cpu=args.cpu			,      mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCamelCase	 =config["""lr"""]
				lowerCamelCase	 =int(config["""num_epochs"""]				)
				lowerCamelCase	 =int(config["""seed"""]				)
				lowerCamelCase	 =int(config["""batch_size"""]				)

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

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

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

				# 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).
				lowerCamelCase	 =model.to(accelerator.device				)
				# Instantiate optimizer
				lowerCamelCase	 =AdamW(params=model.parameters()			,      lr=_UpperCAmelCase				)

				# Instantiate scheduler
				lowerCamelCase	 =get_linear_schedule_with_warmup(
				    optimizer=_UpperCAmelCase			,      num_warmup_steps=1_00			,      num_training_steps=(len(_UpperCAmelCase				) * 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.

				lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =accelerator.prepare(
				    _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)

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

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

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





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


if __name__ == "__main__":
							main()












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__						:			str									={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			List[Any]									=['''FNetTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Any									=['''FNetTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Dict									=[
							    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FNetForMaskedLM''',
							    '''FNetForMultipleChoice''',
							    '''FNetForNextSentencePrediction''',
							    '''FNetForPreTraining''',
							    '''FNetForQuestionAnswering''',
							    '''FNetForSequenceClassification''',
							    '''FNetForTokenClassification''',
							    '''FNetLayer''',
							    '''FNetModel''',
							    '''FNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet import FNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet_fast import FNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_fnet import (
														    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FNetForMaskedLM,
														    FNetForMultipleChoice,
														    FNetForNextSentencePrediction,
														    FNetForPreTraining,
														    FNetForQuestionAnswering,
														    FNetForSequenceClassification,
														    FNetForTokenClassification,
														    FNetLayer,
														    FNetModel,
														    FNetPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__						:			Optional[int]									=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





