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


__lowerCAmelCase												=	16
__lowerCAmelCase												=	32






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE = 16				):
							_snake_case       =  AutoTokenizer.from_pretrained("""bert-base-cased"""				)
							_snake_case       =  load_dataset("""glue"""							, """mrpc"""				)

							def tokenize_function(_SCREAMING_SNAKE_CASE				):
														# max_length=None => use the model max length (it's actually the default)
														_snake_case       =  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():
														_snake_case       =  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
							_snake_case       =  tokenized_datasets.rename_column("""label"""							, """labels"""				)

							def collate_fn(_SCREAMING_SNAKE_CASE				):
														# 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(
														    _SCREAMING_SNAKE_CASE							, padding="""longest"""							, max_length=_SCREAMING_SNAKE_CASE							, pad_to_multiple_of=_SCREAMING_SNAKE_CASE							, return_tensors="""pt"""							, )

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

							return train_dataloader, eval_dataloader






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							# 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(_SCREAMING_SNAKE_CASE				)
							_snake_case,							_snake_case       =  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)
							_snake_case       =  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).
							_snake_case       =  model.to(accelerator.device				)
							# Instantiate optimizer
							_snake_case       =  AdamW(params=model.parameters()							, lr=_SCREAMING_SNAKE_CASE				)

							# Instantiate scheduler
							_snake_case       =  get_linear_schedule_with_warmup(
							    optimizer=_SCREAMING_SNAKE_CASE							, num_warmup_steps=100							, 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.

							_snake_case,							_snake_case,							_snake_case,							_snake_case,							_snake_case       =  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				)
																					_snake_case       =  model(**_SCREAMING_SNAKE_CASE				)
																					_snake_case       =  outputs.loss
																					_snake_case       =  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()
														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():
																												_snake_case       =  model(**_SCREAMING_SNAKE_CASE				)
																					_snake_case       =  outputs.logits.argmax(dim=-1				)
																					_snake_case,							_snake_case       =  accelerator.gather_for_metrics((predictions, batch["""labels"""])				)
																					metric.add_batch(
																					    predictions=_SCREAMING_SNAKE_CASE							, references=_SCREAMING_SNAKE_CASE							, )

														_snake_case       =  metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""							, _SCREAMING_SNAKE_CASE				)






def  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  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."""				)
							_snake_case       =  parser.parse_args()
							_snake_case       =  {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				main()

'''simple docstring'''
from typing import TYPE_CHECKING

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


__lowerCAmelCase												=	{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'EncodecModel',
				    'EncodecPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)