


"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase      :      List[str]   					=					{
    'configuration_prophetnet': ['PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ProphetNetConfig'],
    'tokenization_prophetnet': ['ProphetNetTokenizer'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      str   					=					[
				    'PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ProphetNetDecoder',
				    'ProphetNetEncoder',
				    'ProphetNetForCausalLM',
				    'ProphetNetForConditionalGeneration',
				    'ProphetNetModel',
				    'ProphetNetPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
				from .tokenization_prophetnet import ProphetNetTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_prophetnet import (
								    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ProphetNetDecoder,
								    ProphetNetEncoder,
								    ProphetNetForCausalLM,
								    ProphetNetForConditionalGeneration,
								    ProphetNetModel,
								    ProphetNetPreTrainedModel,
								)

else:
				import sys

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






"""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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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      :      Dict   					=					16
__UpperCAmelCase      :      List[Any]   					=					32
def      A      (			_A,     _A							):




					"""simple docstring"""
					# Initialize accelerator
					if args.with_tracking:
										snake_case_						:Union[str, Any]								=						Accelerator(
										    cpu=args.cpu,     mixed_precision=args.mixed_precision,     log_with="all",     project_dir=args.project_dir							)
					else:
										snake_case_						:Tuple								=						Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision							)

					if hasattr(args.checkpointing_steps,     "isdigit"							):
										if args.checkpointing_steps == "epoch":
															snake_case_						:Tuple								=						args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															snake_case_						:int								=						int(args.checkpointing_steps							)
										else:
															raise ValueError(
															    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
					else:
										snake_case_						:Tuple								=						None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					snake_case_						:Union[str, Any]								=						config["lr"]
					snake_case_						:Any								=						int(config["num_epochs"]							)
					snake_case_						:Optional[int]								=						int(config["seed"]							)
					snake_case_						:List[Any]								=						int(config["batch_size"]							)

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										snake_case_						:Union[str, Any]								=						os.path.split(_A							)[-1].split("."							)[0]
										accelerator.init_trackers(_A,     _A							)

					snake_case_						:Any								=						AutoTokenizer.from_pretrained("bert-base-cased"							)
					snake_case_						:Any								=						load_dataset("glue",     "mrpc"							)
					snake_case_						:Optional[Any]								=						evaluate.load("glue",     "mrpc"							)

					def tokenize_function(_A							):
										# max_length=None => use the model max length (it's actually the default)
										snake_case_						:Any								=						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_						:int								=						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_						:Any								=						tokenized_datasets.rename_column("label",     "labels"							)

					# If the batch size is too big we use gradient accumulation
					snake_case_						:List[Any]								=						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_						:List[str]								=						MAX_GPU_BATCH_SIZE

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

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

					# Instantiate dataloaders.
					snake_case_						:str								=						DataLoader(
					    tokenized_datasets["train"],     shuffle=_A,     collate_fn=_A,     batch_size=_A							)
					snake_case_						:Any								=						DataLoader(
					    tokenized_datasets["validation"],     shuffle=_A,     collate_fn=_A,     batch_size=_A							)

					set_seed(_A							)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					snake_case_						:Optional[Any]								=						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_						:str								=						model.to(accelerator.device							)

					# Instantiate optimizer
					snake_case_						:List[Any]								=						AdamW(params=model.parameters(),     lr=_A							)

					# Instantiate scheduler
					snake_case_						:Tuple								=						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_						:Optional[Any]								=						accelerator.prepare(
					    _A,     _A,     _A,     _A,     _A							)

					# We need to keep track of how many total steps we have iterated over
					snake_case_						:Optional[int]								=						0
					# We also need to keep track of the stating epoch so files are named properly
					snake_case_						:int								=						0

					# Potentially load in the weights and states from a previous save
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''							)
															accelerator.load_state(args.resume_from_checkpoint							)
															snake_case_						:str								=						os.path.basename(args.resume_from_checkpoint							)
										else:
															# Get the most recent checkpoint
															snake_case_						:int								=						[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
															dirs.sort(key=os.path.getctime							)
															snake_case_						:Tuple								=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										snake_case_						:Optional[int]								=						os.path.splitext(_A							)[0]

										if "epoch" in training_difference:
															snake_case_						:Union[str, Any]								=						int(training_difference.replace("epoch_",     ""							)							) + 1
															snake_case_						:List[str]								=						None
										else:
															snake_case_						:Tuple								=						int(training_difference.replace("step_",     ""							)							)
															snake_case_						:Optional[int]								=						resume_step // len(_A							)
															resume_step -= starting_epoch * len(_A							)

    # Now we train the model
					for epoch in range(_A,     _A							):
										model.train()
										if args.with_tracking:
															snake_case_						:Optional[Any]								=						0
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															snake_case_						:Dict								=						accelerator.skip_first_batches(_A,     _A							)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															snake_case_						:Optional[Any]								=						train_dataloader
										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_						:Union[str, Any]								=						model(**_A							)
															snake_case_						:str								=						outputs.loss
															snake_case_						:int								=						loss / gradient_accumulation_steps
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(_A							)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

															if isinstance(_A,     _A							):
																				snake_case_						:List[str]								=						F'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														snake_case_						:Tuple								=						os.path.join(args.output_dir,     _A							)
																									accelerator.save_state(_A							)

										model.eval()
										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_						:str								=						model(**_A							)
															snake_case_						:str								=						outputs.logits.argmax(dim=-1							)
															snake_case_	,  snake_case_						:List[str]								=						accelerator.gather_for_metrics((predictions, batch["labels"])							)
															metric.add_batch(
															    predictions=_A,     references=_A,     )

										snake_case_						:int								=						metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F'''epoch {epoch}:''',     _A							)
										if args.with_tracking:
															accelerator.log(
															    {
															        "accuracy": eval_metric["accuracy"],
															        "f1": eval_metric["f1"],
															        "train_loss": total_loss.item() / len(_A							),
															        "epoch": epoch,
															    },     step=_A,     )

										if checkpointing_steps == "epoch":
															snake_case_						:str								=						F'''epoch_{epoch}'''
															if args.output_dir is not None:
																				snake_case_						:Any								=						os.path.join(args.output_dir,     _A							)
															accelerator.save_state(_A							)

					if args.with_tracking:
										accelerator.end_training()
def      A      (			):




					"""simple docstring"""
					snake_case_						:str								=						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."							)
					parser.add_argument(
					    "--checkpointing_steps",     type=_A,     default=_A,     help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",     )
					parser.add_argument(
					    "--resume_from_checkpoint",     type=_A,     default=_A,     help="If the training should continue from a checkpoint folder.",     )
					parser.add_argument(
					    "--with_tracking",     action="store_true",     help="Whether to load in all available experiment trackers from the environment and use them for logging.",     )
					parser.add_argument(
					    "--output_dir",     type=_A,     default=".",     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",     )
					parser.add_argument(
					    "--project_dir",     type=_A,     default="logs",     help="Location on where to store experiment tracking logs` and relevent project information",     )
					snake_case_						:Optional[int]								=						parser.parse_args()
					snake_case_						:Optional[Any]								=						{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(_A,     _A							)


if __name__ == "__main__":
				main()

