

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


lowerCAmelCase__						=	1_6
lowerCAmelCase__						=	3_2



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							List[Any] )     -> List[Any]:




				'''simple docstring'''





				if args.with_tracking:
								A__            =     Accelerator(
								    cpu=args.cpu		,    mixed_precision=args.mixed_precision		,    log_with="all"		,    project_dir=args.project_dir )
				else:
								A__            =     Accelerator(cpu=args.cpu		,    mixed_precision=args.mixed_precision )

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

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

				A__            =     AutoTokenizer.from_pretrained("bert-base-cased" )
				A__            =     load_dataset("glue"		,    "mrpc" )
				A__            =     evaluate.load("glue"		,    "mrpc" )

				def tokenize_function(SCREAMING_SNAKE_CASE_:							Union[str, Any] ):
								# max_length=None => use the model max length (it's actually the default)
								A__            =     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():
								A__            =     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
				A__            =     tokenized_datasets.rename_column("label"		,    "labels" )

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

				def collate_fn(SCREAMING_SNAKE_CASE_:							Optional[int] ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__            =     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												A__            =     1_6
								elif accelerator.mixed_precision != "no":
												A__            =     8
								else:
												A__            =     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.
				A__            =     DataLoader(
				    tokenized_datasets["train"]		,    shuffle=SCREAMING_SNAKE_CASE_		,    collate_fn=SCREAMING_SNAKE_CASE_		,    batch_size=SCREAMING_SNAKE_CASE_ )
				A__            =     DataLoader(
				    tokenized_datasets["validation"]		,    shuffle=SCREAMING_SNAKE_CASE_		,    collate_fn=SCREAMING_SNAKE_CASE_		,    batch_size=SCREAMING_SNAKE_CASE_ )

				set_seed(SCREAMING_SNAKE_CASE_ )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A__            =     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).
				A__            =     model.to(accelerator.device )

				# Instantiate optimizer
				A__            =     AdamW(params=model.parameters()		,    lr=SCREAMING_SNAKE_CASE_ )

				# Instantiate scheduler
				A__            =     get_linear_schedule_with_warmup(
				    optimizer=SCREAMING_SNAKE_CASE_		,    num_warmup_steps=1_0_0		,    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.
				A__      ,			A__      ,			A__      ,			A__      ,			A__            =     accelerator.prepare(
				    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ )

				# We need to keep track of how many total steps we have iterated over
				A__            =     0
				# We also need to keep track of the stating epoch so files are named properly
				A__            =     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 )
												A__            =     os.path.basename(args.resume_from_checkpoint )
								else:
												# Get the most recent checkpoint
												A__            =     [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
												dirs.sort(key=os.path.getctime )
												A__            =     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								A__            =     os.path.splitext(SCREAMING_SNAKE_CASE_ )[0]

								if "epoch" in training_difference:
												A__            =     int(training_difference.replace("epoch_"		,    "" ) ) + 1
												A__            =     None
								else:
												A__            =     int(training_difference.replace("step_"		,    "" ) )
												A__            =     resume_step // len(SCREAMING_SNAKE_CASE_ )
												resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE_ )

    # Now we train the model
				for epoch in range(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ ):
								model.train()
								if args.with_tracking:
												A__            =     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
												A__            =     accelerator.skip_first_batches(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ )
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												A__            =     train_dataloader
								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 )
												A__            =     model(**SCREAMING_SNAKE_CASE_ )
												A__            =     outputs.loss
												A__            =     loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
																total_loss += loss.detach().float()
												accelerator.backward(SCREAMING_SNAKE_CASE_ )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

												if isinstance(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ ):
																A__            =     F'step_{overall_step}'
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								A__            =     os.path.join(args.output_dir		,    SCREAMING_SNAKE_CASE_ )
																				accelerator.save_state(SCREAMING_SNAKE_CASE_ )

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

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

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

				if args.with_tracking:
								accelerator.end_training()



def    lowerCAmelCase__							(		)     -> List[str]:




				'''simple docstring'''





				A__            =     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." )
				parser.add_argument(
				    "--checkpointing_steps"		,    type=SCREAMING_SNAKE_CASE_		,    default=SCREAMING_SNAKE_CASE_		,    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=SCREAMING_SNAKE_CASE_		,    default=SCREAMING_SNAKE_CASE_		,    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=SCREAMING_SNAKE_CASE_		,    default="."		,    help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		,    )
				parser.add_argument(
				    "--project_dir"		,    type=SCREAMING_SNAKE_CASE_		,    default="logs"		,    help="Location on where to store experiment tracking logs` and relevent project information"		,    )
				A__            =     parser.parse_args()
				A__            =     {"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
				training_function(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
	main()




def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							list		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							int )     -> int:




				'''simple docstring'''





				A__            =     int((lower_bound + upper_bound) // 2 )
				if array[r] == value:
								return r
				if lower_bound >= upper_bound:
								return -1
				if array[r] < value:
								return binary_search(SCREAMING_SNAKE_CASE_		,    r + 1		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ )
				else:
								return binary_search(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    r - 1		,    SCREAMING_SNAKE_CASE_ )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							list )     -> list:




				'''simple docstring'''





				A__            =     0
				if matrix[index][0] == value:
								return [index, 0]
				while index < len(SCREAMING_SNAKE_CASE_ ) and matrix[index][0] < value:
								A__            =     binary_search(matrix[index]		,    0		,    len(matrix[index] ) - 1		,    SCREAMING_SNAKE_CASE_ )
								if r != -1:
												return [index, r]
								index += 1
				return [-1, -1]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

