




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 the checkpointing capability,
# 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_      :							str    							=					16
lowercase_      :							Union[str, Any]    							=					32


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_ = 16		):

						'''simple docstring'''


						_UpperCAmelCase						=	AutoTokenizer.from_pretrained("bert-base-cased"		)
						_UpperCAmelCase						=	load_dataset("glue"      ,       "mrpc"		)

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

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

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

						# Instantiate dataloaders.
						_UpperCAmelCase						=	DataLoader(
						    tokenized_datasets["train"]      ,       shuffle=snake_case_      ,       collate_fn=snake_case_      ,       batch_size=snake_case_		)
						_UpperCAmelCase						=	DataLoader(
						    tokenized_datasets["validation"]      ,       shuffle=snake_case_      ,       collate_fn=snake_case_      ,       batch_size=snake_case_		)

						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_      :							str    							=					mocked_dataloaders  # noqa: F811


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						if os.environ.get("TESTING_MOCKED_DATALOADERS"      ,       snake_case_		) == "1":
												_UpperCAmelCase						=	2
						# Initialize accelerator
						_UpperCAmelCase						=	Accelerator(cpu=args.cpu      ,       mixed_precision=args.mixed_precision		)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_UpperCAmelCase						=	config['lr']
						_UpperCAmelCase						=	int(config["num_epochs"]		)
						_UpperCAmelCase						=	int(config["seed"]		)
						_UpperCAmelCase						=	int(config["batch_size"]		)

						# New Code #
						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps      ,       "isdigit"		):
												if args.checkpointing_steps == "epoch":
																		_UpperCAmelCase						=	args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		_UpperCAmelCase						=	int(args.checkpointing_steps		)
												else:
																		raise ValueError(
																		    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""		)
						else:
												_UpperCAmelCase						=	None

						set_seed(snake_case_		)

						_UpperCAmelCase						=	get_dataloaders(snake_case_      ,       snake_case_		)
						_UpperCAmelCase						=	evaluate.load("glue"      ,       "mrpc"		)

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

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

						# Instantiate optimizer
						_UpperCAmelCase						=	AdamW(params=model.parameters()      ,       lr=snake_case_		)

						# Instantiate scheduler
						_UpperCAmelCase						=	get_linear_schedule_with_warmup(
						    optimizer=snake_case_      ,       num_warmup_steps=100      ,       num_training_steps=(len(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.
						_UpperCAmelCase						=	accelerator.prepare(
						    snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		)

						# New Code #
						# We need to keep track of how many total steps we have iterated over
						_UpperCAmelCase						=	0
						# We also need to keep track of the stating epoch so files are named properly
						_UpperCAmelCase						=	0

						# We need to load the checkpoint back in before training here with `load_state`
						# The total number of epochs is adjusted based on where the state is being loaded from,
						# as we assume continuation of the same training script
						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		)
																		_UpperCAmelCase						=	os.path.basename(args.resume_from_checkpoint		)
												else:
																		# Get the most recent checkpoint
																		_UpperCAmelCase						=	[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
																		dirs.sort(key=os.path.getctime		)
																		_UpperCAmelCase						=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												_UpperCAmelCase						=	os.path.splitext(snake_case_		)[0]

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

    # Now we train the model
						for epoch in range(snake_case_      ,       snake_case_		):
												model.train()
												# New Code #
												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
																		_UpperCAmelCase						=	accelerator.skip_first_batches(snake_case_      ,       snake_case_		)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		_UpperCAmelCase						=	train_dataloader
												for step, batch in enumerate(snake_case_		):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device		)
																		_UpperCAmelCase						=	model(**snake_case_		)
																		_UpperCAmelCase						=	outputs.loss
																		_UpperCAmelCase						=	loss / gradient_accumulation_steps
																		accelerator.backward(snake_case_		)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()
																		# New Code #
																		overall_step += 1

																		# New Code #
																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																		# These are saved to folders named `step_{overall_step}`
																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																		# If mixed precision was used, will also save a "scalar.bin" file
																		if isinstance(snake_case_      ,       snake_case_		):
																								_UpperCAmelCase						=	f"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				_UpperCAmelCase						=	os.path.join(args.output_dir      ,       snake_case_		)
																														accelerator.save_state(snake_case_		)

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

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

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `epoch_{epoch}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if checkpointing_steps == "epoch":
																		_UpperCAmelCase						=	f"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								_UpperCAmelCase						=	os.path.join(args.output_dir      ,       snake_case_		)
																		accelerator.save_state(snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						_UpperCAmelCase						=	argparse.ArgumentParser(description="Simple example of training script."		)
						parser.add_argument(
						    "--mixed_precision"      ,       type=snake_case_      ,       default=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=snake_case_      ,       default=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(
						    "--output_dir"      ,       type=snake_case_      ,       default="."      ,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."      ,       )
						parser.add_argument(
						    "--resume_from_checkpoint"      ,       type=snake_case_      ,       default=snake_case_      ,       help="If the training should continue from a checkpoint folder."      ,       )
						_UpperCAmelCase						=	parser.parse_args()
						_UpperCAmelCase						=	{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(snake_case_      ,       snake_case_		)


if __name__ == "__main__":
				main()







'''simple docstring'''


def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     int:
	if not isinstance(UpperCamelCase ,UpperCamelCase	):
		raise TypeError('recursive_lucas_number accepts only integer arguments.'	)
	if n_th_number == 0:
		return 2
	if n_th_number == 1:
		return 1

	return recursive_lucas_number(n_th_number - 1	) + recursive_lucas_number(
	    n_th_number - 2	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     int:
	if not isinstance(UpperCamelCase ,UpperCamelCase	):
		raise TypeError('dynamic_lucas_number accepts only integer arguments.'	)
	UpperCAmelCase_						,     UpperCAmelCase_		:   List[str]													=       2, 1
	for _ in range(UpperCamelCase	):
		UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       b, a + b
	return a


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						lowerCAmelCase__      				=			int(input("Enter the number of terms in lucas series:\n").strip())
						print("Using recursive function to calculate lucas series:")
						print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
						print("\nUsing dynamic function to calculate lucas series:")
						print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))






