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

__a  :					Optional[int]          =  1_6
__a  :					Optional[int]          =  3_2





def     UpperCAmelCase						(   lowercase							,      lowercase = 16							):







				"""simple docstring"""



				__lowercase			      =							AutoTokenizer.from_pretrained('''bert-base-cased'''							)
				__lowercase			      =							load_dataset('''glue'''							,      '''mrpc'''							)

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

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

								return tokenizer.pad(
								    lowercase							,      padding='''longest'''							,      max_length=lowercase							,      pad_to_multiple_of=lowercase							,      return_tensors='''pt'''							,      )

				# Instantiate dataloaders.
				__lowercase			      =							DataLoader(
				    tokenized_datasets['''train''']							,      shuffle=lowercase							,      collate_fn=lowercase							,      batch_size=lowercase							)
				__lowercase			      =							DataLoader(
				    tokenized_datasets['''validation''']							,      shuffle=lowercase							,      collate_fn=lowercase							,      batch_size=lowercase							)

				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

		__a  :					Tuple          =  mocked_dataloaders  # noqa: F811





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,      lowercase							) == "1":
								__lowercase			      =							2
				# Initialize accelerator
				__lowercase			      =							Accelerator(cpu=args.cpu							,      mixed_precision=args.mixed_precision							)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowercase			      =							config['''lr''']
				__lowercase			      =							int(config['''num_epochs''']							)
				__lowercase			      =							int(config['''seed''']							)
				__lowercase			      =							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":
												__lowercase			      =							args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												__lowercase			      =							int(args.checkpointing_steps							)
								else:
												raise ValueError(
												    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."							)
				else:
								__lowercase			      =							None

				set_seed(lowercase							)

				__lowercase      ,					__lowercase			      =							get_dataloaders(lowercase							,      lowercase							)
				__lowercase			      =							evaluate.load('''glue'''							,      '''mrpc'''							)

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

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowercase			      =							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''							,      return_dict=lowercase							)

				# 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).
				__lowercase			      =							model.to(accelerator.device							)

				# Instantiate optimizer
				__lowercase			      =							AdamW(params=model.parameters()							,      lr=lowercase							)

				# Instantiate scheduler
				__lowercase			      =							get_linear_schedule_with_warmup(
				    optimizer=lowercase							,      num_warmup_steps=100							,      num_training_steps=(len(lowercase							) * 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.
				__lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase			      =							accelerator.prepare(
				    lowercase							,      lowercase							,      lowercase							,      lowercase							,      lowercase							)

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

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

    # Now we train the model
				for epoch in range(lowercase							,      lowercase							):
								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
												__lowercase			      =							accelerator.skip_first_batches(lowercase							,      lowercase							)
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												__lowercase			      =							train_dataloader
								for step, batch in enumerate(lowercase							):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device							)
												__lowercase			      =							model(**lowercase							)
												__lowercase			      =							outputs.loss
												__lowercase			      =							loss / gradient_accumulation_steps
												accelerator.backward(lowercase							)
												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(lowercase							,      lowercase							):
																__lowercase			      =							F"step_{overall_step}"
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								__lowercase			      =							os.path.join(args.output_dir							,      lowercase							)
																				accelerator.save_state(lowercase							)

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

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

								# 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":
												__lowercase			      =							F"epoch_{epoch}"
												if args.output_dir is not None:
																__lowercase			      =							os.path.join(args.output_dir							,      lowercase							)
												accelerator.save_state(lowercase							)





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							argparse.ArgumentParser(description='''Simple example of training script.'''							)
				parser.add_argument(
				    '''--mixed_precision'''							,      type=lowercase							,      default=lowercase							,      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=lowercase							,      default=lowercase							,      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=lowercase							,      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=lowercase							,      default=lowercase							,      help='''If the training should continue from a checkpoint folder.'''							,      )
				__lowercase			      =							parser.parse_args()
				__lowercase			      =							{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase							,      lowercase							)


if __name__ == "__main__":
		main()
from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


__a  :					Union[str, Any]          =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if isinstance(lowercase							,      lowercase							):
								return _split_by_node_map_style_dataset(lowercase							,      rank=lowercase							,      world_size=lowercase							)
				else:
								return _split_by_node_iterable_dataset(lowercase							,      rank=lowercase							,      world_size=lowercase							)