





'''simple docstring'''







import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A									=16
A									=32





def 		snake_case_							(_a			:							Accelerator					,     _a			:							int = 1_6					,     _a			:							str = "bert-base-cased"  ):
    UpperCAmelCase     =	AutoTokenizer.from_pretrained(_a  )
    UpperCAmelCase     =	load_dataset('''glue'''					,     '''mrpc'''  )

    def tokenize_function(_a			:							List[Any]  ):
        # max_length=None => use the model max length (it's actually the default)
        UpperCAmelCase     =	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
    UpperCAmelCase     =	datasets.map(
        _a					,     batched=_a					,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']					,     load_from_cache_file=_a  )

    # 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(_a			:							Union[str, Any]  ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(_a					,     padding='''max_length'''					,     max_length=1_2_8					,     return_tensors='''pt'''  )
        return tokenizer.pad(_a					,     padding='''longest'''					,     return_tensors='''pt'''  )

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

    return train_dataloader, eval_dataloader





def 		snake_case_							(_a			:							Dict					,     _a			:							Union[str, Any]					,     _a			:							List[Any]					,     _a			:							Tuple  ):
    model.eval()
    UpperCAmelCase     =	0
    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():
            UpperCAmelCase     =	model(**_a  )
        UpperCAmelCase     =	outputs.logits.argmax(dim=-1  )
        # It is slightly faster to call this once, than multiple times
        UpperCAmelCase ,  UpperCAmelCase     =	accelerator.gather(
            (predictions, batch['''labels'''])  )  # If we are in a multiprocess environment, the last batch has duplicates
        if accelerator.use_distributed:
            if step == len(_a  ) - 1:
                UpperCAmelCase     =	predictions[: len(eval_dataloader.dataset  ) - samples_seen]
                UpperCAmelCase     =	references[: len(eval_dataloader.dataset  ) - samples_seen]
            else:
                samples_seen += references.shape[0]
        metric.add_batch(
            predictions=_a					,     references=_a					,     )

    UpperCAmelCase     =	metric.compute()
    return eval_metric["accuracy"]





def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							Any  ):
    # Initialize accelerator
    UpperCAmelCase     =	Accelerator()

    # 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''']  )
    UpperCAmelCase     =	args.model_name_or_path

    set_seed(_a  )
    UpperCAmelCase ,  UpperCAmelCase     =	get_dataloaders(_a					,     _a					,     _a  )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    UpperCAmelCase     =	AutoModelForSequenceClassification.from_pretrained(_a					,     return_dict=_a  )

    # Instantiate optimizer
    UpperCAmelCase     =	(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    UpperCAmelCase     =	optimizer_cls(params=model.parameters()					,     lr=_a  )

    if accelerator.state.deepspeed_plugin is not None:
        UpperCAmelCase     =	accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        UpperCAmelCase     =	1
    UpperCAmelCase     =	(len(_a  ) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        UpperCAmelCase     =	get_linear_schedule_with_warmup(
            optimizer=_a					,     num_warmup_steps=0					,     num_training_steps=_a					,     )
    else:
        UpperCAmelCase     =	DummyScheduler(_a					,     total_num_steps=_a					,     warmup_num_steps=0  )

    # 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 ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	accelerator.prepare(
        _a					,     _a					,     _a					,     _a					,     _a  )

    # 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
    UpperCAmelCase     =	evaluate.load('''glue'''					,     '''mrpc'''  )
    UpperCAmelCase     =	num_epochs

    if args.partial_train_epoch is not None:
        UpperCAmelCase     =	args.partial_train_epoch

    if args.resume_from_checkpoint:
        accelerator.load_state(args.resume_from_checkpoint  )
        UpperCAmelCase     =	args.resume_from_checkpoint.split('''epoch_'''  )[1]
        UpperCAmelCase     =	''''''
        for char in epoch_string:
            if char.isdigit():
                state_epoch_num += char
            else:
                break
        UpperCAmelCase     =	int(_a  ) + 1
        UpperCAmelCase     =	evaluation_loop(_a					,     _a					,     _a					,     _a  )
        accelerator.print('''resumed checkpoint performance:'''					,     _a  )
        accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''					,     lr_scheduler.get_lr()[0]  )
        accelerator.print('''resumed optimizers\'s lr:'''					,     optimizer.param_groups[0]['''lr''']  )
        with open(os.path.join(args.output_dir					,     F"state_{starting_epoch-1}.json"  )					,     '''r'''  ) as f:
            UpperCAmelCase     =	json.load(_a  )
            assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
            assert (
                resumed_state["lr"] == lr_scheduler.get_lr()[0]
            ), "Scheduler learning rate mismatch, loading from checkpoint failed"
            assert (
                resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
            ), "Optimizer learning rate mismatch, loading from checkpoint failed"
            assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
            return

    # Now we train the model
    UpperCAmelCase     =	{}
    for epoch in range(_a					,     _a  ):
        model.train()
        for step, batch in enumerate(_a  ):
            UpperCAmelCase     =	model(**_a  )
            UpperCAmelCase     =	outputs.loss
            UpperCAmelCase     =	loss / gradient_accumulation_steps
            accelerator.backward(_a  )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1
        UpperCAmelCase     =	F"epoch_{epoch}"
        UpperCAmelCase     =	os.path.join(args.output_dir					,     _a  )
        accelerator.save_state(_a  )
        UpperCAmelCase     =	evaluation_loop(_a					,     _a					,     _a					,     _a  )
        UpperCAmelCase     =	accuracy
        UpperCAmelCase     =	lr_scheduler.get_lr()[0]
        UpperCAmelCase     =	optimizer.param_groups[0]['''lr''']
        UpperCAmelCase     =	epoch
        UpperCAmelCase     =	overall_step
        accelerator.print(F"epoch {epoch}:"					,     _a  )

        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir					,     F"state_{epoch}.json"  )					,     '''w'''  ) as f:
                json.dump(_a					,     _a  )





def 		snake_case_							():
    UpperCAmelCase     =	argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''  )
    parser.add_argument(
        '''--model_name_or_path'''					,     type=_a					,     default='''bert-base-cased'''					,     help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,     required=_a					,     )
    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(
        '''--resume_from_checkpoint'''					,     type=_a					,     default=_a					,     help='''If the training should continue from a checkpoint folder.'''					,     )
    parser.add_argument(
        '''--partial_train_epoch'''					,     type=_a					,     default=_a					,     help='''If passed, the training will stop after this number of epochs.'''					,     )
    parser.add_argument(
        '''--num_epochs'''					,     type=_a					,     default=2					,     help='''Number of train epochs.'''					,     )
    UpperCAmelCase     =	parser.parse_args()
    UpperCAmelCase     =	{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}

    training_function(_a					,     _a  )


if __name__ == "__main__":
   main()










'''simple docstring'''







from __future__ import annotations





def 		snake_case_							(_a			:							str					,     _a			:							list[str] | None = None  ):
    UpperCAmelCase     =	word_bank or []
    # create a table
    UpperCAmelCase     =	len(_a  ) + 1

    UpperCAmelCase     =	[]
    for _ in range(_a  ):
        table.append([]  )
    # seed value
    UpperCAmelCase     =	[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(_a  ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(_a  )] == word:
                    UpperCAmelCase     =	[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(_a  )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(_a  )]:
        combination.reverse()

    return table[len(_a  )]


if __name__ == "__main__":
   print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
   print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
   print(
       all_construct(
           'hexagonosaurus',
           ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
       )
   )




