




'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 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									=16
A									=32





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

    def tokenize_function(_a			:							int  ):
        # 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
    # starting with the main process first:
    with accelerator.main_process_first():
        UpperCAmelCase     =	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
    UpperCAmelCase     =	tokenized_datasets.rename_column('''label'''					,     '''labels'''  )

    def collate_fn(_a			:							Tuple  ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        UpperCAmelCase     =	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":
            UpperCAmelCase     =	1_6
        elif accelerator.mixed_precision != "no":
            UpperCAmelCase     =	8
        else:
            UpperCAmelCase     =	None

        return tokenizer.pad(
            _a					,     padding='''longest'''					,     max_length=_a					,     pad_to_multiple_of=_a					,     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


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   A									=mocked_dataloaders  # noqa: F811





def 		snake_case_							(_a			:							int					,     _a			:							List[str]  ):
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''					,     _a  ) == "1":
        UpperCAmelCase     =	2
    # New Code #
    UpperCAmelCase     =	int(args.gradient_accumulation_steps  )
    UpperCAmelCase     =	int(args.local_sgd_steps  )
    # Initialize accelerator
    UpperCAmelCase     =	Accelerator(
        cpu=args.cpu					,     mixed_precision=args.mixed_precision					,     gradient_accumulation_steps=_a  )
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''  )
    # 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     =	evaluate.load('''glue'''					,     '''mrpc'''  )

    set_seed(_a  )
    UpperCAmelCase ,  UpperCAmelCase     =	get_dataloaders(_a					,     _a  )
    # 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=_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).
    UpperCAmelCase     =	model.to(accelerator.device  )

    # Instantiate optimizer
    UpperCAmelCase     =	AdamW(params=model.parameters()					,     lr=_a  )

    # Instantiate scheduler
    UpperCAmelCase     =	get_linear_schedule_with_warmup(
        optimizer=_a					,     num_warmup_steps=1_0_0					,     num_training_steps=(len(_a  ) * num_epochs)					,     )

    # 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  )

    # Now we train the model
    for epoch in range(_a  ):
        model.train()
        with LocalSGD(
            accelerator=_a					,     model=_a					,     local_sgd_steps=_a					,     enabled=local_sgd_steps is not None  ) as local_sgd:
            for step, batch in enumerate(_a  ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device  )
                # New code #
                # We use the new `accumulate` context manager to perform gradient accumulation
                # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                with accelerator.accumulate(_a  ):
                    UpperCAmelCase     =	model(**_a  )
                    UpperCAmelCase     =	output.loss
                    accelerator.backward(_a  )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

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

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





def 		snake_case_							():
    UpperCAmelCase     =	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.'''					,     )
    # New Code #
    parser.add_argument(
        '''--gradient_accumulation_steps'''					,     type=_a					,     default=1					,     help='''The number of minibatches to be ran before gradients are accumulated.'''					,     )
    parser.add_argument(
        '''--local_sgd_steps'''					,     type=_a					,     default=8					,     help='''Number of local SGD steps or None to disable local SGD'''  )
    parser.add_argument('''--cpu'''					,     action='''store_true'''					,     help='''If passed, will train on the CPU.'''  )
    UpperCAmelCase     =	parser.parse_args()
    UpperCAmelCase     =	{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
    training_function(_a					,     _a  )


if __name__ == "__main__":
   main()










'''simple docstring'''







def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							int					,     _a			:							int					,     _a			:							list[int]  ):

    # 1. Validate that path exists between current and next vertices
    if graph[path[curr_ind - 1]][next_ver] == 0:
        return False

    # 2. Validate that next vertex is not already in path
    return not any(vertex == next_ver for vertex in path  )





def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							list[int]					,     _a			:							int  ):

    # Base Case
    if curr_ind == len(_a  ):
        # return whether path exists between current and starting vertices
        return graph[path[curr_ind - 1]][path[0]] == 1

    # Recursive Step
    for next_ver in range(0					,     len(_a  )  ):
        if valid_connection(_a					,     _a					,     _a					,     _a  ):
            # Insert current vertex  into path as next transition
            UpperCAmelCase     =	next_ver
            # Validate created path
            if util_hamilton_cycle(_a					,     _a					,     curr_ind + 1  ):
                return True
            # Backtrack
            UpperCAmelCase     =	-1
    return False







def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							int = 0  ):
    UpperCAmelCase     =	[-1] * (len(_a  ) + 1)
    # initialize start and end of path with starting index
    UpperCAmelCase     =	UpperCAmelCase     =	start_index
    # evaluate and if we find answer return path either return empty array
    return path if util_hamilton_cycle(_a					,     _a					,     1  ) else []




