


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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


snake_case							:	Dict					      =				16
snake_case							:	List[Any]					      =				32






def 							__lowercase       (			__lowerCAmelCase							:    Accelerator ,   __lowerCAmelCase							:    int = 1_6       ):
							a__					=					AutoTokenizer.from_pretrained('bert-base-cased'       )
							a__					=					load_dataset('glue' ,   'mrpc'       )

							def tokenize_function(__lowerCAmelCase							:    Optional[int]       ):
														# max_length=None => use the model max length (it's actually the default)
														a__					=					tokenizer(examples['sentence1'] ,   examples['sentence2'] ,   truncation=__lowerCAmelCase ,   max_length=__lowerCAmelCase       )
														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(
														    __lowerCAmelCase ,   batched=__lowerCAmelCase ,   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'       )

							def collate_fn(__lowerCAmelCase							:    str       ):
														# 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(
														    __lowerCAmelCase ,   padding='longest' ,   max_length=__lowerCAmelCase ,   pad_to_multiple_of=__lowerCAmelCase ,   return_tensors='pt' ,   )

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

							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

						snake_case							:	Optional[Any]					      =				mocked_dataloaders  # noqa: F811






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    List[Any]       ):
							# For testing only
							if os.environ.get('TESTING_MOCKED_DATALOADERS' ,   __lowerCAmelCase       ) == "1":
														a__					=					2
							# Initialize accelerator
							a__					=					Accelerator(cpu=args.cpu ,   mixed_precision=args.mixed_precision       )
							# 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']       )

							a__					=					evaluate.load('glue' ,   'mrpc'       )

							# 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

							set_seed(__lowerCAmelCase       )
							a__				, a__					=					get_dataloaders(__lowerCAmelCase ,   __lowerCAmelCase       )
							# 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=__lowerCAmelCase       )

							# 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=__lowerCAmelCase       )

							# Instantiate scheduler
							a__					=					get_linear_schedule_with_warmup(
							    optimizer=__lowerCAmelCase ,   num_warmup_steps=1_0_0 ,   num_training_steps=(len(__lowerCAmelCase       ) * 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(
							    __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

							# Now we train the model
							for epoch in range(__lowerCAmelCase       ):
														model.train()
														for step, batch in enumerate(__lowerCAmelCase       ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device       )
																					a__					=					model(**__lowerCAmelCase       )
																					a__					=					outputs.loss
																					a__					=					loss / gradient_accumulation_steps
																					accelerator.backward(__lowerCAmelCase       )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														a__					=					0
														for step, batch in enumerate(__lowerCAmelCase       ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device       )
																					with torch.no_grad():
																												a__					=					model(**__lowerCAmelCase       )
																					a__					=					outputs.logits.argmax(dim=-1       )
																					a__				, a__					=					accelerator.gather((predictions, batch['labels'])       )
																					# New Code #
																					# First we check if it's a distributed system
																					if accelerator.use_distributed:
																												# Then see if we're on the last batch of our eval dataloader
																												if step == len(__lowerCAmelCase       ) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			a__					=					predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																																			a__					=					references[: len(eval_dataloader.dataset       ) - samples_seen]
																												else:
																																			# Otherwise we add the number of samples seen
																																			samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																					metric.add_batch(
																					    predictions=__lowerCAmelCase ,   references=__lowerCAmelCase ,   )

														a__					=					metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'epoch {epoch}:' ,   __lowerCAmelCase       )






def 							__lowercase       (			):
							a__					=					argparse.ArgumentParser(description='Simple example of training script.'       )
							parser.add_argument(
							    '--mixed_precision' ,   type=__lowerCAmelCase ,   default=__lowerCAmelCase ,   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.'       )
							a__					=					parser.parse_args()
							a__					=					{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
							training_function(__lowerCAmelCase ,   __lowerCAmelCase       )


if __name__ == "__main__":
						main()








def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
							return [
							    {
							        0: [1, 2],
							        1: [0, 2],
							        2: [0, 1, 3, 5],
							        3: [2, 4],
							        4: [3],
							        5: [2, 6, 8],
							        6: [5, 7],
							        7: [6, 8],
							        8: [5, 7],
							    },
							    {
							        0: [6],
							        1: [9],
							        2: [4, 5],
							        3: [4],
							        4: [2, 3],
							        5: [2],
							        6: [0, 7],
							        7: [6],
							        8: [],
							        9: [1],
							    },
							    {
							        0: [4],
							        1: [6],
							        2: [],
							        3: [5, 6, 7],
							        4: [0, 6],
							        5: [3, 8, 9],
							        6: [1, 3, 4, 7],
							        7: [3, 6, 8, 9],
							        8: [5, 7],
							        9: [5, 7],
							    },
							    {
							        0: [1, 3],
							        1: [0, 2, 4],
							        2: [1, 3, 4],
							        3: [0, 2, 4],
							        4: [1, 2, 3],
							    },
							][index]






def 							__lowercase       (			__lowerCAmelCase							:    dict[int, list[int]]       ):
							a__					=					0
							a__					=					len(__lowerCAmelCase       )  # No of vertices in graph
							a__					=					[0] * n
							a__					=					[False] * n

							def dfs(__lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Tuple       ):
														a__					=					True
														a__					=					id_
														id_ += 1
														for to in graph[at]:
																					if to == parent:
																												pass
																					elif not visited[to]:
																												dfs(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   id_       )
																												a__					=					min(low[at] ,   low[to]       )
																												if id_ <= low[to]:
																																			bridges.append((at, to) if at < to else (to, at)       )
																					else:
																												# This edge is a back edge and cannot be a bridge
																												a__					=					min(low[at] ,   low[to]       )

							a__					=					[]
							for i in range(__lowerCAmelCase       ):
														if not visited[i]:
																					dfs(__lowerCAmelCase ,   -1 ,   __lowerCAmelCase ,   id_       )
							return bridges


if __name__ == "__main__":
						import doctest

						doctest.testmod()




