



'''simple docstring'''
import argparse

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


snake_case_					:      List[str]    			=    16
snake_case_					:      int    			=    32






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_ = 1_6				):
						_UpperCamelCase   :						Union[str, Any]												=  AutoTokenizer.from_pretrained('bert-base-cased'				)
						_UpperCamelCase   :						Union[str, Any]												=  load_dataset('glue'    ,    'mrpc'				)

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

						def collate_fn(UpperCAmelCase_				):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												_UpperCamelCase   :						Optional[int]												=  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   :						Dict												=  1_6
												elif accelerator.mixed_precision != "no":
																		_UpperCamelCase   :						int												=  8
												else:
																		_UpperCamelCase   :						Union[str, Any]												=  None

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

						# Instantiate dataloaders.
						_UpperCamelCase   :						List[str]												=  DataLoader(
						    tokenized_datasets['train']    ,    shuffle=UpperCAmelCase_    ,    collate_fn=UpperCAmelCase_    ,    batch_size=UpperCAmelCase_    ,    drop_last=UpperCAmelCase_				)
						_UpperCamelCase   :						Optional[Any]												=  DataLoader(
						    tokenized_datasets['validation']    ,    shuffle=UpperCAmelCase_    ,    collate_fn=UpperCAmelCase_    ,    batch_size=UpperCAmelCase_    ,    drop_last=(accelerator.mixed_precision == 'fp8')    ,    )

						return train_dataloader, eval_dataloader






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						# Initialize accelerator
						_UpperCamelCase   :						Any												=  Accelerator(cpu=args.cpu    ,    mixed_precision=args.mixed_precision				)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_UpperCamelCase   :						List[Any]												=  config['lr']
						_UpperCamelCase   :						List[Any]												=  int(config['num_epochs']				)
						_UpperCamelCase   :						Any												=  int(config['seed']				)
						_UpperCamelCase   :						Any												=  int(config['batch_size']				)

						_UpperCamelCase   :						Union[str, Any]												=  evaluate.load('glue'    ,    'mrpc'				)

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

						set_seed(UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase   :						int												=  get_dataloaders(UpperCAmelCase_    ,    UpperCAmelCase_				)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_UpperCamelCase   :						int												=  AutoModelForSequenceClassification.from_pretrained('bert-base-cased'    ,    return_dict=UpperCAmelCase_				)

						# 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   :						str												=  model.to(accelerator.device				)
						# Instantiate optimizer
						_UpperCamelCase   :						List[Any]												=  AdamW(params=model.parameters()    ,    lr=UpperCAmelCase_				)

						# Instantiate scheduler
						_UpperCamelCase   :						List[Any]												=  get_linear_schedule_with_warmup(
						    optimizer=UpperCAmelCase_    ,    num_warmup_steps=1_0_0    ,    num_training_steps=(len(UpperCAmelCase_				) * 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							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Any												=  accelerator.prepare(
						    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)

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

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

												_UpperCamelCase   :						Optional[int]												=  metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'epoch {epoch}:'    ,    UpperCAmelCase_				)






def A__       (	):
						_UpperCamelCase   :						List[str]												=  argparse.ArgumentParser(description='Simple example of training script.'				)
						parser.add_argument(
						    '--mixed_precision'    ,    type=UpperCAmelCase_    ,    default=UpperCAmelCase_    ,    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.'				)
						_UpperCamelCase   :						List[str]												=  parser.parse_args()
						_UpperCamelCase   :						Dict												=  {'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
						training_function(UpperCAmelCase_    ,    UpperCAmelCase_				)


if __name__ == "__main__":
					main()







'''simple docstring'''
from typing import List

import numpy as np






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Any												=  {key: len(UpperCAmelCase_				) for key, value in gen_kwargs.items() if isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)}
						if len(set(lists_lengths.values()				)				) > 1:
												raise RuntimeError(
												    (
												        'Sharding is ambiguous for this dataset: '
												        + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
												        + '\n'.join(f'\t- key {key} has length {length}' for key, length in lists_lengths.items()				)
												        + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
												        + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
												    )				)
						_UpperCamelCase   :						Tuple												=  max(lists_lengths.values()    ,    default=0				)
						return max(1    ,    UpperCAmelCase_				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  []
						for group_idx in range(UpperCAmelCase_				):
												_UpperCamelCase   :						int												=  num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
												if num_shards_to_add == 0:
																		break
												_UpperCamelCase   :						Tuple												=  shards_indices_per_group[-1].stop if shards_indices_per_group else 0
												_UpperCamelCase   :						Tuple												=  range(UpperCAmelCase_    ,    start + num_shards_to_add				)
												shards_indices_per_group.append(UpperCAmelCase_				)
						return shards_indices_per_group






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Tuple												=  _number_of_shards_in_gen_kwargs(UpperCAmelCase_				)
						if num_shards == 1:
												return [dict(UpperCAmelCase_				)]
						else:
												_UpperCamelCase   :						str												=  _distribute_shards(num_shards=UpperCAmelCase_    ,    max_num_jobs=UpperCAmelCase_				)
												return [
												    {
												        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
												        if isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)
												        else value
												        for key, value in gen_kwargs.items()
												    }
												    for group_idx in range(len(UpperCAmelCase_				)				)
												]






def A__       (	UpperCAmelCase_				):
						return {
						    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
						    if isinstance(gen_kwargs_list[0][key]    ,    UpperCAmelCase_				)
						    else gen_kwargs_list[0][key]
						    for key in gen_kwargs_list[0]
						}






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						int												=  {len(UpperCAmelCase_				) for value in gen_kwargs.values() if isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)}
						_UpperCamelCase   :						Union[str, Any]												=  {}
						for size in list_sizes:
												_UpperCamelCase   :						str												=  list(range(UpperCAmelCase_				)				)
												rng.shuffle(indices_per_size[size]				)
						# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
						_UpperCamelCase   :						Union[str, Any]												=  dict(UpperCAmelCase_				)
						for key, value in shuffled_kwargs.items():
												if isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				):
																		_UpperCamelCase   :						Dict												=  [value[i] for i in indices_per_size[len(UpperCAmelCase_				)]]
						return shuffled_kwargs


