


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


_A			=						16
_A			=						32

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : List[Any] = 16    ):
					__UpperCamelCase   							=AutoTokenizer.from_pretrained('bert-base-cased'    )
					__UpperCamelCase   							=load_dataset('glue'      ,					'mrpc'    )

					def tokenize_function(SCREAMING_SNAKE_CASE__      : List[str]    ):
										# max_length=None => use the model max length (it's actually the default)
										__UpperCamelCase   							=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():
										__UpperCamelCase   							=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
					__UpperCamelCase   							=tokenized_datasets.rename_column('label'      ,					'labels'    )

					def collate_fn(SCREAMING_SNAKE_CASE__      : Optional[int]    ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										__UpperCamelCase   							=1_28 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   							=16
										elif accelerator.mixed_precision != "no":
															__UpperCamelCase   							=8
										else:
															__UpperCamelCase   							=None

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

					# Instantiate dataloaders.
					__UpperCamelCase   							=DataLoader(
					    tokenized_datasets['train']      ,					shuffle=_lowerCamelCase      ,					collate_fn=_lowerCamelCase      ,					batch_size=_lowerCamelCase      ,					drop_last=_lowerCamelCase    )
					__UpperCamelCase   							=DataLoader(
					    tokenized_datasets['validation']      ,					shuffle=_lowerCamelCase      ,					collate_fn=_lowerCamelCase      ,					batch_size=_lowerCamelCase      ,					drop_last=(accelerator.mixed_precision == 'fp8')      ,					)

					return train_dataloader, eval_dataloader

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : Any    ):
					__UpperCamelCase   							=Accelerator(cpu=args.cpu      ,					mixed_precision=args.mixed_precision    )
					# 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'    )

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

					set_seed(_lowerCamelCase    )
					__UpperCamelCase   							=get_dataloaders(_lowerCamelCase      ,					_lowerCamelCase    )
					# 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=_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).
					__UpperCamelCase   							=model.to(accelerator.device    )
					# Instantiate optimizer
					__UpperCamelCase   							=AdamW(params=model.parameters()      ,					lr=_lowerCamelCase    )

					# Instantiate scheduler
					__UpperCamelCase   							=get_linear_schedule_with_warmup(
					    optimizer=_lowerCamelCase      ,					num_warmup_steps=1_00      ,					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.

					__UpperCamelCase   							=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    )
															__UpperCamelCase   							=model(**_lowerCamelCase    )
															__UpperCamelCase   							=outputs.loss
															__UpperCamelCase   							=loss / gradient_accumulation_steps
															accelerator.backward(_lowerCamelCase    )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

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

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

def 						_UpperCAmelCase    (      ):
					__UpperCamelCase   							=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.'    )
					__UpperCamelCase   							=parser.parse_args()
					__UpperCamelCase   							={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
					training_function(_lowerCamelCase      ,					_lowerCamelCase    )


if __name__ == "__main__":
			main()









from __future__ import annotations

from typing import Any

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : list[Any]    ):
     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					[]      ,					0    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : list[Any]      ,					SCREAMING_SNAKE_CASE__      : list[Any]      ,					SCREAMING_SNAKE_CASE__      : int    ):

     if index == len(SCREAMING_SNAKE_CASE__    ):
          print(SCREAMING_SNAKE_CASE__    )
          return

     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					index + 1    )
     current_subsequence.append(sequence[index]    )
     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					index + 1    )
     current_subsequence.pop()


if __name__ == "__main__":
   _A			=						[3, 1, 2, 4]
   generate_all_subsequences(seq)

   seq.clear()
   seq.extend(['A', 'B', 'C'])
   generate_all_subsequences(seq)





