


"""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


lowercase_			     =  1_6
lowercase_			     =  3_2

def        lowercase							(		lowerCAmelCase__ :     Accelerator    ,					lowerCAmelCase__ :     int = 16    ,					lowerCAmelCase__ :     str = "bert-base-cased"			) ->      List[str]:
		__a           =			AutoTokenizer.from_pretrained(lowerCAmelCase__			)
		__a           =			load_dataset('''glue'''    ,					'''mrpc'''			)

		def tokenize_function(lowerCAmelCase__ :     Any			):
				# 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
		__a           =			datasets.map(
		    lowerCAmelCase__    ,					batched=lowerCAmelCase__    ,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,					load_from_cache_file=lowerCAmelCase__			)

		# 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__ :     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(lowerCAmelCase__    ,					padding='''max_length'''    ,					max_length=128    ,					return_tensors='''pt'''			)
				return tokenizer.pad(lowerCAmelCase__    ,					padding='''longest'''    ,					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

def        lowercase							(		lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     Any			) ->      List[str]:
		# Initialize accelerator
		__a           =			Accelerator()

		# 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           =			args.model_name_or_path

		set_seed(lowerCAmelCase__			)
		__a      ,     __a           =			get_dataloaders(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)

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

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

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

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

		# We need to keep track of how many total steps we have iterated over
		__a           =			0
		# We also need to keep track of the stating epoch so files are named properly
		__a           =			0

		# Now we train the model
		__a           =			evaluate.load('''glue'''    ,					'''mrpc'''			)
		__a           =			0
		__a           =			{}
		for epoch in range(lowerCAmelCase__    ,					lowerCAmelCase__			):
				model.train()
				for step, batch in enumerate(lowerCAmelCase__			):
						__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()

						overall_step += 1

				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			)
						# It is slightly faster to call this once, than multiple times
						__a      ,     __a           =			accelerator.gather(
						    (predictions, batch['''labels'''])			)  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
								if step == len(lowerCAmelCase__			) - 1:
										__a           =			predictions[: len(eval_dataloader.dataset			) - samples_seen]
										__a           =			references[: len(eval_dataloader.dataset			) - samples_seen]
								else:
										samples_seen += references.shape[0]
						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__			)
				__a           =			eval_metric['''accuracy''']

				if best_performance < eval_metric["accuracy"]:
						__a           =			eval_metric['''accuracy''']

		if args.performance_lower_bound is not None:
				assert (
				    args.performance_lower_bound <= best_performance
				), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
				with open(os.path.join(args.output_dir    ,					'''all_results.json'''			)    ,					'''w'''			) as f:
						json.dump(lowerCAmelCase__    ,					lowerCAmelCase__			)

def        lowercase							(		) ->      int:
		__a           =			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''			)
		parser.add_argument(
		    '''--model_name_or_path'''    ,					type=lowerCAmelCase__    ,					default='''bert-base-cased'''    ,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''    ,					required=lowerCAmelCase__    ,					)
		parser.add_argument(
		    '''--output_dir'''    ,					type=lowerCAmelCase__    ,					default='''.'''    ,					help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''    ,					)
		parser.add_argument(
		    '''--performance_lower_bound'''    ,					type=lowerCAmelCase__    ,					default=lowerCAmelCase__    ,					help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''    ,					)
		parser.add_argument(
		    '''--num_epochs'''    ,					type=lowerCAmelCase__    ,					default=3    ,					help='''Number of train epochs.'''    ,					)
		__a           =			parser.parse_args()
		__a           =			{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
		training_function(lowerCAmelCase__    ,					lowerCAmelCase__			)


if __name__ == "__main__":
				main()











'''simple docstring'''




from math import pow



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):
			'''simple docstring'''





			if current_sum == needed_sum:
						# If the sum of the powers is equal to needed_sum, then we have a solution.
						solutions_count += 1
						return current_sum, solutions_count

			A_	:						Optional[int]	=     int(pow(lowerCamelCase__						,      lowerCamelCase__    )    )
			if current_sum + i_to_n <= needed_sum:
						# If the sum of the powers is less than needed_sum, then continue adding powers.
						current_sum += i_to_n
						A_,    A_	:						int	=     backtrack(
						    lowerCamelCase__						,      lowerCamelCase__						,      current_number + 1						,      lowerCamelCase__						,      lowerCamelCase__    )
						current_sum -= i_to_n
			if i_to_n < needed_sum:
						# If the power of i is less than needed_sum, then try with the next power.
						A_,    A_	:						int	=     backtrack(
						    lowerCamelCase__						,      lowerCamelCase__						,      current_number + 1						,      lowerCamelCase__						,      lowerCamelCase__    )
			return current_sum, solutions_count



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
						raise ValueError(
						    """Invalid input\n"""
						    """needed_sum must be between 1 and 1000, power between 2 and 10."""    )

			return backtrack(lowerCamelCase__						,      lowerCamelCase__						,      1						,      0						,      0    )[1]  # Return the solutions_count


if __name__ == "__main__":
						import doctest

						doctest.testmod()