
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


SCREAMING_SNAKE_CASE							:int							 =				16
SCREAMING_SNAKE_CASE							:Any							 =				32


def 						UpperCAmelCase			(	a_		,   a_ = 1_6		,   a_ = "bert-base-cased"						)		->	Union[str, Any]:







				"""simple docstring"""



				__A   			= AutoTokenizer.from_pretrained(a_						)
				__A   			= load_dataset("glue"		,   "mrpc"						)

				def tokenize_function(a_						):
								# max_length=None => use the model max length (it's actually the default)
								__A   			= 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
				__A   			= datasets.map(
				    a_		,   batched=a_		,   remove_columns=["idx", "sentence1", "sentence2"]		,   load_from_cache_file=a_						)

				# 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(a_						):
								# 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(a_		,   padding="max_length"		,   max_length=1_2_8		,   return_tensors="pt"						)
								return tokenizer.pad(a_		,   padding="longest"		,   return_tensors="pt"						)

				# Instantiate dataloaders.
				__A   			= DataLoader(
				    tokenized_datasets["train"]		,   shuffle=a_		,   collate_fn=a_		,   batch_size=a_						)
				__A   			= DataLoader(
				    tokenized_datasets["validation"]		,   shuffle=a_		,   collate_fn=a_		,   batch_size=a_						)

				return train_dataloader, eval_dataloader


def 						UpperCAmelCase			(	a_		,   a_						)		->	List[str]:







				"""simple docstring"""



				__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(a_						)
				__A			,							__A   			= get_dataloaders(a_		,   a_		,   a_						)

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

				# 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=a_						)

				if accelerator.state.deepspeed_plugin is not None:
								__A   			= accelerator.state.deepspeed_plugin.deepspeed_config[
								    "gradient_accumulation_steps"
								]
				else:
								__A   			= 1
				__A   			= (len(a_						) * 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=a_		,   num_warmup_steps=0		,   num_training_steps=a_		,   )
				else:
								__A   			= DummyScheduler(a_		,   total_num_steps=a_		,   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(
				    a_		,   a_		,   a_		,   a_		,   a_						)

				# 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(a_		,   a_						):
								model.train()
								for step, batch in enumerate(a_						):
												__A   			= model(**a_						)
												__A   			= outputs.loss
												__A   			= loss / gradient_accumulation_steps
												accelerator.backward(a_						)
												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(a_						):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device						)
												with torch.no_grad():
																__A   			= model(**a_						)
												__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(a_						) - 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=a_		,   references=a_		,   )

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


def 						UpperCAmelCase			(	)		->	List[str]:







				"""simple docstring"""



				__A   			= argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."						)
				parser.add_argument(
				    "--model_name_or_path"		,   type=a_		,   default="bert-base-cased"		,   help="Path to pretrained model or model identifier from huggingface.co/models."		,   required=a_		,   )
				parser.add_argument(
				    "--output_dir"		,   type=a_		,   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=a_		,   default=a_		,   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=a_		,   default=3		,   help="Number of train epochs."		,   )
				__A   			= parser.parse_args()
				__A   			= {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
				training_function(a_		,   a_						)


if __name__ == "__main__":
					main()


def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) <= 1:
								return [tuple(a_						)]

				__A   			= []

				def generate(a_		,   a_						):
								if k == 1:
												res.append(tuple(arr[:]						)						)
												return

								generate(k - 1		,   a_						)

								for i in range(k - 1						):
												if k % 2 == 0:  # k is even
																__A			,							__A   			= arr[k - 1], arr[i]
												else:  # k is odd
																__A			,							__A   			= arr[k - 1], arr[0]
												generate(k - 1		,   a_						)

				generate(len(a_						)		,   a_						)
				return res


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:int							 =				input('Enter numbers separated by a comma:\n').strip()
					SCREAMING_SNAKE_CASE							:Dict							 =				[int(item) for item in user_input.split(',')]
					print(heaps(arr))
