'''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


lowerCAmelCase_   :   Optional[int]       =					16
lowerCAmelCase_   :   List[Any]       =					32







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     Optional[int] = 16					,  __lowerCamelCase  :     Dict = "bert-base-cased"     )   ->    int:
			a__     = AutoTokenizer.from_pretrained(__lowerCamelCase     )
			a__     = load_dataset("glue"					,  "mrpc"     )

			def tokenize_function(__lowerCamelCase  :     Dict     ):
						# 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  :     Optional[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    _lowerCamelCase  (__lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Union[str, 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__     = 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__     = 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__     = 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    _lowerCamelCase  ()   ->    List[str]:
			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'''


def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    bool:
			a__     = 0
			for ch in input_str:
						a__     = ord(__lowerCamelCase     )
						a__     = pow(2					,  __lowerCamelCase     )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
		import doctest

		doctest.testmod()



