




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


UpperCamelCase__					    =    16
UpperCamelCase__					    =    32







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Accelerator   ,   SCREAMING_SNAKE_CASE_							:       int = 16   ,   SCREAMING_SNAKE_CASE_							:       str = "bert-base-cased"  ):
					__lowerCAmelCase            = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase            = load_dataset("glue"   ,   "mrpc"  )

					def tokenize_function(SCREAMING_SNAKE_CASE_							:       str  ):
										# max_length=None => use the model max length (it's actually the default)
										__lowerCAmelCase            = tokenizer(examples["sentence1"]   ,   examples["sentence2"]   ,   truncation=SCREAMING_SNAKE_CASE_   ,   max_length=SCREAMING_SNAKE_CASE_  )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					__lowerCAmelCase            = datasets.map(
					    SCREAMING_SNAKE_CASE_   ,   batched=SCREAMING_SNAKE_CASE_   ,   remove_columns=["idx", "sentence1", "sentence2"]   ,   load_from_cache_file=SCREAMING_SNAKE_CASE_  )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					__lowerCAmelCase            = tokenized_datasets.rename_column("label"   ,   "labels"  )

					def collate_fn(SCREAMING_SNAKE_CASE_							:       List[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(SCREAMING_SNAKE_CASE_   ,   padding="max_length"   ,   max_length=1_28   ,   return_tensors="pt"  )
										return tokenizer.pad(SCREAMING_SNAKE_CASE_   ,   padding="longest"   ,   return_tensors="pt"  )

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

					return train_dataloader, eval_dataloader







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Tuple   ,   SCREAMING_SNAKE_CASE_							:       Any   ,   SCREAMING_SNAKE_CASE_							:       Any   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]  ):
					model.eval()
					__lowerCAmelCase            = 0
					for step, batch in enumerate(SCREAMING_SNAKE_CASE_  ):
										# We could avoid this line since we set the accelerator with `device_placement=True`.
										batch.to(accelerator.device  )
										with torch.no_grad():
															__lowerCAmelCase            = model(**SCREAMING_SNAKE_CASE_  )
										__lowerCAmelCase            = outputs.logits.argmax(dim=-1  )
										# It is slightly faster to call this once, than multiple times
										__lowerCAmelCase     ,							__lowerCAmelCase            = accelerator.gather(
										    (predictions, batch["labels"])  )  # If we are in a multiprocess environment, the last batch has duplicates
										if accelerator.use_distributed:
															if step == len(SCREAMING_SNAKE_CASE_  ) - 1:
																				__lowerCAmelCase            = predictions[: len(eval_dataloader.dataset  ) - samples_seen]
																				__lowerCAmelCase            = references[: len(eval_dataloader.dataset  ) - samples_seen]
															else:
																				samples_seen += references.shape[0]
										metric.add_batch(
										    predictions=SCREAMING_SNAKE_CASE_   ,   references=SCREAMING_SNAKE_CASE_   ,   )

					__lowerCAmelCase            = metric.compute()
					return eval_metric["accuracy"]







def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[str]   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]  ):
					# Initialize accelerator
					__lowerCAmelCase            = Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__lowerCAmelCase            = config["lr"]
					__lowerCAmelCase            = int(config["num_epochs"]  )
					__lowerCAmelCase            = int(config["seed"]  )
					__lowerCAmelCase            = int(config["batch_size"]  )
					__lowerCAmelCase            = args.model_name_or_path

					set_seed(SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase     ,							__lowerCAmelCase            = get_dataloaders(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )

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

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

					if accelerator.state.deepspeed_plugin is not None:
										__lowerCAmelCase            = accelerator.state.deepspeed_plugin.deepspeed_config[
										    "gradient_accumulation_steps"
										]
					else:
										__lowerCAmelCase            = 1
					__lowerCAmelCase            = (len(SCREAMING_SNAKE_CASE_  ) * num_epochs) // gradient_accumulation_steps

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

					# We need to keep track of how many total steps we have iterated over
					__lowerCAmelCase            = 0
					# We also need to keep track of the stating epoch so files are named properly
					__lowerCAmelCase            = 0
					__lowerCAmelCase            = evaluate.load("glue"   ,   "mrpc"  )
					__lowerCAmelCase            = num_epochs

					if args.partial_train_epoch is not None:
										__lowerCAmelCase            = args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint  )
										__lowerCAmelCase            = args.resume_from_checkpoint.split("epoch_"  )[1]
										__lowerCAmelCase            = ""
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										__lowerCAmelCase            = int(SCREAMING_SNAKE_CASE_  ) + 1
										__lowerCAmelCase            = evaluation_loop(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
										accelerator.print("resumed checkpoint performance:"   ,   SCREAMING_SNAKE_CASE_  )
										accelerator.print("resumed checkpoint's scheduler's lr:"   ,   lr_scheduler.get_lr()[0]  )
										accelerator.print("resumed optimizers's lr:"   ,   optimizer.param_groups[0]["lr"]  )
										with open(os.path.join(args.output_dir   ,   F"""state_{starting_epoch-1}.json"""  )   ,   "r"  ) as f:
															__lowerCAmelCase            = json.load(SCREAMING_SNAKE_CASE_  )
															assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["lr"] == lr_scheduler.get_lr()[0]
															), "Scheduler learning rate mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
															), "Optimizer learning rate mismatch, loading from checkpoint failed"
															assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
															return

    # Now we train the model
					__lowerCAmelCase            = {}
					for epoch in range(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  ):
										model.train()
										for step, batch in enumerate(SCREAMING_SNAKE_CASE_  ):
															__lowerCAmelCase            = model(**SCREAMING_SNAKE_CASE_  )
															__lowerCAmelCase            = outputs.loss
															__lowerCAmelCase            = loss / gradient_accumulation_steps
															accelerator.backward(SCREAMING_SNAKE_CASE_  )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1
										__lowerCAmelCase            = F"""epoch_{epoch}"""
										__lowerCAmelCase            = os.path.join(args.output_dir   ,   SCREAMING_SNAKE_CASE_  )
										accelerator.save_state(SCREAMING_SNAKE_CASE_  )
										__lowerCAmelCase            = evaluation_loop(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
										__lowerCAmelCase            = accuracy
										__lowerCAmelCase            = lr_scheduler.get_lr()[0]
										__lowerCAmelCase            = optimizer.param_groups[0]["lr"]
										__lowerCAmelCase            = epoch
										__lowerCAmelCase            = overall_step
										accelerator.print(F"""epoch {epoch}:"""   ,   SCREAMING_SNAKE_CASE_  )

										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															with open(os.path.join(args.output_dir   ,   F"""state_{epoch}.json"""  )   ,   "w"  ) as f:
																				json.dump(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )







def 			_a   (		):
					__lowerCAmelCase            = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."  )
					parser.add_argument(
					    "--model_name_or_path"   ,   type=SCREAMING_SNAKE_CASE_   ,   default="bert-base-cased"   ,   help="Path to pretrained model or model identifier from huggingface.co/models."   ,   required=SCREAMING_SNAKE_CASE_   ,   )
					parser.add_argument(
					    "--output_dir"   ,   type=SCREAMING_SNAKE_CASE_   ,   default="."   ,   help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."   ,   )
					parser.add_argument(
					    "--resume_from_checkpoint"   ,   type=SCREAMING_SNAKE_CASE_   ,   default=SCREAMING_SNAKE_CASE_   ,   help="If the training should continue from a checkpoint folder."   ,   )
					parser.add_argument(
					    "--partial_train_epoch"   ,   type=SCREAMING_SNAKE_CASE_   ,   default=SCREAMING_SNAKE_CASE_   ,   help="If passed, the training will stop after this number of epochs."   ,   )
					parser.add_argument(
					    "--num_epochs"   ,   type=SCREAMING_SNAKE_CASE_   ,   default=2   ,   help="Number of train epochs."   ,   )
					__lowerCAmelCase            = parser.parse_args()
					__lowerCAmelCase            = {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

					training_function(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
					main()










from math import ceil, sqrt







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int = 1_00_00_00  ):
					__lowerCAmelCase            = 0

					for outer_width in range(3   ,   (limit // 4) + 2  ):
										if outer_width**2 > limit:
															__lowerCAmelCase            = max(ceil(sqrt(outer_width**2 - limit  )  )   ,   1  )
										else:
															__lowerCAmelCase            = 1
										if (outer_width - hole_width_lower_bound) % 2:
															hole_width_lower_bound += 1

										answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

					return answer


if __name__ == "__main__":
					print(f'''{solution() = }''')




