"""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


_A   					=    1_6
_A   					=    3_2




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase = 16  ,					__UpperCAmelCase = "bert-base-cased"       )							->   int:
				SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				load_dataset("glue"  ,					"mrpc"       )

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

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

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

				def collate_fn(__UpperCAmelCase       ):
								# 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(__UpperCAmelCase  ,					padding="max_length"  ,					max_length=128  ,					return_tensors="pt"       )
								return tokenizer.pad(__UpperCAmelCase  ,					padding="longest"  ,					return_tensors="pt"       )

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

				return train_dataloader, eval_dataloader




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

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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Dict:
				# Initialize accelerator
				SCREAMING_SNAKE_CASE__		  =				Accelerator()

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

				set_seed(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				get_dataloaders(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )

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

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

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

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

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

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

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint       )
								SCREAMING_SNAKE_CASE__		  =				args.resume_from_checkpoint.split("epoch_"       )[1]
								SCREAMING_SNAKE_CASE__		  =				""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								SCREAMING_SNAKE_CASE__		  =				int(__UpperCAmelCase       ) + 1
								SCREAMING_SNAKE_CASE__		  =				evaluation_loop(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )
								accelerator.print("resumed checkpoint performance:"  ,					__UpperCAmelCase       )
								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:
												SCREAMING_SNAKE_CASE__		  =				json.load(__UpperCAmelCase       )
												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
				SCREAMING_SNAKE_CASE__		  =				{}
				for epoch in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
								model.train()
								for step, batch in enumerate(__UpperCAmelCase       ):
												SCREAMING_SNAKE_CASE__		  =				model(**__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__		  =				outputs.loss
												SCREAMING_SNAKE_CASE__		  =				loss / gradient_accumulation_steps
												accelerator.backward(__UpperCAmelCase       )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

								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(__UpperCAmelCase  ,					__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  )							->   str:
				SCREAMING_SNAKE_CASE__		  =				argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."       )
				parser.add_argument(
				    "--model_name_or_path"  ,					type=__UpperCAmelCase  ,					default="bert-base-cased"  ,					help="Path to pretrained model or model identifier from huggingface.co/models."  ,					required=__UpperCAmelCase  ,					)
				parser.add_argument(
				    "--output_dir"  ,					type=__UpperCAmelCase  ,					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=__UpperCAmelCase  ,					default=__UpperCAmelCase  ,					help="If the training should continue from a checkpoint folder."  ,					)
				parser.add_argument(
				    "--partial_train_epoch"  ,					type=__UpperCAmelCase  ,					default=__UpperCAmelCase  ,					help="If passed, the training will stop after this number of epochs."  ,					)
				parser.add_argument(
				    "--num_epochs"  ,					type=__UpperCAmelCase  ,					default=2  ,					help="Number of train epochs."  ,					)
				SCREAMING_SNAKE_CASE__		  =				parser.parse_args()
				SCREAMING_SNAKE_CASE__		  =				{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

				training_function(__UpperCAmelCase  ,					__UpperCAmelCase       )


if __name__ == "__main__":
						main()





"""simple docstring"""






import sys




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]

				for chain_length in range(2  ,					__UpperCAmelCase       ):
								for a in range(1  ,					n - chain_length + 1       ):
												SCREAMING_SNAKE_CASE__		  =				a + chain_length - 1

												SCREAMING_SNAKE_CASE__		  =				sys.maxsize
												for c in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				(
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				SCREAMING_SNAKE_CASE__		  =				cost
																				SCREAMING_SNAKE_CASE__		  =				c
				return matrix, sol




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if i == j:
								print("A" + str(__UpperCAmelCase       )  ,					end=" "       )
				else:
								print("("  ,					end=" "       )
								print_optiomal_solution(__UpperCAmelCase  ,					__UpperCAmelCase  ,					optimal_solution[i][j]       )
								print_optiomal_solution(__UpperCAmelCase  ,					optimal_solution[i][j] + 1  ,					__UpperCAmelCase       )
								print(")"  ,					end=" "       )




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				[30, 35, 15, 5, 10, 20, 25]
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				matrix_chain_order(__UpperCAmelCase       )

				print("No. of Operation required: " + str(matrix[1][n - 1]       )       )
				print_optiomal_solution(__UpperCAmelCase  ,					1  ,					n - 1       )


if __name__ == "__main__":
						main()




