"""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     = 16
__A     = 32


def       lowercase_      (      _lowerCamelCase:		Accelerator  ,      _lowerCamelCase:		int = 16  ,      _lowerCamelCase:		str = "bert-base-cased"					)       ->					Optional[int]:



							'''simple docstring'''

							__lowerCamelCase    :						List[Any]						      =   AutoTokenizer.from_pretrained(A__					)
							__lowerCamelCase    :						Tuple						      =   load_dataset("glue"  ,      "mrpc"					)

							def tokenize_function(_lowerCamelCase:		Optional[int]					):
														# max_length=None => use the model max length (it's actually the default)
														__lowerCamelCase    :						Optional[Any]						      =   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
							__lowerCamelCase    :						str						      =   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
							__lowerCamelCase    :						Optional[Any]						      =   tokenized_datasets.rename_column("label"  ,      "labels"					)

							def collate_fn(_lowerCamelCase:		List[str]					):
														# 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=128  ,      return_tensors="pt"					)
														return tokenizer.pad(A__  ,      padding="longest"  ,      return_tensors="pt"					)

							# Instantiate dataloaders.
							__lowerCamelCase    :						int						      =   DataLoader(
							    tokenized_datasets["train"]  ,      shuffle=A__  ,      collate_fn=A__  ,      batch_size=A__					)
							__lowerCamelCase    :						int						      =   DataLoader(
							    tokenized_datasets["validation"]  ,      shuffle=A__  ,      collate_fn=A__  ,      batch_size=A__					)

							return train_dataloader, eval_dataloader


def       lowercase_      (      _lowerCamelCase:		Optional[Any]  ,      _lowerCamelCase:		Optional[int]  ,      _lowerCamelCase:		List[str]  ,      _lowerCamelCase:		Dict					)       ->					Dict:



							'''simple docstring'''

							model.eval()
							__lowerCamelCase    :						Union[str, Any]						      =   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():
																					__lowerCamelCase    :						int						      =   model(**A__					)
														__lowerCamelCase    :						List[str]						      =   outputs.logits.argmax(dim=-1					)
														# It is slightly faster to call this once, than multiple times
														__lowerCamelCase      ,     __lowerCamelCase    :						int						      =   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:
																												__lowerCamelCase    :						Any						      =   predictions[: len(eval_dataloader.dataset					) - samples_seen]
																												__lowerCamelCase    :						List[Any]						      =   references[: len(eval_dataloader.dataset					) - samples_seen]
																					else:
																												samples_seen += references.shape[0]
														metric.add_batch(
														    predictions=A__  ,      references=A__  ,      )

							__lowerCamelCase    :						Optional[Any]						      =   metric.compute()
							return eval_metric["accuracy"]


def       lowercase_      (      _lowerCamelCase:		str  ,      _lowerCamelCase:		Optional[Any]					)       ->					Optional[Any]:



							'''simple docstring'''

							__lowerCamelCase    :						Optional[int]						      =   Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__lowerCamelCase    :						Tuple						      =   config["lr"]
							__lowerCamelCase    :						Optional[Any]						      =   int(config["num_epochs"]					)
							__lowerCamelCase    :						Any						      =   int(config["seed"]					)
							__lowerCamelCase    :						Optional[int]						      =   int(config["batch_size"]					)
							__lowerCamelCase    :						Union[str, Any]						      =   args.model_name_or_path

							set_seed(A__					)
							__lowerCamelCase      ,     __lowerCamelCase    :						Optional[Any]						      =   get_dataloaders(A__  ,      A__  ,      A__					)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__lowerCamelCase    :						Tuple						      =   AutoModelForSequenceClassification.from_pretrained(A__  ,      return_dict=A__					)

							# Instantiate optimizer
							__lowerCamelCase    :						Optional[Any]						      =   (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							__lowerCamelCase    :						Union[str, Any]						      =   optimizer_cls(params=model.parameters()  ,      lr=A__					)

							if accelerator.state.deepspeed_plugin is not None:
														__lowerCamelCase    :						Optional[int]						      =   accelerator.state.deepspeed_plugin.deepspeed_config[
														    "gradient_accumulation_steps"
														]
							else:
														__lowerCamelCase    :						Optional[Any]						      =   1
							__lowerCamelCase    :						Optional[Any]						      =   (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
							):
														__lowerCamelCase    :						List[str]						      =   get_linear_schedule_with_warmup(
														    optimizer=A__  ,      num_warmup_steps=0  ,      num_training_steps=A__  ,      )
							else:
														__lowerCamelCase    :						Optional[Any]						      =   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.
							__lowerCamelCase      ,     __lowerCamelCase      ,     __lowerCamelCase      ,     __lowerCamelCase      ,     __lowerCamelCase    :						Optional[int]						      =   accelerator.prepare(
							    A__  ,      A__  ,      A__  ,      A__  ,      A__					)

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

							if args.partial_train_epoch is not None:
														__lowerCamelCase    :						str						      =   args.partial_train_epoch

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

																					overall_step += 1
														__lowerCamelCase    :						Tuple						      =   F"""epoch_{epoch}"""
														__lowerCamelCase    :						List[Any]						      =   os.path.join(args.output_dir  ,      A__					)
														accelerator.save_state(A__					)
														__lowerCamelCase    :						Any						      =   evaluation_loop(A__  ,      A__  ,      A__  ,      A__					)
														__lowerCamelCase    :						Tuple						      =   accuracy
														__lowerCamelCase    :						List[str]						      =   lr_scheduler.get_lr()[0]
														__lowerCamelCase    :						Dict						      =   optimizer.param_groups[0]["lr"]
														__lowerCamelCase    :						Any						      =   epoch
														__lowerCamelCase    :						str						      =   overall_step
														accelerator.print(F"""epoch {epoch}:"""  ,      A__					)

														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(A__  ,      A__					)


def       lowercase_      (      )       ->					Tuple:



							'''simple docstring'''

							__lowerCamelCase    :						Optional[Any]						      =   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(
							    "--resume_from_checkpoint"  ,      type=A__  ,      default=A__  ,      help="If the training should continue from a checkpoint folder."  ,      )
							parser.add_argument(
							    "--partial_train_epoch"  ,      type=A__  ,      default=A__  ,      help="If passed, the training will stop after this number of epochs."  ,      )
							parser.add_argument(
							    "--num_epochs"  ,      type=A__  ,      default=2  ,      help="Number of train epochs."  ,      )
							__lowerCamelCase    :						Dict						      =   parser.parse_args()
							__lowerCamelCase    :						List[str]						      =   {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

							training_function(A__  ,      A__					)


if __name__ == "__main__":
				main()


import os
def      a      (			)    ->      Any:





      """simple docstring"""



      with open(os.path.dirname(A__     ) + '/p022_names.txt'     ) as file:
            _lowercase	       =str(file.readlines()[0]     )
            _lowercase	       =names.replace('"'      ,   ''     ).split(','     )

      names.sort()

      _lowercase	       =0
      _lowercase	       =0

      for i, name in enumerate(A__     ):
            for letter in name:
                  name_score += ord(A__     ) - 64

            total_score += (i + 1) * name_score
            _lowercase	       =0
      return total_score


if __name__ == "__main__":
  print(solution())


