




import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCamelCase__            =     16
UpperCamelCase__            =     32







def       _a (		SCREAMING_SNAKE_CASE_				:							Accelerator , SCREAMING_SNAKE_CASE_				:							int = 16	):
					__lowerCAmelCase							  =			AutoTokenizer.from_pretrained("bert-base-cased"	)
					__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=UpperCamelCase__ , max_length=UpperCamelCase__	)
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										__lowerCAmelCase							  =			datasets.map(
										    UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=["idx", "sentence1", "sentence2"] , )

					# 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_				:							str	):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										__lowerCAmelCase							  =			1_28 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															__lowerCAmelCase							  =			16
										elif accelerator.mixed_precision != "no":
															__lowerCAmelCase							  =			8
										else:
															__lowerCAmelCase							  =			None

										return tokenizer.pad(
										    UpperCamelCase__ , padding="longest" , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors="pt" , )

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

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					UpperCamelCase__            =     mocked_dataloaders  # noqa: F811







def       _a (		SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							int	):
					# For testing only
					if os.environ.get("TESTING_MOCKED_DATALOADERS" , UpperCamelCase__	) == "1":
										__lowerCAmelCase							  =			2
					# Initialize accelerator
					__lowerCAmelCase							  =			Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision	)
					# 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"]	)

					# New Code #
					# Parse out whether we are saving every epoch or after a certain number of batches
					if hasattr(args.checkpointing_steps , "isdigit"	):
										if args.checkpointing_steps == "epoch":
															__lowerCAmelCase							  =			args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															__lowerCAmelCase							  =			int(args.checkpointing_steps	)
										else:
															raise ValueError(
															    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
					else:
										__lowerCAmelCase							  =			None

					set_seed(UpperCamelCase__	)

					__lowerCAmelCase							  =			get_dataloaders(UpperCamelCase__ , UpperCamelCase__	)
					__lowerCAmelCase							  =			evaluate.load("glue" , "mrpc"	)

					# If the batch size is too big we use gradient accumulation
					__lowerCAmelCase							  =			1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										__lowerCAmelCase							  =			batch_size // MAX_GPU_BATCH_SIZE
										__lowerCAmelCase							  =			MAX_GPU_BATCH_SIZE

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__lowerCAmelCase							  =			AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=UpperCamelCase__	)

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					__lowerCAmelCase							  =			model.to(accelerator.device	)

					# Instantiate optimizer
					__lowerCAmelCase							  =			AdamW(params=model.parameters() , lr=UpperCamelCase__	)

					# Instantiate scheduler
					__lowerCAmelCase							  =			get_linear_schedule_with_warmup(
					    optimizer=UpperCamelCase__ , num_warmup_steps=1_00 , num_training_steps=(len(UpperCamelCase__	) * num_epochs) // gradient_accumulation_steps , )

					# 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							  =			accelerator.prepare(
					    UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__	)

					# New Code #
					# 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

					# We need to load the checkpoint back in before training here with `load_state`
					# The total number of epochs is adjusted based on where the state is being loaded from,
					# as we assume continuation of the same training script
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""	)
															accelerator.load_state(args.resume_from_checkpoint	)
															__lowerCAmelCase							  =			os.path.basename(args.resume_from_checkpoint	)
										else:
															# Get the most recent checkpoint
															__lowerCAmelCase							  =			[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
															dirs.sort(key=os.path.getctime	)
															__lowerCAmelCase							  =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										__lowerCAmelCase							  =			os.path.splitext(UpperCamelCase__	)[0]

										if "epoch" in training_difference:
															__lowerCAmelCase							  =			int(training_difference.replace("epoch_" , ""	)	) + 1
															__lowerCAmelCase							  =			None
										else:
															__lowerCAmelCase							  =			int(training_difference.replace("step_" , ""	)	)
															__lowerCAmelCase							  =			resume_step // len(UpperCamelCase__	)
															resume_step -= starting_epoch * len(UpperCamelCase__	)

    # Now we train the model
					for epoch in range(UpperCamelCase__ , UpperCamelCase__	):
										model.train()
										# New Code #
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															__lowerCAmelCase							  =			accelerator.skip_first_batches(UpperCamelCase__ , UpperCamelCase__	)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															__lowerCAmelCase							  =			train_dataloader
										for step, batch in enumerate(UpperCamelCase__	):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device	)
															__lowerCAmelCase							  =			model(**UpperCamelCase__	)
															__lowerCAmelCase							  =			outputs.loss
															__lowerCAmelCase							  =			loss / gradient_accumulation_steps
															accelerator.backward(UpperCamelCase__	)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
															# New Code #
															overall_step += 1

															# New Code #
															# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
															# These are saved to folders named `step_{overall_step}`
															# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
															# If mixed precision was used, will also save a "scalar.bin" file
															if isinstance(UpperCamelCase__ , UpperCamelCase__	):
																				__lowerCAmelCase							  =			F"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														__lowerCAmelCase							  =			os.path.join(args.output_dir , UpperCamelCase__	)
																									accelerator.save_state(UpperCamelCase__	)

										model.eval()
										for step, batch in enumerate(UpperCamelCase__	):
															# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
															batch.to(accelerator.device	)
															with torch.no_grad():
																				__lowerCAmelCase							  =			model(**UpperCamelCase__	)
															__lowerCAmelCase							  =			outputs.logits.argmax(dim=-1	)
															__lowerCAmelCase							  =			accelerator.gather_for_metrics((predictions, batch["labels"])	)
															metric.add_batch(
															    predictions=UpperCamelCase__ , references=UpperCamelCase__ , )

										__lowerCAmelCase							  =			metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}:""" , UpperCamelCase__	)

										# New Code #
										# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
										# These are saved to folders named `epoch_{epoch}`
										# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
										# If mixed precision was used, will also save a "scalar.bin" file
										if checkpointing_steps == "epoch":
															__lowerCAmelCase							  =			F"""epoch_{epoch}"""
															if args.output_dir is not None:
																				__lowerCAmelCase							  =			os.path.join(args.output_dir , UpperCamelCase__	)
															accelerator.save_state(UpperCamelCase__	)







def       _a (		):
					__lowerCAmelCase							  =			argparse.ArgumentParser(description="Simple example of training script."	)
					parser.add_argument(
					    "--mixed_precision" , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
					    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
					    "and an Nvidia Ampere GPU." , )
					parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU."	)
					parser.add_argument(
					    "--checkpointing_steps" , type=UpperCamelCase__ , default=UpperCamelCase__ , help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch." , )
					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." , )
					__lowerCAmelCase							  =			parser.parse_args()
					__lowerCAmelCase							  =			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(UpperCamelCase__ , UpperCamelCase__	)


if __name__ == "__main__":
					main()











'''simple docstring'''






def 				_lowerCamelCase(	UpperCamelCase__     :			int = 1_000		)		->	int:
    A							:							Optional[Any]				=    [0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8]
    # number of letters in twenty, thirty, ..., ninety (0 for numbers less than
    # 20 due to inconsistency in teens)
    A							:							Tuple				=    [0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

    A							:							str				=    0

    for i in range(1     ,							n + 1		):
        if i < 1_000:
            if i >= 100:
                # add number of letters for "n hundred"
                count += ones_counts[i // 100] + 7

                if i % 100 != 0:
                    # add number of letters for "and" if number is not multiple
                    # of 100
                    count += 3

            if 0 < i % 100 < 20:
                # add number of letters for one, two, three, ..., nineteen
                # (could be combined with below if not for inconsistency in
                # teens)
                count += ones_counts[i % 100]
            else:
                # add number of letters for twenty, twenty one, ..., ninety
                # nine
                count += ones_counts[i % 10]
                count += tens_counts[(i % 100 - i % 10) // 10]
        else:
            count += ones_counts[i // 1_000] + 8
    return count


if __name__ == "__main__":
     print(solution(int(input().strip())))
