



import argparse
import gc
import json
import os

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


_lowercase     							=						16
_lowercase     							=						32






def      _A				(UpperCamelCase		:      Union[str, Any] )  ->Optional[int]:



			'''simple docstring'''







			return int(x / 2**20 )
class 				__A					:





			def __enter__(self				):
						gc.collect()
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
						lowerCamelCase__ :      Dict								=		torch.cuda.memory_allocated()
						return self





			def __exit__(self							,    *__magic_name__				):
						gc.collect()
						torch.cuda.empty_cache()
						lowerCamelCase__ :      List[Any]								=		torch.cuda.memory_allocated()
						lowerCamelCase__ :      Any								=		torch.cuda.max_memory_allocated()
						lowerCamelCase__ :      Optional[int]								=		bamb(self.end - self.begin				)
						lowerCamelCase__ :      List[str]								=		bamb(self.peak - self.begin				)
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def      _A				(UpperCamelCase		:      Accelerator      ,			UpperCamelCase		:      int = 16      ,			UpperCamelCase		:      str = "bert-base-cased"      ,			UpperCamelCase		:      int = 320      ,			UpperCamelCase		:      int = 160      ,			)  ->Union[str, Any]:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		AutoTokenizer.from_pretrained(UpperCamelCase )
			lowerCamelCase__ :      List[Any]								=		load_dataset(
			    """glue"""      ,			"""mrpc"""      ,			split={"""train""": f"train[:{n_train}]", """validation""": f"validation[:{n_val}]"} )

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

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

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

			return train_dataloader, eval_dataloader






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      int )  ->int:



			'''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__ :      Tuple								=		int(config["""seed"""] )
			lowerCamelCase__ :      Optional[int]								=		int(config["""batch_size"""] )
			lowerCamelCase__ :      List[Any]								=		args.model_name_or_path

			set_seed(UpperCamelCase )
			lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		get_dataloaders(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			args.n_train      ,			args.n_val )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCamelCase__ :      List[Any]								=		AutoModelForSequenceClassification.from_pretrained(UpperCamelCase      ,			return_dict=UpperCamelCase )

			# Instantiate optimizer
			lowerCamelCase__ :      List[str]								=		(
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			lowerCamelCase__ :      Tuple								=		optimizer_cls(params=model.parameters()      ,			lr=UpperCamelCase )

			if accelerator.state.deepspeed_plugin is not None:
						lowerCamelCase__ :      str								=		accelerator.state.deepspeed_plugin.deepspeed_config[
						    """gradient_accumulation_steps"""
						]
			else:
						lowerCamelCase__ :      Dict								=		1
			lowerCamelCase__ :      Union[str, Any]								=		(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
			):
						lowerCamelCase__ :      Tuple								=		get_linear_schedule_with_warmup(
						    optimizer=UpperCamelCase      ,			num_warmup_steps=0      ,			num_training_steps=UpperCamelCase      ,			)
			else:
						lowerCamelCase__ :      Dict								=		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.
			lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Any								=		accelerator.prepare(
			    UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

			# We need to keep track of how many total steps we have iterated over
			lowerCamelCase__ :      Any								=		0
			# We also need to keep track of the stating epoch so files are named properly
			lowerCamelCase__ :      str								=		0

			# Now we train the model
			lowerCamelCase__ :      Optional[int]								=		{}
			for epoch in range(UpperCamelCase      ,			UpperCamelCase ):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(UpperCamelCase ):
												lowerCamelCase__ :      Optional[Any]								=		model(**UpperCamelCase )
												lowerCamelCase__ :      Tuple								=		outputs.loss
												lowerCamelCase__ :      Union[str, Any]								=		loss / gradient_accumulation_steps
												accelerator.backward(UpperCamelCase )
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin ) ) )
						accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used ) )
						accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked ) )
						accelerator.print(
						    """Total Peak Memory consumed during the train (max): {}""".format(
						        tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
						lowerCamelCase__ :      Any								=		tracemalloc.peaked + bamb(tracemalloc.begin )
						if args.peak_memory_upper_bound is not None:
									assert (
									    train_total_peak_memory[f"epoch-{epoch}"] <= args.peak_memory_upper_bound
									), "Peak memory usage exceeded the upper bound"

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir      ,			"""peak_memory_utilization.json""" )      ,			"""w""" ) as f:
									json.dump(UpperCamelCase      ,			UpperCamelCase )






def      _A				()  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		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(
			    """--peak_memory_upper_bound"""      ,			type=UpperCamelCase      ,			default=UpperCamelCase      ,			help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""      ,			)
			parser.add_argument(
			    """--n_train"""      ,			type=UpperCamelCase      ,			default=320      ,			help="""Number of training examples to use."""      ,			)
			parser.add_argument(
			    """--n_val"""      ,			type=UpperCamelCase      ,			default=160      ,			help="""Number of validation examples to use."""      ,			)
			parser.add_argument(
			    """--num_epochs"""      ,			type=UpperCamelCase      ,			default=1      ,			help="""Number of train epochs."""      ,			)
			lowerCamelCase__ :      Optional[Any]								=		parser.parse_args()
			lowerCamelCase__ :      Optional[Any]								=		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
			training_function(UpperCamelCase      ,			UpperCamelCase )


if __name__ == "__main__":
				main()








import itertools
import string
from collections.abc import Generator, Iterable






def      _A				(UpperCamelCase		:      Iterable[str]      ,			UpperCamelCase		:      int )  ->Generator[tuple[str, ...], None, None]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		iter(UpperCamelCase )
			while True:
						lowerCamelCase__ :      Union[str, Any]								=		tuple(itertools.islice(UpperCamelCase      ,			UpperCamelCase ) )
						if not chunk:
									return
						yield chunk






def      _A				(UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		"""""".join([c.upper() for c in dirty if c in string.ascii_letters] )
			lowerCamelCase__ :      Any								=		""""""

			if len(UpperCamelCase ) < 2:
						return dirty

			for i in range(len(UpperCamelCase ) - 1 ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(UpperCamelCase ) & 1:
						clean += "X"

			return clean






def      _A				(UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCamelCase__ :      List[Any]								=		[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(UpperCamelCase )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(UpperCamelCase )

			return table






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      int								=		prepare_input(UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      Any								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      int								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Any								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



