





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


A   : Dict			=				1_6
A   : Union[str, Any]			=				3_2







def        __lowerCamelCase (							__a       :Dict   )		->    Optional[Any]:






		"""simple docstring"""

		return int(x / 2**2_0   )


class   A      :
			'''simple docstring'''




			def __enter__(      self       :   Union[str, Any]       )     ->     Optional[Any]:







					"""simple docstring"""






					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					A__  				=       torch.cuda.memory_allocated()
					return self







			def __exit__(      self       :   Dict							,     *__lowerCAmelCase       :   str       )     ->     Optional[Any]:







					"""simple docstring"""






					gc.collect()
					torch.cuda.empty_cache()
					A__  				=       torch.cuda.memory_allocated()
					A__  				=       torch.cuda.max_memory_allocated()
					A__  				=       bamb(self.end - self.begin       )
					A__  				=       bamb(self.peak - self.begin       )
					# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def        __lowerCamelCase (							__a       :Accelerator   ,					__a       :int = 1_6   ,					__a       :str = "bert-base-cased"   ,					__a       :int = 3_2_0   ,					__a       :int = 1_6_0   ,					)		->    List[str]:






		"""simple docstring"""

		A__  				=       AutoTokenizer.from_pretrained(__a   )
		A__  				=       load_dataset(
		    """glue"""   ,					"""mrpc"""   ,					split={"""train""": F'train[:{n_train}]', """validation""": F'validation[:{n_val}]'}   )

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

		def collate_fn(__a       :Optional[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(__a   ,					padding="""max_length"""   ,					max_length=1_2_8   ,					return_tensors="""pt"""   )
				return tokenizer.pad(__a   ,					padding="""longest"""   ,					return_tensors="""pt"""   )

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

		return train_dataloader, eval_dataloader







def        __lowerCamelCase (							__a       :str   ,					__a       :List[str]   )		->    int:






		"""simple docstring"""

		A__  				=       Accelerator()

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

		set_seed(__a   )
		A__				,      A__  				=       get_dataloaders(__a   ,					__a   ,					__a   ,					args.n_train   ,					args.n_val   )

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

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

		if accelerator.state.deepspeed_plugin is not None:
				A__  				=       accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				A__  				=       1
		A__  				=       (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
		):
				A__  				=       get_linear_schedule_with_warmup(
				    optimizer=__a   ,					num_warmup_steps=0   ,					num_training_steps=__a   ,					)
		else:
				A__  				=       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.
		A__				,      A__				,      A__				,      A__				,      A__  				=       accelerator.prepare(
		    __a   ,					__a   ,					__a   ,					__a   ,					__a   )

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

		# Now we train the model
		A__  				=       {}
		for epoch in range(__a   ,					__a   ):
				with TorchTracemalloc() as tracemalloc:
						model.train()
						for step, batch in enumerate(__a   ):
								A__  				=       model(**__a   )
								A__  				=       outputs.loss
								A__  				=       loss / gradient_accumulation_steps
								accelerator.backward(__a   )
								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   )   )   )
				A__  				=       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(__a   ,					__a   )







def        __lowerCamelCase (							)		->    Optional[int]:






		"""simple docstring"""

		A__  				=       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(
		    """--peak_memory_upper_bound"""   ,					type=__a   ,					default=__a   ,					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=__a   ,					default=3_2_0   ,					help="""Number of training examples to use."""   ,					)
		parser.add_argument(
		    """--n_val"""   ,					type=__a   ,					default=1_6_0   ,					help="""Number of validation examples to use."""   ,					)
		parser.add_argument(
		    """--num_epochs"""   ,					type=__a   ,					default=1   ,					help="""Number of train epochs."""   ,					)
		A__  				=       parser.parse_args()
		A__  				=       {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}
		training_function(__a   ,					__a   )


if __name__ == "__main__":
					main()










import math







def        __lowerCamelCase (							)		->    None:






		"""simple docstring"""

		A__  				=       input("""Enter message: """   )
		A__  				=       int(input(F'Enter key [2-{len(__a   ) - 1}]: '   )   )
		A__  				=       input("""Encryption/Decryption [e/d]: """   )

		if mode.lower().startswith("""e"""   ):
				A__  				=       encrypt_message(__a   ,					__a   )
		elif mode.lower().startswith("""d"""   ):
				A__  				=       decrypt_message(__a   ,					__a   )

		# Append pipe symbol (vertical bar) to identify spaces at the end.
		print(F'Output:\n{text + "|"}'   )







def        __lowerCamelCase (							__a       :int   ,					__a       :str   )		->    str:






		"""simple docstring"""

		A__  				=       [""""""] * key
		for col in range(__a   ):
				A__  				=       col
				while pointer < len(__a   ):
						cipher_text[col] += message[pointer]
						pointer += key
		return "".join(__a   )







def        __lowerCamelCase (							__a       :int   ,					__a       :str   )		->    str:






		"""simple docstring"""

		A__  				=       math.ceil(len(__a   ) / key   )
		A__  				=       key
		A__  				=       (num_cols * num_rows) - len(__a   )
		A__  				=       [""""""] * num_cols
		A__  				=       0
		A__  				=       0

		for symbol in message:
				plain_text[col] += symbol
				col += 1

				if (
				    (col == num_cols)
				    or (col == num_cols - 1)
				    and (row >= num_rows - num_shaded_boxes)
				):
						A__  				=       0
						row += 1

		return "".join(__a   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()



