
import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


lowercase__						:Optional[int]					    =       16
lowercase__						:str					    =       32



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					return int(x / 2**20						)







class 							lowercase						:




							def __enter__(							self):
												gc.collect()
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
												lowercase		      =		torch.cuda.memory_allocated()
												lowercase		      =		psutil.Process()

												lowercase		      =		self.cpu_mem_used()
												lowercase		      =		True
												lowercase		      =		threading.Thread(target=self.peak_monitor_func)
												lowercase		      =		True
												peak_monitor_thread.start()
												return self




							def 					A__ (							self):
												return self.process.memory_info().rss




							def 					A__ (							self):
												lowercase		      =		-1

												while True:
																	lowercase		      =		max(self.cpu_mem_used()  ,self.cpu_peak)

																	# can't sleep or will not catch the peak right (this comment is here on purpose)
																	# time.sleep(0.001) # 1msec

																	if not self.peak_monitoring:
																						break





							def __exit__(							self  ,*A__):
												lowercase		      =		False

												gc.collect()
												torch.cuda.empty_cache()
												lowercase		      =		torch.cuda.memory_allocated()
												lowercase		      =		torch.cuda.max_memory_allocated()
												lowercase		      =		bamb(self.end - self.begin)
												lowercase		      =		bamb(self.peak - self.begin)

												lowercase		      =		self.cpu_mem_used()
												lowercase		      =		bamb(self.cpu_end - self.cpu_begin)
												lowercase		      =		bamb(self.cpu_peak - self.cpu_begin)
												# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

							lowercase__						:Tuple					    =       mocked_dataloaders  # noqa: F811



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					# For testing only
					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''					,     lowerCAmelCase__						) == "1":
										lowercase		      =		2

					# New Code #
					# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
					lowercase		      =		FullyShardedDataParallelPlugin(
					    state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCAmelCase__					,     ranka_only=lowerCAmelCase__						)					,     optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCAmelCase__					,     ranka_only=lowerCAmelCase__						)					,     )

					# Initialize accelerator
					if args.with_tracking:
										lowercase		      =		Accelerator(
										    cpu=args.cpu					,     mixed_precision=args.mixed_precision					,     log_with='''wandb'''					,     project_dir=args.logging_dir					,     fsdp_plugin=lowerCAmelCase__					,     )
					else:
										lowercase		      =		Accelerator(fsdp_plugin=lowerCAmelCase__						)
					accelerator.print(accelerator.distributed_type						)

					if hasattr(args.checkpointing_steps					,     '''isdigit'''						):
										if args.checkpointing_steps == "epoch":
															lowercase		      =		args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															lowercase		      =		int(args.checkpointing_steps						)
										else:
															raise ValueError(
															    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'						)
					else:
										lowercase		      =		None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					lowercase		      =		config['''lr''']
					lowercase		      =		int(config['''num_epochs''']						)
					lowercase		      =		int(config['''seed''']						)
					lowercase		      =		int(config['''batch_size''']						)

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										lowercase		      =		vars(lowerCAmelCase__						)
										accelerator.init_trackers('''fsdp_glue_no_trainer'''					,     lowerCAmelCase__						)

					lowercase		      =		AutoTokenizer.from_pretrained(args.model_name_or_path						)
					lowercase		      =		load_dataset('''glue'''					,     '''mrpc'''						)
					lowercase		      =		evaluate.load('''glue'''					,     '''mrpc'''						)

					def tokenize_function(lowerCAmelCase__						):
										# max_length=None => use the model max length (it's actually the default)
										lowercase		      =		tokenizer(examples['''sentence1''']					,     examples['''sentence2''']					,     truncation=lowerCAmelCase__					,     max_length=lowerCAmelCase__						)
										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():
										lowercase		      =		datasets.map(
										    lowerCAmelCase__					,     batched=lowerCAmelCase__					,     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
					lowercase		      =		tokenized_datasets.rename_column('''label'''					,     '''labels'''						)

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

					def collate_fn(lowerCAmelCase__						):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										lowercase		      =		128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															lowercase		      =		16
										elif accelerator.mixed_precision != "no":
															lowercase		      =		8
										else:
															lowercase		      =		None

										return tokenizer.pad(
										    lowerCAmelCase__					,     padding='''longest'''					,     max_length=lowerCAmelCase__					,     pad_to_multiple_of=lowerCAmelCase__					,     return_tensors='''pt'''					,     )

					# Instantiate dataloaders.
					lowercase		      =		DataLoader(
					    tokenized_datasets['''train''']					,     shuffle=lowerCAmelCase__					,     collate_fn=lowerCAmelCase__					,     batch_size=lowerCAmelCase__						)
					lowercase		      =		DataLoader(
					    tokenized_datasets['''validation''']					,     shuffle=lowerCAmelCase__					,     collate_fn=lowerCAmelCase__					,     batch_size=lowerCAmelCase__						)

					set_seed(lowerCAmelCase__						)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowercase		      =		AutoModelForSequenceClassification.from_pretrained(
					    args.model_name_or_path					,     return_dict=lowerCAmelCase__					,     low_cpu_mem_usage=lowerCAmelCase__						)

					# New Code #
					# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
					lowercase		      =		accelerator.prepare(lowerCAmelCase__						)
					accelerator.print(lowerCAmelCase__						)

					# Instantiate optimizer
					# New Code #
					# For FSDP feature, at present it doesn't support multiple parameter groups,
					# so we need to create a single parameter group for the whole model
					lowercase		      =		torch.optim.AdamW(params=model.parameters()					,     lr=lowerCAmelCase__					,     weight_decay=2E-4						)

					# Instantiate scheduler
					lowercase		      =		get_linear_schedule_with_warmup(
					    optimizer=lowerCAmelCase__					,     num_warmup_steps=10					,     num_training_steps=(len(lowerCAmelCase__						) * num_epochs) // gradient_accumulation_steps					,     )

					# New Code #
					# For FSDP feature, prepare everything except the model as we have already prepared the model
					# before creating the optimizer
					# 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.
					lowercase      ,		lowercase      ,		lowercase      ,		lowercase		      =		accelerator.prepare(
					    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__						)

					lowercase		      =		0

					# Potentially load in the weights and states from a previous save
					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						)
															lowercase		      =		os.path.basename(args.resume_from_checkpoint						)
										else:
															# Get the most recent checkpoint
															lowercase		      =		[f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
															dirs.sort(key=os.path.getctime						)
															lowercase		      =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										lowercase		      =		os.path.splitext(lowerCAmelCase__						)[0]

										if "epoch" in training_difference:
															num_epochs -= int(training_difference.replace('''epoch_'''					,     ''''''						)						)
															lowercase		      =		None
										else:
															lowercase		      =		int(training_difference.replace('''step_'''					,     ''''''						)						)
															num_epochs -= resume_step // len(lowerCAmelCase__						)
															# If resuming by step, we also need to know exactly how far into the DataLoader we went
															lowercase		      =		(num_epochs * len(lowerCAmelCase__						)) - resume_step

    # Now we train the model
					for epoch in range(lowerCAmelCase__						):
										# New Code #
										# context manager to track the peak memory usage during the training epoch
										with TorchTracemalloc() as tracemalloc:
															model.train()
															if args.with_tracking:
																				lowercase		      =		0
															for step, batch in enumerate(lowerCAmelCase__						):
																				# We need to skip steps until we reach the resumed step
																				if args.resume_from_checkpoint and epoch == 0:
																									if resume_step is not None and step < resume_step:
																														pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device						)
																				lowercase		      =		model(**lowerCAmelCase__						)
																				lowercase		      =		outputs.loss
																				lowercase		      =		loss / gradient_accumulation_steps
																				# We keep track of the loss at each epoch
																				if args.with_tracking:
																									total_loss += loss.detach().float()
																				accelerator.backward(lowerCAmelCase__						)
																				if step % gradient_accumulation_steps == 0:
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# accelerator.print(lr_scheduler.get_lr())

																				overall_step += 1

																				if isinstance(lowerCAmelCase__					,     lowerCAmelCase__						):
																									lowercase		      =		f'step_{overall_step}'
																									if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																			lowercase		      =		os.path.join(args.output_dir					,     lowerCAmelCase__						)
																														accelerator.save_state(lowerCAmelCase__						)
        # New Code #
        # 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						)						)						)
										# Logging the peak memory usage of the GPU to the tracker
										if args.with_tracking:
															accelerator.log(
															    {
															        '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin						),
															    }					,     step=lowerCAmelCase__					,     )

										# New Code #
										# context manager to track the peak memory usage during the evaluation
										with TorchTracemalloc() as tracemalloc:
															model.eval()
															for step, batch in enumerate(lowerCAmelCase__						):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device						)
																				with torch.no_grad():
																									lowercase		      =		model(**lowerCAmelCase__						)
																				lowercase		      =		outputs.logits.argmax(dim=-1						)
																				lowercase      ,		lowercase		      =		accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
																				metric.add_batch(
																				    predictions=lowerCAmelCase__					,     references=lowerCAmelCase__					,     )

															lowercase		      =		metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(f'epoch {epoch}:'					,     lowerCAmelCase__						)
															if args.with_tracking:
																				accelerator.log(
																				    {
																				        '''accuracy''': eval_metric['''accuracy'''],
																				        '''f1''': eval_metric['''f1'''],
																				        '''train_loss''': total_loss.item() / len(lowerCAmelCase__						),
																				    }					,     step=lowerCAmelCase__					,     )

															if checkpointing_steps == "epoch":
																				lowercase		      =		f'epoch_{epoch}'
																				if args.output_dir is not None:
																									lowercase		      =		os.path.join(args.output_dir					,     lowerCAmelCase__						)
																				accelerator.save_state(lowerCAmelCase__						)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
										accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin						)						)						)
										accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used						)						)
										accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked						)						)
										accelerator.print(
										    '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin						)						)						)
										# Logging the peak memory usage of the GPU to the tracker
										if args.with_tracking:
															accelerator.log(
															    {
															        '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin						),
															    }					,     step=lowerCAmelCase__					,     )

					if args.with_tracking:
										accelerator.end_training()



def 			UpperCamelCase       (				):
					'''simple docstring'''
					lowercase		      =		argparse.ArgumentParser(description='''Simple example of training script.'''						)
					parser.add_argument(
					    '''--mixed_precision'''					,     type=lowerCAmelCase__					,     default=lowerCAmelCase__					,     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=lowerCAmelCase__					,     default=lowerCAmelCase__					,     help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''					,     )
					parser.add_argument(
					    '''--resume_from_checkpoint'''					,     type=lowerCAmelCase__					,     default=lowerCAmelCase__					,     help='''If the training should continue from a checkpoint folder.'''					,     )
					parser.add_argument(
					    '''--with_tracking'''					,     action='''store_true'''					,     help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''					,     )
					parser.add_argument(
					    '''--output_dir'''					,     type=lowerCAmelCase__					,     default='''.'''					,     help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''					,     )
					parser.add_argument(
					    '''--logging_dir'''					,     type=lowerCAmelCase__					,     default='''logs'''					,     help='''Location on where to store experiment tracking logs`'''					,     )
					parser.add_argument(
					    '''--model_name_or_path'''					,     type=lowerCAmelCase__					,     help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,     required=lowerCAmelCase__					,     )
					lowercase		      =		parser.parse_args()
					lowercase		      =		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(lowerCAmelCase__					,     lowerCAmelCase__						)


if __name__ == "__main__":
							main()






from __future__ import annotations

import random
import string







class 							lowercase						:




							def __init__(							self  ,A__ = None):
												lowercase		      =		passcode or self.__passcode_creator()
												lowercase		      =		self.__make_key_list()
												lowercase		      =		self.__make_shift_key()




							def __str__(							self):
												return "".join(self.__passcode)




							def 					A__ (							self  ,A__):
												for i in range(1  ,len(A__)  ,2):
																	iterlist[i] *= -1
												return iterlist




							def 					A__ (							self):
												lowercase		      =		string.ascii_letters + string.digits
												lowercase		      =		[random.choice(A__) for _ in range(random.randint(1_0  ,2_0))]
												return password




							def 					A__ (							self):
												lowercase		      =		(
												    string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
												)

												lowercase		      =		[]

												# creates points known as breakpoints to break the key_list_options at those
												# points and pivot each substring
												lowercase		      =		sorted(set(self.__passcode))
												lowercase		      =		[]

												# algorithm for creating a new shuffled list, keys_l, out of key_list_options
												for i in key_list_options:
																	temp_list.extend(A__)

																	# checking breakpoints at which to pivot temporary sublist and add it into
																	# keys_l
																	if i in breakpoints or i == key_list_options[-1]:
																						keys_l.extend(temp_list[::-1])
																						temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
												return keys_l




							def 					A__ (							self):
												lowercase		      =		sum(self.__neg_pos([ord(A__) for x in self.__passcode]))
												return num if num > 0 else len(self.__passcode)




							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# decoding shift like Caesar cipher algorithm implementing negative shift or
												# reverse shift or left shift
												for i in encoded_message:
																	lowercase		      =		self.__key_list.index(A__)
																	decoded_message += self.__key_list[
																	    (position - self.__shift_key) % -len(self.__key_list)
																	]

												return decoded_message





							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# encoding shift like Caesar cipher algorithm implementing positive shift or
												# forward shift or right shift
												for i in plaintext:
																	lowercase		      =		self.__key_list.index(A__)
																	encoded_message += self.__key_list[
																	    (position + self.__shift_key) % len(self.__key_list)
																	]

												return encoded_message



def 			UpperCamelCase       (				lowerCAmelCase__ = "Hello, this is a modified Caesar cipher"						):
					'''simple docstring'''
					lowercase		      =		ShuffledShiftCipher()
					return cipa.decrypt(cipa.encrypt(lowerCAmelCase__						)						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




