





"""simple docstring"""
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
#
########################################################################


_snake_case					:		int							=			16
_snake_case					:		Any							=			32






def  A__    (      UpperCamelCase	):
							return int(x / 2**20	)






class  _UpperCAmelCase       :


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

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


					def      lowerCamelCase	( self      :List[Any]		):
												return self.process.memory_info().rss


					def      lowerCamelCase	( self      :Dict		):
												A		=  -1

												while True:
																			A		=  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      :Optional[int]					,	*__UpperCamelCase      :Any		):
												A		=  False

												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		)

												A		=  self.cpu_mem_used()
												A		=  bamb(self.cpu_end - self.cpu_begin		)
												A		=  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

					_snake_case					:		Dict							=			mocked_dataloaders  # noqa: F811






def  A__    (      UpperCamelCase ,  UpperCamelCase	):

							if os.environ.get("TESTING_MOCKED_DATALOADERS" ,  lowercase_	) == "1":
														A		=  2

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

							# Initialize accelerator
							if args.with_tracking:
														A		=  Accelerator(
														    cpu=args.cpu ,  mixed_precision=args.mixed_precision ,  log_with="wandb" ,  project_dir=args.logging_dir ,  fsdp_plugin=lowercase_ ,  )
							else:
														A		=  Accelerator(fsdp_plugin=lowercase_	)
							accelerator.print(accelerator.distributed_type	)

							if hasattr(args.checkpointing_steps ,  "isdigit"	):
														if args.checkpointing_steps == "epoch":
																					A		=  args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					A		=  int(args.checkpointing_steps	)
														else:
																					raise ValueError(
																					    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."	)
							else:
														A		=  None
							# 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"]	)

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

							A		=  AutoTokenizer.from_pretrained(args.model_name_or_path	)
							A		=  load_dataset("glue" ,  "mrpc"	)
							A		=  evaluate.load("glue" ,  "mrpc"	)

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

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

							def collate_fn(UpperCamelCase	):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														A		=  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":
																					A		=  16
														elif accelerator.mixed_precision != "no":
																					A		=  8
														else:
																					A		=  None

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

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

							set_seed(lowercase_	)

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

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

							# 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
							A		=  torch.optim.AdamW(params=model.parameters() ,  lr=lowercase_ ,  weight_decay=2E-4	)

							# Instantiate scheduler
							A		=  get_linear_schedule_with_warmup(
							    optimizer=lowercase_ ,  num_warmup_steps=10 ,  num_training_steps=(len(lowercase_	) * 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.
							A,     A,     A,     A		=  accelerator.prepare(
							    lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_	)

							A		=  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	)
																					A		=  os.path.basename(args.resume_from_checkpoint	)
														else:
																					# Get the most recent checkpoint
																					A		=  [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
																					dirs.sort(key=os.path.getctime	)
																					A		=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														A		=  os.path.splitext(lowercase_	)[0]

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

																					 # Now we train the model
							for epoch in range(lowercase_	):
														# New Code #
														# context manager to track the peak memory usage during the training epoch
														with TorchTracemalloc() as tracemalloc:
																					model.train()
																					if args.with_tracking:
																												A		=  0
																					for step, batch in enumerate(lowercase_	):
																												# 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	)
																												A		=  model(**lowercase_	)
																												A		=  outputs.loss
																												A		=  loss / gradient_accumulation_steps
																												# We keep track of the loss at each epoch
																												if args.with_tracking:
																																			total_loss += loss.detach().float()
																												accelerator.backward(lowercase_	)
																												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(lowercase_ ,  lowercase_	):
																																			A		=  F"step_{overall_step}"
																																			if overall_step % checkpointing_steps == 0:
																																										if args.output_dir is not None:
																																																	A		=  os.path.join(args.output_dir ,  lowercase_	)
																																										accelerator.save_state(lowercase_	)
																																										  # 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=lowercase_ ,  )

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

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

																					if checkpointing_steps == "epoch":
																												A		=  F"epoch_{epoch}"
																												if args.output_dir is not None:
																																			A		=  os.path.join(args.output_dir ,  lowercase_	)
																												accelerator.save_state(lowercase_	)
																												    # 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=lowercase_ ,  )

							if args.with_tracking:
														accelerator.end_training()






def  A__    (      ):
							A		=  argparse.ArgumentParser(description="Simple example of training script."	)
							parser.add_argument(
							    "--mixed_precision" ,  type=lowercase_ ,  default=lowercase_ ,  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=lowercase_ ,  default=lowercase_ ,  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=lowercase_ ,  default=lowercase_ ,  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=lowercase_ ,  default="." ,  help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." ,  )
							parser.add_argument(
							    "--logging_dir" ,  type=lowercase_ ,  default="logs" ,  help="Location on where to store experiment tracking logs`" ,  )
							parser.add_argument(
							    "--model_name_or_path" ,  type=lowercase_ ,  help="Path to pretrained model or model identifier from huggingface.co/models." ,  required=lowercase_ ,  )
							A		=  parser.parse_args()
							A		=  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
							training_function(lowercase_ ,  lowercase_	)


if __name__ == "__main__":
					main()









"""simple docstring"""
def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							for index, item in enumerate(UpperCamelCase	):
														if item == target:
																					return index
							return -1






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							if not (0 <= high < len(UpperCamelCase	) and 0 <= low < len(UpperCamelCase	)):
														raise Exception("Invalid upper or lower bound!"	)
							if high < low:
														return -1
							if sequence[low] == target:
														return low
							if sequence[high] == target:
														return high
							return rec_linear_search(UpperCamelCase ,  low + 1 ,  high - 1 ,  UpperCamelCase	)


if __name__ == "__main__":
		_snake_case					:		int							=			input('Enter numbers separated by comma:\n').strip()
		_snake_case					:		int							=			[int(item.strip()) for item in user_input.split(',')]

		_snake_case					:		Union[str, Any]							=			int(input('Enter a single number to be found in the list:\n').strip())
		_snake_case					:		List[Any]							=			linear_search(sequence, target)
		if result != -1:
				print(F"""linear_search({sequence}, {target}) = {result}""")
		else:
				print(F"""{target} was not found in {sequence}""")


