





'''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_   :  Dict								=      16
snake_case_   :  Optional[Any]								=      32



def  lowercase__(		_UpperCamelCase  : List[str]				)->   Any:






					"""simple docstring"""

					return int(x / 2**20				)

class     A_ :

			'''simple docstring'''





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

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




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




			def       a	(					self   ):
								_UpperCamelCase											=   -1

								while True:
													_UpperCamelCase											=   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_   ):
								_UpperCamelCase											=   False

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

								_UpperCamelCase											=   self.cpu_mem_used()
								_UpperCamelCase											=   bamb(self.cpu_end - self.cpu_begin   )
								_UpperCamelCase											=   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_   :  Union[str, Any]								=      mocked_dataloaders  # noqa: F811



def  lowercase__(		_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : int				)->   Dict:






					"""simple docstring"""

					if os.environ.get("TESTING_MOCKED_DATALOADERS" ,   SCREAMING_SNAKE_CASE__				) == "1":
										_UpperCamelCase											=   2

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

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

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

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

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

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

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

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

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

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

					set_seed(SCREAMING_SNAKE_CASE__				)

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

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

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

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

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

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

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

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

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

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

					if args.with_tracking:
										accelerator.end_training()



def  lowercase__(		)->   List[str]:






					"""simple docstring"""

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


if __name__ == "__main__":
			main()










'''simple docstring'''



from __future__ import annotations



def  lowercase__(		_UpperCamelCase  : list[list[int]] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int | float				)->   None:






					"""simple docstring"""

					_UpperCamelCase				,	_UpperCamelCase											=   m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f"Key {key} found at row- {i + 1} column- {j + 1}"				)
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f"Key {key} not found"				)



def  lowercase__(		)->   None:






					"""simple docstring"""

					_UpperCamelCase											=   [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
					_UpperCamelCase											=   int(input("Enter the element to be searched:"				)				)
					print(_UpperCamelCase				)
					search_in_a_sorted_matrix(_UpperCamelCase ,   len(_UpperCamelCase				) ,   len(mat[0]				) ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



