

"""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
#
########################################################################


A_							:     Dict     =	16
A_							:     int     =	32

def 		A    (       snake_case__      ):




			'''simple docstring'''


			return int(x / 2**20      )





class 				lowerCamelCase  :

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

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

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      Optional[Any]:
						return self.process.memory_info().rss

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Any:
						SCREAMING_SNAKE_CASE__									= -1

						while True:
									SCREAMING_SNAKE_CASE__									= 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			:  Any      ,			*__UpperCAmelCase			:  Dict    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= False

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

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

						A_							:     Optional[Any]     =	mocked_dataloaders  # noqa: F811

def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


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

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

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

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

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

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

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

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

			def collate_fn(snake_case__      ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						SCREAMING_SNAKE_CASE__									= 1_28 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									SCREAMING_SNAKE_CASE__									= 16
						elif accelerator.mixed_precision != "no":
									SCREAMING_SNAKE_CASE__									= 8
						else:
									SCREAMING_SNAKE_CASE__									= None

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

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

			set_seed(snake_case__      )

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

			# New Code #
			# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
			SCREAMING_SNAKE_CASE__									= accelerator.prepare(snake_case__      )
			accelerator.print(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
			SCREAMING_SNAKE_CASE__									= torch.optim.AdamW(params=model.parameters()						,			lr=snake_case__						,			weight_decay=2e-4      )

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

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

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

    # Now we train the model
			for epoch in range(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:
												SCREAMING_SNAKE_CASE__									= 0
									for step, batch in enumerate(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      )
												SCREAMING_SNAKE_CASE__									= model(**snake_case__      )
												SCREAMING_SNAKE_CASE__									= outputs.loss
												SCREAMING_SNAKE_CASE__									= loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
															total_loss += loss.detach().float()
												accelerator.backward(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(snake_case__						,			snake_case__      ):
															SCREAMING_SNAKE_CASE__									= f"""step_{overall_step}"""
															if overall_step % checkpointing_steps == 0:
																		if args.output_dir is not None:
																					SCREAMING_SNAKE_CASE__									= os.path.join(args.output_dir						,			snake_case__      )
																		accelerator.save_state(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=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(snake_case__      ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device      )
												with torch.no_grad():
															SCREAMING_SNAKE_CASE__									= model(**snake_case__      )
												SCREAMING_SNAKE_CASE__									= outputs.logits.argmax(dim=-1      )
												SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= accelerator.gather_for_metrics((predictions, batch["""labels"""])      )
												metric.add_batch(
												    predictions=snake_case__						,			references=snake_case__						,			)

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

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

			if args.with_tracking:
						accelerator.end_training()

def 		A    (       ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= argparse.ArgumentParser(description="""Simple example of training script."""      )
			parser.add_argument(
			    """--mixed_precision"""						,			type=snake_case__						,			default=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=snake_case__						,			default=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=snake_case__						,			default=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=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=snake_case__						,			default="""logs"""						,			help="""Location on where to store experiment tracking logs`"""						,			)
			parser.add_argument(
			    """--model_name_or_path"""						,			type=snake_case__						,			help="""Path to pretrained model or model identifier from huggingface.co/models."""						,			required=snake_case__						,			)
			SCREAMING_SNAKE_CASE__									= parser.parse_args()
			SCREAMING_SNAKE_CASE__									= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(snake_case__						,			snake_case__      )


if __name__ == "__main__":
						main()





"""simple docstring"""




from .hash_table import HashTable





class 				lowerCamelCase  (A__     ):

			def __init__(   self			:  List[str]      ,			*__UpperCAmelCase			:  Union[str, Any]      ,			**__UpperCAmelCase			:  Any    )				->      Optional[int]:
						super().__init__(*__UpperCAmelCase      ,			**__UpperCAmelCase    )





			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=None    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= 1
						SCREAMING_SNAKE_CASE__									= self.hash_function(key + i * i    )

						while self.values[new_key] is not None and self.values[new_key] != key:
									i += 1
									SCREAMING_SNAKE_CASE__									= (
									    self.hash_function(key + i * i    )
									    if not self.balanced_factor() >= self.lim_charge
									    else None
									)

									if new_key is None:
												break

						return new_key


