
from .number_theory.prime_numbers import next_prime





class    _snake_case      :







	def __init__(       self		,     _a		,     _a = None		,     _a = None		,     ):
		__magic_name__	:	Optional[int]			     =  size_table
		__magic_name__	:	int			     =  [None] * self.size_table
		__magic_name__	:	int			     =  0.75 if lim_charge is None else lim_charge
		__magic_name__	:	int			     =  1 if charge_factor is None else charge_factor
		__magic_name__	:	list			     =  []
		__magic_name__	:	dict			     =  {}







	def     SCREAMING_SNAKE_CASE					(       self	):
		return self._keys







	def     SCREAMING_SNAKE_CASE					(       self	):
		return sum(1 for slot in self.values if slot is not None	) / (
		    self.size_table * self.charge_factor
		)







	def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
		return key % self.size_table







	def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
		print(f'''step {step_ord}'''	)
		print(list(range(len(self.values	)	)	)	)
		print(self.values	)







	def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
		__magic_name__	:	int			     =  1
		__magic_name__	:	Optional[int]			     =  values
		for value in values:
			self.insert_data(_A	)
			self._step_by_step(_A	)
			i += 1







	def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
		__magic_name__	:	str			     =  data
		__magic_name__	:	Optional[int]			     =  data







	def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a=None	):
		__magic_name__	:	int			     =  self.hash_function(key + 1	)

		while self.values[new_key] is not None and self.values[new_key] != key:
			if self.values.count(_A	) > 0:
				__magic_name__	:	Dict			     =  self.hash_function(new_key + 1	)
			else:
				__magic_name__	:	Tuple			     =  None
				break

		return new_key







	def     SCREAMING_SNAKE_CASE					(       self	):
		__magic_name__	:	Dict			     =  [value for value in self.values if value is not None]
		__magic_name__	:	int			     =  next_prime(self.size_table		,     factor=2	)
		self._keys.clear()
		__magic_name__	:	Tuple			     =  [None] * self.size_table  # hell's pointers D: don't DRY ;/
		for value in survivor_values:
			self.insert_data(_A	)







	def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
		__magic_name__	:	Tuple			     =  self.hash_function(_A	)

		if self.values[key] is None:
			self._set_value(_A		,     _A	)

		elif self.values[key] == data:
			pass

		else:
			__magic_name__	:	str			     =  self._collision_resolution(_A		,     _A	)
			if collision_resolution is not None:
				self._set_value(_A		,     _A	)
			else:
				self.rehashing()
				self.insert_data(_A	)


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				:	Any							   =						16
snake_case				:	Union[str, Any]							   =						32






def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	Any:







	'''simple docstring'''


	return int(x / 2**20     )





class    _snake_case      :







			def __enter__(       self	):
				gc.collect()
				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
				__magic_name__	:	List[Any]			     =  torch.cuda.memory_allocated()
				__magic_name__	:	Dict			     =  psutil.Process()

				__magic_name__	:	List[str]			     =  self.cpu_mem_used()
				__magic_name__	:	Optional[int]			     =  True
				__magic_name__	:	int			     =  threading.Thread(target=self.peak_monitor_func	)
				__magic_name__	:	str			     =  True
				peak_monitor_thread.start()
				return self







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







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	Dict			     =  -1

				while True:
					__magic_name__	:	Optional[Any]			     =  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	):
				__magic_name__	:	List[Any]			     =  False

				gc.collect()
				torch.cuda.empty_cache()
				__magic_name__	:	Union[str, Any]			     =  torch.cuda.memory_allocated()
				__magic_name__	:	Any			     =  torch.cuda.max_memory_allocated()
				__magic_name__	:	List[Any]			     =  bamb(self.end - self.begin	)
				__magic_name__	:	List[str]			     =  bamb(self.peak - self.begin	)

				__magic_name__	:	List[str]			     =  self.cpu_mem_used()
				__magic_name__	:	int			     =  bamb(self.cpu_end - self.cpu_begin	)
				__magic_name__	:	List[Any]			     =  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				:	Tuple							   =						mocked_dataloaders  # noqa: F811






def 						lowerCAmelCase_		(      _snake_case   :						str   ,     _snake_case   :						Dict     )       ->	Dict:







	'''simple docstring'''


	if os.environ.get("TESTING_MOCKED_DATALOADERS"   ,     _snake_case     ) == "1":
		__magic_name__	:	Optional[Any]			     =  2

	# New Code #
	# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
	__magic_name__	:	Dict			     =  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:
		__magic_name__	:	Union[str, Any]			     =  Accelerator(
		    cpu=args.cpu   ,     mixed_precision=args.mixed_precision   ,     log_with="wandb"   ,     project_dir=args.logging_dir   ,     fsdp_plugin=_snake_case   ,     )
	else:
		__magic_name__	:	List[str]			     =  Accelerator(fsdp_plugin=_snake_case     )
	accelerator.print(accelerator.distributed_type     )

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

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
		__magic_name__	:	List[Any]			     =  vars(_snake_case     )
		accelerator.init_trackers("fsdp_glue_no_trainer"   ,     _snake_case     )

	__magic_name__	:	Dict			     =  AutoTokenizer.from_pretrained(args.model_name_or_path     )
	__magic_name__	:	List[Any]			     =  load_dataset("glue"   ,     "mrpc"     )
	__magic_name__	:	Union[str, Any]			     =  evaluate.load("glue"   ,     "mrpc"     )

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

	# If the batch size is too big we use gradient accumulation
	__magic_name__	:	Dict			     =  1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		__magic_name__	:	List[Any]			     =  batch_size // MAX_GPU_BATCH_SIZE
		__magic_name__	:	Dict			     =  MAX_GPU_BATCH_SIZE

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

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

	# Instantiate dataloaders.
	__magic_name__	:	Optional[int]			     =  DataLoader(
	    tokenized_datasets["train"]   ,     shuffle=_snake_case   ,     collate_fn=_snake_case   ,     batch_size=_snake_case     )
	__magic_name__	:	Dict			     =  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)
	__magic_name__	:	Optional[Any]			     =  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
	__magic_name__	:	List[str]			     =  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
	__magic_name__	:	Optional[Any]			     =  torch.optim.AdamW(params=model.parameters()   ,     lr=_snake_case   ,     weight_decay=2E-4     )

	# Instantiate scheduler
	__magic_name__	:	Any			     =  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.
	__magic_name__     ,					__magic_name__     ,					__magic_name__     ,					__magic_name__	:	Dict			     =  accelerator.prepare(
	    _snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case     )

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

		if "epoch" in training_difference:
			num_epochs -= int(training_difference.replace("epoch_"   ,     ""     )     )
			__magic_name__	:	List[Any]			     =  None
		else:
			__magic_name__	:	List[str]			     =  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
			__magic_name__	:	Tuple			     =  (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:
				__magic_name__	:	List[Any]			     =  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     )
				__magic_name__	:	List[Any]			     =  model(**_snake_case     )
				__magic_name__	:	Optional[int]			     =  outputs.loss
				__magic_name__	:	Tuple			     =  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     ):
					__magic_name__	:	List[Any]			     =  F'''step_{overall_step}'''
					if overall_step % checkpointing_steps == 0:
						if args.output_dir is not None:
							__magic_name__	:	List[str]			     =  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():
					__magic_name__	:	Tuple			     =  model(**_snake_case     )
				__magic_name__	:	Any			     =  outputs.logits.argmax(dim=-1     )
				__magic_name__     ,					__magic_name__	:	Any			     =  accelerator.gather_for_metrics((predictions, batch["labels"])     )
				metric.add_batch(
				    predictions=_snake_case   ,     references=_snake_case   ,     )

			__magic_name__	:	Tuple			     =  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":
				__magic_name__	:	Dict			     =  F'''epoch_{epoch}'''
				if args.output_dir is not None:
					__magic_name__	:	Dict			     =  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 						lowerCAmelCase_		(      )       ->	Optional[int]:







	'''simple docstring'''


	__magic_name__	:	List[str]			     =  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   ,     )
	__magic_name__	:	str			     =  parser.parse_args()
	__magic_name__	:	Dict			     =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(_snake_case   ,     _snake_case     )


if __name__ == "__main__":
				main()
