




from .hash_table import HashTable




class    snake_case__							(SCREAMING_SNAKE_CASE_       ):
				"""simple docstring"""



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





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

							while self.values[new_key] is not None and self.values[new_key] != key:
										i += 1
										a   =      (
										    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












import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# 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)
#
# 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
#
########################################################################


__lowerCAmelCase		:    List[Any]	      =		16
__lowerCAmelCase		:    List[Any]	      =		32




def       __magic_name__    (			A		: List[Any],  A		: List[Any]       ):






			'''simple docstring'''







			if args.with_tracking:
						a   =      Accelerator(
						    cpu=args.cpu,  mixed_precision=args.mixed_precision,  log_with="all",  project_dir=args.project_dir       )
			else:
						a   =      Accelerator(cpu=args.cpu,  mixed_precision=args.mixed_precision       )

			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   =      os.path.split(A       )[-1].split("."       )[0]
						accelerator.init_trackers(A,  A       )

			a   =      AutoTokenizer.from_pretrained("bert-base-cased"       )
			a   =      load_dataset("glue",  "mrpc"       )
			a   =      evaluate.load("glue",  "mrpc"       )

			def tokenize_function(A		: Tuple       ):
						# max_length=None => use the model max length (it's actually the default)
						a   =      tokenizer(examples["sentence1"],  examples["sentence2"],  truncation=A,  max_length=A       )
						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(
						    A,  batched=A,  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(A		: Tuple       ):
						# 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(
						    A,  padding="longest",  max_length=A,  pad_to_multiple_of=A,  return_tensors="pt",  )

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

			set_seed(A       )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a   =      AutoModelForSequenceClassification.from_pretrained("bert-base-cased",  return_dict=A       )

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			a   =      model.to(accelerator.device       )

			# Instantiate optimizer
			a   =      AdamW(params=model.parameters(),  lr=A       )

			# Instantiate scheduler
			a   =      get_linear_schedule_with_warmup(
			    optimizer=A,  num_warmup_steps=100,  num_training_steps=(len(A       ) * num_epochs) // gradient_accumulation_steps,  )

			# Prepare everything
			# 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   ,		a   =      accelerator.prepare(
			    A,  A,  A,  A,  A       )

			# We need to keep track of how many total steps we have iterated over
			a   =      0
			# We also need to keep track of the stating epoch so files are named properly
			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(A       )[0]

						if "epoch" in training_difference:
									a   =      int(training_difference.replace("epoch_",  ""       )       ) + 1
									a   =      None
						else:
									a   =      int(training_difference.replace("step_",  ""       )       )
									a   =      resume_step // len(A       )
									resume_step -= starting_epoch * len(A       )

    # Now we train the model
			for epoch in range(A,  A       ):
						model.train()
						if args.with_tracking:
									a   =      0
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									a   =      accelerator.skip_first_batches(A,  A       )
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									a   =      train_dataloader
						for step, batch in enumerate(A       ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device       )
									a   =      model(**A       )
									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(A       )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

									if isinstance(A,  A       ):
												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,  A       )
															accelerator.save_state(A       )

						model.eval()
						for step, batch in enumerate(A       ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device       )
									with torch.no_grad():
												a   =      model(**A       )
									a   =      outputs.logits.argmax(dim=-1       )
									a   ,		a   =      accelerator.gather_for_metrics((predictions, batch["labels"])       )
									metric.add_batch(
									    predictions=A,  references=A,  )

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

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

			if args.with_tracking:
						accelerator.end_training()




def       __magic_name__    (			):






			'''simple docstring'''







			a   =      argparse.ArgumentParser(description="Simple example of training script."       )
			parser.add_argument(
			    "--mixed_precision",  type=A,  default=A,  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=A,  default=A,  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=A,  default=A,  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=A,  default=".",  help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",  )
			parser.add_argument(
			    "--project_dir",  type=A,  default="logs",  help="Location on where to store experiment tracking logs` and relevent project information",  )
			a   =      parser.parse_args()
			a   =      {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(A,  A       )


if __name__ == "__main__":
		main()






