





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,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

_UpperCAmelCase										=					1_6
_UpperCAmelCase										=					3_2




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_ = 16	)  ->		Dict:
       UpperCamelCase_ 							=	AutoTokenizer.from_pretrained("bert-base-cased"	)
       UpperCamelCase_ 							=	load_dataset("glue"       ,		"mrpc"	)

       def tokenize_function(UpperCamelCase_	):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase_ 							=	tokenizer(examples["sentence1"]       ,		examples["sentence2"]       ,		truncation=UpperCamelCase_       ,		max_length=UpperCamelCase_	)
              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(
                  UpperCamelCase_       ,		batched=UpperCamelCase_       ,		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"	)

       def collate_fn(UpperCamelCase_	):
              # 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(
                  UpperCamelCase_       ,		padding="longest"       ,		max_length=UpperCamelCase_       ,		pad_to_multiple_of=UpperCamelCase_       ,		return_tensors="pt"       ,		)

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

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     _UpperCAmelCase										=					mocked_dataloaders  # noqa: F811




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       # For testing only
       if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,		UpperCamelCase_	) == "1":
              UpperCamelCase_ 							=	2
       # Initialize accelerator
       UpperCamelCase_ 							=	Accelerator(cpu=args.cpu       ,		mixed_precision=args.mixed_precision	)
       # 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"]	)

       # New Code #
       # Parse out whether we are saving every epoch or after a certain number of batches
       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

       set_seed(UpperCamelCase_	)

       UpperCamelCase_							,			UpperCamelCase_ 							=	get_dataloaders(UpperCamelCase_       ,		UpperCamelCase_	)
       UpperCamelCase_ 							=	evaluate.load("glue"       ,		"mrpc"	)

       # 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

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

       # 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).
       UpperCamelCase_ 							=	model.to(accelerator.device	)

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

       # Instantiate scheduler
       UpperCamelCase_ 							=	get_linear_schedule_with_warmup(
           optimizer=UpperCamelCase_       ,		num_warmup_steps=100       ,		num_training_steps=(len(UpperCamelCase_	) * 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.
       UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	accelerator.prepare(
           UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       UpperCamelCase_ 							=	0
       # We also need to keep track of the stating epoch so files are named properly
       UpperCamelCase_ 							=	0

       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       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(UpperCamelCase_	)[0]

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

    # Now we train the model
       for epoch in range(UpperCamelCase_       ,		UpperCamelCase_	):
              model.train()
              # New Code #
              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
                     UpperCamelCase_ 							=	accelerator.skip_first_batches(UpperCamelCase_       ,		UpperCamelCase_	)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     UpperCamelCase_ 							=	train_dataloader
              for step, batch in enumerate(UpperCamelCase_	):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device	)
                     UpperCamelCase_ 							=	model(**UpperCamelCase_	)
                     UpperCamelCase_ 							=	outputs.loss
                     UpperCamelCase_ 							=	loss / gradient_accumulation_steps
                     accelerator.backward(UpperCamelCase_	)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()
                     # New Code #
                     overall_step += 1

                     # New Code #
                     # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                     # These are saved to folders named `step_{overall_step}`
                     # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                     # If mixed precision was used, will also save a "scalar.bin" file
                     if isinstance(UpperCamelCase_       ,		UpperCamelCase_	):
                            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       ,		UpperCamelCase_	)
                                   accelerator.save_state(UpperCamelCase_	)

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

              UpperCamelCase_ 							=	metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:'''       ,		UpperCamelCase_	)

              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `epoch_{epoch}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if checkpointing_steps == "epoch":
                     UpperCamelCase_ 							=	F'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            UpperCamelCase_ 							=	os.path.join(args.output_dir       ,		UpperCamelCase_	)
                     accelerator.save_state(UpperCamelCase_	)




def 			lowerCAmelCase_			(				)  ->		Dict:
       UpperCamelCase_ 							=	argparse.ArgumentParser(description="Simple example of training script."	)
       parser.add_argument(
           "--mixed_precision"       ,		type=UpperCamelCase_       ,		default=UpperCamelCase_       ,		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=UpperCamelCase_       ,		default=UpperCamelCase_       ,		help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."       ,		)
       parser.add_argument(
           "--output_dir"       ,		type=UpperCamelCase_       ,		default="."       ,		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."       ,		)
       parser.add_argument(
           "--resume_from_checkpoint"       ,		type=UpperCamelCase_       ,		default=UpperCamelCase_       ,		help="If the training should continue from a checkpoint folder."       ,		)
       UpperCamelCase_ 							=	parser.parse_args()
       UpperCamelCase_ 							=	{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(UpperCamelCase_       ,		UpperCamelCase_	)


if __name__ == "__main__":
     main()







def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					53_81
      for x in s:
            lowerCamelCase__   	=					((hash_value << 5) + hash_value) + ord(lowerCamelCase    )
      return hash_value & 0Xffff_ffff