
"""simple docstring"""
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
#
########################################################################

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


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 16			)  ->      List[Any]:


					'''simple docstring'''
					__a:    List[str]		     =       AutoTokenizer.from_pretrained("""bert-base-cased"""			)
					__a:    Tuple		     =       load_dataset("""glue"""						,					"""mrpc"""			)

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

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

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

					# Instantiate dataloaders.
					__a:    Dict		     =       DataLoader(
					    tokenized_datasets["""train"""]						,					shuffle=lowerCamelCase_						,					collate_fn=lowerCamelCase_						,					batch_size=lowerCamelCase_			)
					__a:    int		     =       DataLoader(
					    tokenized_datasets["""validation"""]						,					shuffle=lowerCamelCase_						,					collate_fn=lowerCamelCase_						,					batch_size=lowerCamelCase_			)

					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

			lowercase		:    List[str]			=       mocked_dataloaders  # noqa: F811


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_			)  ->      Optional[int]:


					'''simple docstring'''
					# For testing only
					if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,					lowerCamelCase_			) == "1":
										__a:    Optional[Any]		     =       2
					# Initialize accelerator
					__a:    Optional[Any]		     =       Accelerator(cpu=args.cpu						,					mixed_precision=args.mixed_precision			)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__a:    Optional[Any]		     =       config["""lr"""]
					__a:    Tuple		     =       int(config["""num_epochs"""]			)
					__a:    str		     =       int(config["""seed"""]			)
					__a:    Union[str, Any]		     =       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":
															__a:    Any		     =       args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															__a:    Optional[int]		     =       int(args.checkpointing_steps			)
										else:
															raise ValueError(
															    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""			)
					else:
										__a:    Optional[Any]		     =       None

					set_seed(lowerCamelCase_			)

					__a     ,					__a:    Optional[int]		     =       get_dataloaders(lowerCamelCase_						,					lowerCamelCase_			)
					__a:    Union[str, Any]		     =       evaluate.load("""glue"""						,					"""mrpc"""			)

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

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

					# 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:    Union[str, Any]		     =       model.to(accelerator.device			)

					# Instantiate optimizer
					__a:    Union[str, Any]		     =       AdamW(params=model.parameters()						,					lr=lowerCamelCase_			)

					# Instantiate scheduler
					__a:    int		     =       get_linear_schedule_with_warmup(
					    optimizer=lowerCamelCase_						,					num_warmup_steps=1_00						,					num_training_steps=(len(lowerCamelCase_			) * 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:    Tuple		     =       accelerator.prepare(
					    lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_			)

					# New Code #
					# We need to keep track of how many total steps we have iterated over
					__a:    str		     =       0
					# We also need to keep track of the stating epoch so files are named properly
					__a:    Union[str, Any]		     =       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			)
															__a:    List[Any]		     =       os.path.basename(args.resume_from_checkpoint			)
										else:
															# Get the most recent checkpoint
															__a:    Optional[Any]		     =       [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
															dirs.sort(key=os.path.getctime			)
															__a:    Tuple		     =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										__a:    str		     =       os.path.splitext(lowerCamelCase_			)[0]

										if "epoch" in training_difference:
															__a:    Optional[int]		     =       int(training_difference.replace("""epoch_"""						,					""""""			)			) + 1
															__a:    Optional[int]		     =       None
										else:
															__a:    Dict		     =       int(training_difference.replace("""step_"""						,					""""""			)			)
															__a:    Optional[Any]		     =       resume_step // len(lowerCamelCase_			)
															resume_step -= starting_epoch * len(lowerCamelCase_			)

    # Now we train the model
					for epoch in range(lowerCamelCase_						,					lowerCamelCase_			):
										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
															__a:    int		     =       accelerator.skip_first_batches(lowerCamelCase_						,					lowerCamelCase_			)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															__a:    Optional[Any]		     =       train_dataloader
										for step, batch in enumerate(lowerCamelCase_			):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device			)
															__a:    Optional[Any]		     =       model(**lowerCamelCase_			)
															__a:    Optional[Any]		     =       outputs.loss
															__a:    Optional[int]		     =       loss / gradient_accumulation_steps
															accelerator.backward(lowerCamelCase_			)
															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(lowerCamelCase_						,					lowerCamelCase_			):
																				__a:    Any		     =       f"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														__a:    Dict		     =       os.path.join(args.output_dir						,					lowerCamelCase_			)
																									accelerator.save_state(lowerCamelCase_			)

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

										__a:    Any		     =       metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f"""epoch {epoch}:"""						,					lowerCamelCase_			)

										# 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":
															__a:    Dict		     =       f"""epoch_{epoch}"""
															if args.output_dir is not None:
																				__a:    Optional[int]		     =       os.path.join(args.output_dir						,					lowerCamelCase_			)
															accelerator.save_state(lowerCamelCase_			)


def      lowerCamelCase_     (						)  ->      Optional[Any]:


					'''simple docstring'''
					__a:    Union[str, Any]		     =       argparse.ArgumentParser(description="""Simple example of training script."""			)
					parser.add_argument(
					    """--mixed_precision"""						,					type=lowerCamelCase_						,					default=lowerCamelCase_						,					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=lowerCamelCase_						,					default=lowerCamelCase_						,					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=lowerCamelCase_						,					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=lowerCamelCase_						,					default=lowerCamelCase_						,					help="""If the training should continue from a checkpoint folder."""						,					)
					__a:    int		     =       parser.parse_args()
					__a:    str		     =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
					training_function(lowerCamelCase_						,					lowerCamelCase_			)


if __name__ == "__main__":
			main()


"""simple docstring"""
from transformers import ProcessorMixin




class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    '''AutoFeatureExtractor'''
							SCREAMING_SNAKE_CASE		  =    '''AutoTokenizer'''
