






"""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 experiment tracking 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
#
########################################################################

_lowerCAmelCase       =				16
_lowerCAmelCase       =				32






def       __UpperCamelCase						(		snake_case__ , snake_case__ = 16       ):
	A_		:    List[Any]						     = AutoTokenizer.from_pretrained("""bert-base-cased"""       )
	A_		:    Optional[int]						     = load_dataset("""glue""" , """mrpc"""       )

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

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

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

	# Instantiate dataloaders.
	A_		:    Union[str, Any]						     = DataLoader(
	    tokenized_datasets["""train"""] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__       )
	A_		:    Tuple						     = DataLoader(
	    tokenized_datasets["""validation"""] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__       )

	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

							_lowerCAmelCase       =				mocked_dataloaders  # noqa: F811






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , snake_case__       ) == "1":
		A_		:    List[str]						     = 2
	# Initialize Accelerator

	# New Code #
	# We pass in "all" to `log_with` to grab all available trackers in the environment
	# Note: If using a custom `Tracker` class, should be passed in here such as:
	# >>> log_with = ["all", MyCustomTrackerClassInstance()]
	if args.with_tracking:
		A_		:    str						     = Accelerator(
		    cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="""all""" , project_dir=args.project_dir       )
	else:
		A_		:    str						     = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision       )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A_		:    List[str]						     = config["""lr"""]
	A_		:    Any						     = int(config["""num_epochs"""]       )
	A_		:    List[Any]						     = int(config["""seed"""]       )
	A_		:    List[Any]						     = int(config["""batch_size"""]       )
	set_seed(snake_case__       )

	A_     ,  A_		:    List[str]						     = get_dataloaders(snake_case__ , snake_case__       )
	A_		:    Dict						     = evaluate.load("""glue""" , """mrpc"""       )

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

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

	# 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_		:    Dict						     = model.to(accelerator.device       )

	# Instantiate optimizer
	A_		:    Optional[Any]						     = AdamW(params=model.parameters() , lr=snake_case__       )

	# Instantiate scheduler
	A_		:    List[Any]						     = get_linear_schedule_with_warmup(
	    optimizer=snake_case__ , num_warmup_steps=100 , num_training_steps=(len(snake_case__       ) * 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_		:    Optional[int]						     = accelerator.prepare(
	    snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       )

	# New Code #
	# We need to initialize the trackers we use. Overall configurations can also be stored
	if args.with_tracking:
		A_		:    str						     = os.path.split(snake_case__       )[-1].split("""."""       )[0]
		accelerator.init_trackers(snake_case__ , snake_case__       )

	# Now we train the model
	for epoch in range(snake_case__       ):
		model.train()
		# New Code #
		# For our tracking example, we will log the total loss of each epoch
		if args.with_tracking:
			A_		:    Optional[int]						     = 0
		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       )
			A_		:    List[Any]						     = model(**snake_case__       )
			A_		:    Optional[Any]						     = outputs.loss
			# New Code #
			if args.with_tracking:
				total_loss += loss.detach().float()
			A_		:    Optional[int]						     = loss / gradient_accumulation_steps
			accelerator.backward(snake_case__       )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		for step, batch in enumerate(snake_case__       ):
			# 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_		:    int						     = model(**snake_case__       )
			A_		:    List[Any]						     = outputs.logits.argmax(dim=-1       )
			A_     ,  A_		:    int						     = accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
			metric.add_batch(
			    predictions=snake_case__ , references=snake_case__ , )

		A_		:    Union[str, Any]						     = metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"""epoch {epoch}:""" , snake_case__       )

		# New Code #
		# To actually log, we call `Accelerator.log`
		# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
		if args.with_tracking:
			accelerator.log(
			    {
			        """accuracy""": eval_metric["""accuracy"""],
			        """f1""": eval_metric["""f1"""],
			        """train_loss""": total_loss.item() / len(snake_case__       ),
			        """epoch""": epoch,
			    } , step=snake_case__ , )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
	if args.with_tracking:
		accelerator.end_training()






def       __UpperCamelCase						(		):
	A_		:    List[Any]						     = 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(
	    """--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(
	    """--project_dir""" , type=snake_case__ , default="""logs""" , help="""Location on where to store experiment tracking logs` and relevent project information""" , )
	A_		:    Tuple						     = parser.parse_args()
	A_		:    Optional[Any]						     = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(snake_case__ , snake_case__       )


if __name__ == "__main__":
							main()









"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	@register_to_config
	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = False  ,  ):
		super().__init__()

		A_		:    Tuple						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)

		A_		:    List[str]						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_		:    Any						     = False

		A_		:    Tuple						     = nn.Dropout(p=lowerCAmelCase_				)

		A_		:    List[str]						     = TaConfig(
		    vocab_size=lowerCAmelCase_  ,  d_model=lowerCAmelCase_  ,  num_heads=lowerCAmelCase_  ,  d_kv=lowerCAmelCase_  ,  d_ff=lowerCAmelCase_  ,  dropout_rate=lowerCAmelCase_  ,  feed_forward_proj=lowerCAmelCase_  ,  is_decoder=lowerCAmelCase_  ,  is_encoder_decoder=lowerCAmelCase_  ,  )

		A_		:    Optional[Any]						     = nn.ModuleList()
		for lyr_num in range(lowerCAmelCase_				):
			A_		:    Tuple						     = TaBlock(lowerCAmelCase_				)
			self.encoders.append(lowerCAmelCase_				)

		A_		:    Any						     = TaLayerNorm(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = nn.Dropout(p=lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.token_embedder(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = encoder_input_tokens.shape[1]
		A_		:    Any						     = torch.arange(lowerCAmelCase_  ,  device=encoder_input_tokens.device				)
		x += self.position_encoding(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = self.dropout_pre(lowerCAmelCase_				)

		# inverted the attention mask
		A_		:    int						     = encoder_input_tokens.size()
		A_		:    Optional[Any]						     = self.get_extended_attention_mask(lowerCAmelCase_  ,  lowerCAmelCase_				)

		for lyr in self.encoders:
			A_		:    int						     = lyr(lowerCAmelCase_  ,  lowerCAmelCase_				)[0]
		A_		:    List[str]						     = self.layer_norm(lowerCAmelCase_				)

		return self.dropout_post(lowerCAmelCase_				), encoder_inputs_mask

