

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
#
########################################################################

A_	:			Optional[Any]			=      16
A_	:			Optional[int]			=      32

def 						UpperCamelCase (lowercase_:			Accelerator   ,				lowercase_:			int = 16 )					->				Optional[Any]:
	A__							:   Union[str, Any]					       =		AutoTokenizer.from_pretrained("""bert-base-cased""" )
	A__							:   Union[str, Any]					       =		load_dataset("""glue"""   ,				"""mrpc""" )

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

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

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

	# Instantiate dataloaders.
	A__							:   List[Any]					       =		DataLoader(
	    tokenized_datasets["""train"""]   ,				shuffle=lowercase_   ,				collate_fn=lowercase_   ,				batch_size=lowercase_ )
	A__							:   List[str]					       =		DataLoader(
	    tokenized_datasets["""validation"""]   ,				shuffle=lowercase_   ,				collate_fn=lowercase_   ,				batch_size=lowercase_ )

	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

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

def 						UpperCamelCase (lowercase_:			Optional[Any]   ,				lowercase_:			Optional[int] )					->				Optional[int]:
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""   ,				lowercase_ ) == "1":
		A__							:   Optional[Any]					       =		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__							:   Tuple					       =		Accelerator(
		    cpu=args.cpu   ,				mixed_precision=args.mixed_precision   ,				log_with="""all"""   ,				project_dir=args.project_dir )
	else:
		A__							:   Tuple					       =		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__							:   Optional[int]					       =		int(config["""num_epochs"""] )
	A__							:   int					       =		int(config["""seed"""] )
	A__							:   Union[str, Any]					       =		int(config["""batch_size"""] )
	set_seed(lowercase_ )

	A__							:   str					       =		get_dataloaders(lowercase_   ,				lowercase_ )
	A__							:   List[str]					       =		evaluate.load("""glue"""   ,				"""mrpc""" )

	# If the batch size is too big we use gradient accumulation
	A__							:   int					       =		1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A__							:   int					       =		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__							:   List[Any]					       =		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""   ,				return_dict=lowercase_ )

	# 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__							:   Any					       =		model.to(accelerator.device )

	# Instantiate optimizer
	A__							:   int					       =		AdamW(params=model.parameters()   ,				lr=lowercase_ )

	# Instantiate scheduler
	A__							:   Any					       =		get_linear_schedule_with_warmup(
	    optimizer=lowercase_   ,				num_warmup_steps=100   ,				num_training_steps=(len(lowercase_ ) * 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__							:   Dict					       =		accelerator.prepare(
	    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )

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

	# Now we train the model
	for epoch in range(lowercase_ ):
		model.train()
		# New Code #
		# For our tracking example, we will log the total loss of each epoch
		if args.with_tracking:
			A__							:   Union[str, Any]					       =		0
		for step, batch in enumerate(lowercase_ ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device )
			A__							:   str					       =		model(**lowercase_ )
			A__							:   Optional[Any]					       =		outputs.loss
			# New Code #
			if args.with_tracking:
				total_loss += loss.detach().float()
			A__							:   List[str]					       =		loss / gradient_accumulation_steps
			accelerator.backward(lowercase_ )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		A__							:   List[str]					       =		metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""   ,				lowercase_ )

		# 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(lowercase_ ),
			        """epoch""": epoch,
			    }   ,				step=lowercase_   ,				)

    # 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 ()					->				List[Any]:
	A__							:   List[Any]					       =		argparse.ArgumentParser(description="""Simple example of training script.""" )
	parser.add_argument(
	    """--mixed_precision"""   ,				type=lowercase_   ,				default=lowercase_   ,				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=lowercase_   ,				default="""logs"""   ,				help="""Location on where to store experiment tracking logs` and relevent project information"""   ,				)
	A__							:   Dict					       =		parser.parse_args()
	A__							:   Any					       =		{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(lowercase_   ,				lowercase_ )


if __name__ == "__main__":
	main()






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



