


'''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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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      :      List[Any]            =					16
_lowercase      :      List[str]            =					32


def 				lowerCamelCase__				(	A       :      Accelerator				,       A       :      int = 16					):




						'''simple docstring'''






						UpperCAmelCase					    =     AutoTokenizer.from_pretrained('''bert-base-cased'''					)
						UpperCAmelCase					    =     load_dataset('''glue'''				,       '''mrpc'''					)

						def tokenize_function(A       :      int					):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase					    =     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():
												UpperCAmelCase					    =     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
						UpperCAmelCase					    =     tokenized_datasets.rename_column('''label'''				,       '''labels'''					)

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

												return tokenizer.pad(
												    A				,       padding='''longest'''				,       max_length=A				,       pad_to_multiple_of=A				,       return_tensors='''pt'''				,       )

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

						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[Any]            =					mocked_dataloaders  # noqa: F811


def 				lowerCamelCase__				(	A       :      Tuple				,       A       :      List[str]					):




						'''simple docstring'''






						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,       A					) == "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''']					)

						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

						set_seed(A					)
						UpperCAmelCase    ,    UpperCAmelCase					    =     get_dataloaders(A				,       A					)
						# 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=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).
						UpperCAmelCase					    =     model.to(accelerator.device					)

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

						# Instantiate scheduler
						UpperCAmelCase					    =     get_linear_schedule_with_warmup(
						    optimizer=A				,       num_warmup_steps=1_00				,       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.
						UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     accelerator.prepare(
						    A				,       A				,       A				,       A				,       A					)

						# Now we train the model
						for epoch in range(A					):
												model.train()
												for step, batch in enumerate(A					):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device					)
																		UpperCAmelCase					    =     model(**A					)
																		UpperCAmelCase					    =     outputs.loss
																		UpperCAmelCase					    =     loss / gradient_accumulation_steps
																		accelerator.backward(A					)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												UpperCAmelCase					    =     0
												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():
																								UpperCAmelCase					    =     model(**A					)
																		UpperCAmelCase					    =     outputs.logits.argmax(dim=-1					)
																		UpperCAmelCase    ,    UpperCAmelCase					    =     accelerator.gather((predictions, batch['''labels'''])					)
																		# New Code #
																		# First we check if it's a distributed system
																		if accelerator.use_distributed:
																								# Then see if we're on the last batch of our eval dataloader
																								if step == len(A					) - 1:
																														# Last batch needs to be truncated on distributed systems as it contains additional samples
																														UpperCAmelCase					    =     predictions[: len(eval_dataloader.dataset					) - samples_seen]
																														UpperCAmelCase					    =     references[: len(eval_dataloader.dataset					) - samples_seen]
																								else:
																														# Otherwise we add the number of samples seen
																														samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																		metric.add_batch(
																		    predictions=A				,       references=A				,       )

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


def 				lowerCamelCase__				(	):




						'''simple docstring'''






						UpperCAmelCase					    =     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.'''					)
						UpperCAmelCase					    =     parser.parse_args()
						UpperCAmelCase					    =     {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(A				,       A					)


if __name__ == "__main__":
	main()









'''simple docstring'''



# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)





