
'''simple docstring'''







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available






@dataclass
class 				lowerCAmelCase__     (						SCREAMING_SNAKE_CASE__  ):
					lowerCAmelCase_       =  4_2


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
							from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
							from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline





'''simple docstring'''







import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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				:						Tuple												= 3_2
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Accelerator   ,   __SCREAMING_SNAKE_CASE      :				int = 16							):



						"""simple docstring"""





						lowercase_			:  int									= AutoTokenizer.from_pretrained('''bert-base-cased'''							)
						lowercase_			:  int									= load_dataset('''glue'''   ,   '''mrpc'''							)

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

						def collate_fn(__SCREAMING_SNAKE_CASE      :				List[Any]							):
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		lowercase_			:  List[str]									= 16
												elif accelerator.mixed_precision != "no":
																		lowercase_			:  List[Any]									= 8
												else:
																		lowercase_			:  List[Any]									= None

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

						# Instantiate dataloaders.
						lowercase_			:  List[str]									= DataLoader(
						    tokenized_datasets['''train''']   ,   shuffle=__SCREAMING_SNAKE_CASE   ,   collate_fn=__SCREAMING_SNAKE_CASE   ,   batch_size=__SCREAMING_SNAKE_CASE							)
						lowercase_			:  int									= DataLoader(
						    tokenized_datasets['''validation''']   ,   shuffle=__SCREAMING_SNAKE_CASE   ,   collate_fn=__SCREAMING_SNAKE_CASE   ,   batch_size=__SCREAMING_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

							_lowercase				:						Tuple												= mocked_dataloaders  # noqa: F811
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				str   ,   __SCREAMING_SNAKE_CASE      :				List[Any]							):



						"""simple docstring"""





						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,   __SCREAMING_SNAKE_CASE							) == "1":
												lowercase_			:  Dict									= 2
						# Initialize accelerator
						lowercase_			:  Any									= Accelerator(cpu=args.cpu   ,   mixed_precision=args.mixed_precision							)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase_			:  Optional[Any]									= config['''lr''']
						lowercase_			:  Optional[Any]									= int(config['''num_epochs''']							)
						lowercase_			:  str									= int(config['''seed''']							)
						lowercase_			:  Optional[Any]									= int(config['''batch_size''']							)

						lowercase_			:  int									= evaluate.load('''glue'''   ,   '''mrpc'''							)

						# New Code #
						# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
						# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
						# half each time. From this, we can calculate the number of gradient accumulation steps needed
						# and modify the Accelerator object as a result
						@find_executable_batch_size(starting_batch_size=int(__SCREAMING_SNAKE_CASE							)							)
						def inner_training_loop(__SCREAMING_SNAKE_CASE      :				Any							):
												# Since we need to modify the outside accelerator object, we need to bring it
												# to the local scope
												nonlocal accelerator

												# We can calculate the number of gradient accumulation steps based on the current
												# batch size vs the starting batch size
												lowercase_			:  Union[str, Any]									= observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												lowercase_			:  Tuple									= num_gradient_accumulation_steps

												# Next we need to free all of the stored model references in the Accelerator each time
												accelerator.free_memory()

												# And set the seed so our results are reproducable each reset
												set_seed(__SCREAMING_SNAKE_CASE							)

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												lowercase_			:  Dict									= AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   ,   return_dict=__SCREAMING_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).
												lowercase_			:  Optional[int]									= model.to(accelerator.device							)

												# Instantiate optimizer
												lowercase_			:  int									= AdamW(params=model.parameters()   ,   lr=__SCREAMING_SNAKE_CASE							)
												lowercase_		,				lowercase_			:  List[str]									= get_dataloaders(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)

												# Instantiate scheduler
												lowercase_			:  str									= get_linear_schedule_with_warmup(
												    optimizer=__SCREAMING_SNAKE_CASE   ,   num_warmup_steps=100   ,   num_training_steps=(len(__SCREAMING_SNAKE_CASE							) * num_epochs)   ,   )

												# 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.
												lowercase_		,				lowercase_		,				lowercase_		,				lowercase_		,				lowercase_			:  Any									= accelerator.prepare(
												    __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)

												# Now we train the model
												for epoch in range(__SCREAMING_SNAKE_CASE							):
																		model.train()
																		for step, batch in enumerate(__SCREAMING_SNAKE_CASE							):
																								# And perform gradient accumulation
																								with accelerator.accumulate(__SCREAMING_SNAKE_CASE							):
																														# We could avoid this line since we set the accelerator with `device_placement=True`.
																														batch.to(accelerator.device							)
																														lowercase_			:  List[str]									= model(**__SCREAMING_SNAKE_CASE							)
																														lowercase_			:  Any									= outputs.loss
																														accelerator.backward(__SCREAMING_SNAKE_CASE							)
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()
def       snake_case_	(       ):



						"""simple docstring"""





						lowercase_			:  int									= argparse.ArgumentParser(description='''Simple example of training script.'''							)
						parser.add_argument(
						    '''--mixed_precision'''   ,   type=__SCREAMING_SNAKE_CASE   ,   default=__SCREAMING_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.'''							)
						lowercase_			:  int									= parser.parse_args()
						# New Code #
						# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
						lowercase_			:  Union[str, Any]									= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
						training_function(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							main()



