


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

lowerCAmelCase					 =	1_6
lowerCAmelCase					 =	3_2







def  _lowerCamelCase(			lowercase__     ,				lowercase__ = 1_6			)		->			List[Any]:

						'''simple docstring'''







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

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

						def collate_fn(lowercase__			):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__lowercase=    1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__lowercase=    1_6
												elif accelerator.mixed_precision != "no":
																		__lowercase=    8
												else:
																		__lowercase=    None

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

						# Instantiate dataloaders.
						__lowercase=    DataLoader(
						    tokenized_datasets['train']     ,				shuffle=lowercase__     ,				collate_fn=lowercase__     ,				batch_size=lowercase__			)
						__lowercase=    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

	lowerCAmelCase					 =	mocked_dataloaders  # noqa: F811







def  _lowerCamelCase(			lowercase__     ,				lowercase__			)		->			Optional[int]:

						'''simple docstring'''







						if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,				lowercase__			) == "1":
												__lowercase=    2
						# Initialize accelerator
						__lowercase=    Accelerator(cpu=args.cpu     ,				mixed_precision=args.mixed_precision			)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__lowercase=    config["lr"]
						__lowercase=    int(config['num_epochs']			)
						__lowercase=    int(config['seed']			)
						__lowercase=    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":
																		__lowercase=    args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		__lowercase=    int(args.checkpointing_steps			)
												else:
																		raise ValueError(
																		    F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'			)
						else:
												__lowercase=    None

						set_seed(lowercase__			)

						__lowercase=    get_dataloaders(lowercase__     ,				lowercase__			)
						__lowercase=    evaluate.load('glue'     ,				'mrpc'			)

						# If the batch size is too big we use gradient accumulation
						__lowercase=    1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__lowercase=    batch_size // MAX_GPU_BATCH_SIZE
												__lowercase=    MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__lowercase=    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).
						__lowercase=    model.to(accelerator.device			)

						# Instantiate optimizer
						__lowercase=    AdamW(params=model.parameters()     ,				lr=lowercase__			)

						# Instantiate scheduler
						__lowercase=    get_linear_schedule_with_warmup(
						    optimizer=lowercase__     ,				num_warmup_steps=1_0_0     ,				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.
						__lowercase=    accelerator.prepare(
						    lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)

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

												if "epoch" in training_difference:
																		__lowercase=    int(training_difference.replace('epoch_'     ,				''			)			) + 1
																		__lowercase=    None
												else:
																		__lowercase=    int(training_difference.replace('step_'     ,				''			)			)
																		__lowercase=    resume_step // len(lowercase__			)
																		resume_step -= starting_epoch * len(lowercase__			)

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

												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():
																								__lowercase=    model(**lowercase__			)
																		__lowercase=    outputs.logits.argmax(dim=-1			)
																		__lowercase=    accelerator.gather_for_metrics((predictions, batch['labels'])			)
																		metric.add_batch(
																		    predictions=lowercase__     ,				references=lowercase__     ,				)

												__lowercase=    metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'epoch {epoch}:'     ,				lowercase__			)

												# 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":
																		__lowercase=    F'epoch_{epoch}'
																		if args.output_dir is not None:
																								__lowercase=    os.path.join(args.output_dir     ,				lowercase__			)
																		accelerator.save_state(lowercase__			)







def  _lowerCamelCase(			)		->			Tuple:

						'''simple docstring'''







						__lowercase=    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(
						    '--checkpointing_steps'     ,				type=lowercase__     ,				default=lowercase__     ,				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=lowercase__     ,				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=lowercase__     ,				default=lowercase__     ,				help='If the training should continue from a checkpoint folder.'     ,				)
						__lowercase=    parser.parse_args()
						__lowercase=    {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
						training_function(lowercase__     ,				lowercase__			)


if __name__ == "__main__":
	main()










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

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    A					(     A_				):
							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
							UpperCamelCase_     :							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
	from .pipeline_alt_diffusion import AltDiffusionPipeline
	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline






