

import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCAmelCase	:     List[Any]		       = 16
_UpperCAmelCase	:     Dict		       = 32





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__ = 16   ):







							'''simple docstring'''






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

							def tokenize_function(UpperCamelCase__   ):
														# max_length=None => use the model max length (it's actually the default)
														snake_case_           =					tokenizer(examples['sentence1']     ,							examples['sentence2']     ,							truncation=UpperCamelCase__     ,							max_length=UpperCamelCase__   )
														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():
														snake_case_           =					datasets.map(
														    UpperCamelCase__     ,							batched=UpperCamelCase__     ,							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
							snake_case_           =					tokenized_datasets.rename_column('label'     ,							'labels'   )

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

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

							# Instantiate dataloaders.
							snake_case_           =					DataLoader(
							    tokenized_datasets['train']     ,							shuffle=UpperCamelCase__     ,							collate_fn=UpperCamelCase__     ,							batch_size=UpperCamelCase__     ,							drop_last=UpperCamelCase__   )
							snake_case_           =					DataLoader(
							    tokenized_datasets['validation']     ,							shuffle=UpperCamelCase__     ,							collate_fn=UpperCamelCase__     ,							batch_size=UpperCamelCase__     ,							drop_last=(accelerator.mixed_precision == 'fp8')     ,							)

							return train_dataloader, eval_dataloader





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					Accelerator(cpu=args.cpu     ,							mixed_precision=args.mixed_precision   )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							snake_case_           =					config['lr']
							snake_case_           =					int(config['num_epochs']   )
							snake_case_           =					int(config['seed']   )
							snake_case_           =					int(config['batch_size']   )

							snake_case_           =					evaluate.load('glue'     ,							'mrpc'   )

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

							set_seed(UpperCamelCase__   )
							snake_case_       ,					snake_case_           =					get_dataloaders(UpperCamelCase__     ,							UpperCamelCase__   )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							snake_case_           =					AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,							return_dict=UpperCamelCase__   )

							# 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).
							snake_case_           =					model.to(accelerator.device   )
							# Instantiate optimizer
							snake_case_           =					AdamW(params=model.parameters()     ,							lr=UpperCamelCase__   )

							# Instantiate scheduler
							snake_case_           =					get_linear_schedule_with_warmup(
							    optimizer=UpperCamelCase__     ,							num_warmup_steps=100     ,							num_training_steps=(len(UpperCamelCase__   ) * 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.

							snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_           =					accelerator.prepare(
							    UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )

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

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

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





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					argparse.ArgumentParser(description='Simple example of training script.'   )
							parser.add_argument(
							    '--mixed_precision'     ,							type=UpperCamelCase__     ,							default=UpperCamelCase__     ,							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.'   )
							snake_case_           =					parser.parse_args()
							snake_case_           =					{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(UpperCamelCase__     ,							UpperCamelCase__   )


if __name__ == "__main__":
					main()







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

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)







@dataclass
class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Union[List[PIL.Image.Image], np.ndarray]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_if import IFPipeline
					from .pipeline_if_imgaimg import IFImgaImgPipeline
					from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
					from .pipeline_if_inpainting import IFInpaintingPipeline
					from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
					from .pipeline_if_superresolution import IFSuperResolutionPipeline
					from .safety_checker import IFSafetyChecker
					from .watermark import IFWatermarker




