

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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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							:  str            =						16
lowerCAmelCase							:  Optional[Any]            =						32







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase = 16   ):
							SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AutoTokenizer.from_pretrained("bert-base-cased"   )
							SCREAMING_SNAKE_CASE_:			int  		=	load_dataset("glue"    ,     "mrpc"   )

							def tokenize_function(_UpperCAmelCase   ):
														# max_length=None => use the model max length (it's actually the default)
														SCREAMING_SNAKE_CASE_:			List[str]  		=	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():
														SCREAMING_SNAKE_CASE_:			int  		=	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
							SCREAMING_SNAKE_CASE_:			Any  		=	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.
														SCREAMING_SNAKE_CASE_:			Any  		=	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":
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	16
														elif accelerator.mixed_precision != "no":
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	8
														else:
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	None

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

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE_:			Tuple  		=	DataLoader(
							    tokenized_datasets["train"]    ,     shuffle=__UpperCAmelCase    ,     collate_fn=__UpperCAmelCase    ,     batch_size=__UpperCAmelCase   )
							SCREAMING_SNAKE_CASE_:			Tuple  		=	DataLoader(
							    tokenized_datasets["validation"]    ,     shuffle=__UpperCAmelCase    ,     collate_fn=__UpperCAmelCase    ,     batch_size=__UpperCAmelCase   )

							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							:  Any            =						mocked_dataloaders  # noqa: F811







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
							# For testing only
							if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,     __UpperCAmelCase   ) == "1":
														SCREAMING_SNAKE_CASE_:			int  		=	2
							# Initialize accelerator
							SCREAMING_SNAKE_CASE_:			str  		=	Accelerator(cpu=args.cpu    ,     mixed_precision=args.mixed_precision   )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							SCREAMING_SNAKE_CASE_:			List[str]  		=	config['''lr''']
							SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	int(config["num_epochs"]   )
							SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	int(config["seed"]   )
							SCREAMING_SNAKE_CASE_:			Optional[int]  		=	int(config["batch_size"]   )

							SCREAMING_SNAKE_CASE_:			List[str]  		=	evaluate.load("glue"    ,     "mrpc"   )

							# New Code #
							# We now can define an inner training loop function. It should take a batch size as the only parameter,
							# and build the dataloaders in there.
							# It also gets our decorator
							@find_executable_batch_size(starting_batch_size=__UpperCAmelCase   )
							def inner_training_loop(_UpperCAmelCase   ):
														# And now just move everything below under this function
														# We need to bring in the Accelerator object from earlier
														nonlocal accelerator
														# And reset all of its attributes that could hold onto any memory:
														accelerator.free_memory()

														# Then we can declare the model, optimizer, and everything else:
														set_seed(__UpperCAmelCase   )

														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	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).
														SCREAMING_SNAKE_CASE_:			str  		=	model.to(accelerator.device   )

														# Instantiate optimizer
														SCREAMING_SNAKE_CASE_:			str  		=	AdamW(params=model.parameters()    ,     lr=__UpperCAmelCase   )
														SCREAMING_SNAKE_CASE_:			Tuple  		=	get_dataloaders(__UpperCAmelCase    ,     __UpperCAmelCase   )

														# Instantiate scheduler
														SCREAMING_SNAKE_CASE_:			int  		=	get_linear_schedule_with_warmup(
														    optimizer=__UpperCAmelCase    ,     num_warmup_steps=1_00    ,     num_training_steps=(len(__UpperCAmelCase   ) * 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.
														SCREAMING_SNAKE_CASE_:			Any  		=	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   )
																												SCREAMING_SNAKE_CASE_:			List[Any]  		=	model(**__UpperCAmelCase   )
																												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	outputs.loss
																												accelerator.backward(__UpperCAmelCase   )
																												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():
																																			SCREAMING_SNAKE_CASE_:			Tuple  		=	model(**__UpperCAmelCase   )
																												SCREAMING_SNAKE_CASE_:			int  		=	outputs.logits.argmax(dim=-1   )
																												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	accelerator.gather_for_metrics((predictions, batch["labels"])   )
																												metric.add_batch(
																												    predictions=__UpperCAmelCase    ,     references=__UpperCAmelCase    ,     )

																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(f"epoch {epoch}:"    ,     __UpperCAmelCase   )

    # 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       A_				(   ):
							SCREAMING_SNAKE_CASE_:			List[str]  		=	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."   )
							SCREAMING_SNAKE_CASE_:			int  		=	parser.parse_args()
							SCREAMING_SNAKE_CASE_:			str  		=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(__UpperCAmelCase    ,     __UpperCAmelCase   )


if __name__ == "__main__":
						main()









import torch

from diffusers import StableDiffusionPipeline


lowerCAmelCase							:  Any            =						"""path-to-your-trained-model"""
lowerCAmelCase							:  int            =						StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

lowerCAmelCase							:  Union[str, Any]            =						"""A photo of sks dog in a bucket"""
lowerCAmelCase							:  Any            =						pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






