
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 experiment tracking 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
#
########################################################################

UpperCamelCase_   	=	1_6
UpperCamelCase_   	=	3_2






def 		_UpperCAmelCase   (	UpperCamelCase:     Accelerator			,							UpperCamelCase:     int = 1_6  ):






	"""simple docstring"""


	__lowerCAmelCase         =							AutoTokenizer.from_pretrained("bert-base-cased"  )
	__lowerCAmelCase         =							load_dataset("glue"			,							"mrpc"  )

	def tokenize_function(UpperCamelCase:     List[str]  ):
		# max_length=None => use the model max length (it's actually the default)
		__lowerCAmelCase         =							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():
		__lowerCAmelCase         =							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
	__lowerCAmelCase         =							tokenized_datasets.rename_column("label"			,							"labels"  )

	def collate_fn(UpperCamelCase:     Tuple  ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__lowerCAmelCase         =							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":
			__lowerCAmelCase         =							1_6
		elif accelerator.mixed_precision != "no":
			__lowerCAmelCase         =							8
		else:
			__lowerCAmelCase         =							None

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

	# Instantiate dataloaders.
	__lowerCAmelCase         =							DataLoader(
	    tokenized_datasets["train"]			,							shuffle=UpperCamelCase			,							collate_fn=UpperCamelCase			,							batch_size=UpperCamelCase  )
	__lowerCAmelCase         =							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

					UpperCamelCase_   	=	mocked_dataloaders  # noqa: F811






def 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     Optional[Any]  ):






	"""simple docstring"""


	if os.environ.get("TESTING_MOCKED_DATALOADERS"			,							UpperCamelCase  ) == "1":
		__lowerCAmelCase         =							2
	# Initialize Accelerator

	# New Code #
	# We pass in "all" to `log_with` to grab all available trackers in the environment
	# Note: If using a custom `Tracker` class, should be passed in here such as:
	# >>> log_with = ["all", MyCustomTrackerClassInstance()]
	if args.with_tracking:
		__lowerCAmelCase         =							Accelerator(
		    cpu=args.cpu			,							mixed_precision=args.mixed_precision			,							log_with="all"			,							project_dir=args.project_dir  )
	else:
		__lowerCAmelCase         =							Accelerator(cpu=args.cpu			,							mixed_precision=args.mixed_precision  )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__lowerCAmelCase         =							config["lr"]
	__lowerCAmelCase         =							int(config["num_epochs"]  )
	__lowerCAmelCase         =							int(config["seed"]  )
	__lowerCAmelCase         =							int(config["batch_size"]  )
	set_seed(UpperCamelCase  )

	__lowerCAmelCase   ,						__lowerCAmelCase         =							get_dataloaders(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							evaluate.load("glue"			,							"mrpc"  )

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

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

	# Instantiate optimizer
	__lowerCAmelCase         =							AdamW(params=model.parameters()			,							lr=UpperCamelCase  )

	# Instantiate scheduler
	__lowerCAmelCase         =							get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase			,							num_warmup_steps=1_0_0			,							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.
	__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							accelerator.prepare(
	    UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )

	# New Code #
	# We need to initialize the trackers we use. Overall configurations can also be stored
	if args.with_tracking:
		__lowerCAmelCase         =							os.path.split(UpperCamelCase  )[-1].split("."  )[0]
		accelerator.init_trackers(UpperCamelCase			,							UpperCamelCase  )

	# Now we train the model
	for epoch in range(UpperCamelCase  ):
		model.train()
		# New Code #
		# For our tracking example, we will log the total loss of each epoch
		if args.with_tracking:
			__lowerCAmelCase         =							0
		for step, batch in enumerate(UpperCamelCase  ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device  )
			__lowerCAmelCase         =							model(**UpperCamelCase  )
			__lowerCAmelCase         =							outputs.loss
			# New Code #
			if args.with_tracking:
				total_loss += loss.detach().float()
			__lowerCAmelCase         =							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` (the default).
			batch.to(accelerator.device  )
			with torch.no_grad():
				__lowerCAmelCase         =							model(**UpperCamelCase  )
			__lowerCAmelCase         =							outputs.logits.argmax(dim=-1  )
			__lowerCAmelCase   ,						__lowerCAmelCase         =							accelerator.gather_for_metrics((predictions, batch["labels"])  )
			metric.add_batch(
			    predictions=UpperCamelCase			,							references=UpperCamelCase			,							)

		__lowerCAmelCase         =							metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"epoch {epoch}:"			,							UpperCamelCase  )

		# New Code #
		# To actually log, we call `Accelerator.log`
		# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
		if args.with_tracking:
			accelerator.log(
			    {
			        "accuracy": eval_metric["accuracy"],
			        "f1": eval_metric["f1"],
			        "train_loss": total_loss.item() / len(UpperCamelCase  ),
			        "epoch": epoch,
			    }			,							step=UpperCamelCase			,							)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
	if args.with_tracking:
		accelerator.end_training()






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	__lowerCAmelCase         =							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."  )
	parser.add_argument(
	    "--with_tracking"			,							action="store_true"			,							help="Whether to load in all available experiment trackers from the environment and use them for logging."			,							)
	parser.add_argument(
	    "--project_dir"			,							type=UpperCamelCase			,							default="logs"			,							help="Location on where to store experiment tracking logs` and relevent project information"			,							)
	__lowerCAmelCase         =							parser.parse_args()
	__lowerCAmelCase         =							{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
	training_function(UpperCamelCase			,							UpperCamelCase  )


if __name__ == "__main__":
					main()








import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

@slow
@require_torch_gpu
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe(
								    image=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )

								assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






