


"""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, 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
#
########################################################################


a_       							=    16
a_       							=    32






def      a__ (				__lowercase     ,			__lowercase = 16   )	->	Tuple:
				_A					  =			AutoTokenizer.from_pretrained("bert-base-cased"   )
				_A					  =			load_dataset("glue"     ,			"mrpc"   )

				def tokenize_function(__lowercase   ):
								# max_length=None => use the model max length (it's actually the default)
								_A					  =			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():
								_A					  =			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
				_A					  =			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.
								_A					  =			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":
												_A					  =			16
								elif accelerator.mixed_precision != "no":
												_A					  =			8
								else:
												_A					  =			None

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

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

		a_       							=    mocked_dataloaders  # noqa: F811






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

				_A					  =			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=__lowercase   )
				def inner_training_loop(__lowercase   ):
								# 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(__lowercase   )

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

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

								# Instantiate scheduler
								_A					  =			get_linear_schedule_with_warmup(
								    optimizer=__lowercase     ,			num_warmup_steps=100     ,			num_training_steps=(len(__lowercase   ) * 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.
								_A  ,		_A  ,		_A  ,		_A  ,		_A					  =			accelerator.prepare(
								    __lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )

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

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

												_A					  =			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}:"""     ,			__lowercase   )

    # 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__ (				)	->	int:
				_A					  =			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."   )
				_A					  =			parser.parse_args()
				_A					  =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(__lowercase     ,			__lowercase   )


if __name__ == "__main__":
		main()



"""simple docstring"""


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_       							=    False
class snake_case      (  unittest.TestCase):
		pass





@nightly
@require_torch_gpu
class snake_case      (  unittest.TestCase):







		def  a_ (					self      :       Optional[int]		)       ->						str:





						'''simple docstring'''







						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







		def  a_ (					self      :       Tuple		)       ->						Any:





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt="first prompt"    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"    ,   ).images

						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(a__		)
										_A					  =			VersatileDiffusionPipeline.from_pretrained(a__    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			generator.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt="first prompt"    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"    ,   ).images

						assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"







		def  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			"cyberpunk 2077"
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt=a__    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"    ,   ).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]		)

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

						_A					  =			"A painting of a squirrel eating a burger "
						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.text_to_image(
						    prompt=a__    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"		).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]		)

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

						_A					  =			pipe.image_variation(a__    ,   generator=a__    ,   output_type="numpy"		).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]		)

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