
"""simple docstring"""







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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


_SCREAMING_SNAKE_CASE	       =      16
_SCREAMING_SNAKE_CASE	       =      32
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 16 )  ->     List[str]:







							"""simple docstring"""


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

							def tokenize_function(SCREAMING_SNAKE_CASE ):
														# max_length=None => use the model max length (it's actually the default)
														__snake_case     			=       tokenizer(examples["sentence1"]							,    examples["sentence2"]							,    truncation=SCREAMING_SNAKE_CASE							,    max_length=SCREAMING_SNAKE_CASE )
														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(
														    SCREAMING_SNAKE_CASE							,    batched=SCREAMING_SNAKE_CASE							,    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(SCREAMING_SNAKE_CASE ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														__snake_case     			=       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":
																					__snake_case     			=       16
														elif accelerator.mixed_precision != "no":
																					__snake_case     			=       8
														else:
																					__snake_case     			=       None

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

							# Instantiate dataloaders.
							__snake_case     			=       DataLoader(
							    tokenized_datasets["train"]							,    shuffle=SCREAMING_SNAKE_CASE							,    collate_fn=SCREAMING_SNAKE_CASE							,    batch_size=SCREAMING_SNAKE_CASE )
							__snake_case     			=       DataLoader(
							    tokenized_datasets["validation"]							,    shuffle=SCREAMING_SNAKE_CASE							,    collate_fn=SCREAMING_SNAKE_CASE							,    batch_size=SCREAMING_SNAKE_CASE )

							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

		_SCREAMING_SNAKE_CASE	       =      mocked_dataloaders  # noqa: F811
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Union[str, Any]:







							"""simple docstring"""


							if os.environ.get("TESTING_MOCKED_DATALOADERS"							,    SCREAMING_SNAKE_CASE ) == "1":
														__snake_case     			=       2
							# Initialize accelerator
							__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(SCREAMING_SNAKE_CASE )
							__snake_case			,   __snake_case     			=       get_dataloaders(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							# 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=SCREAMING_SNAKE_CASE )

							# 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=SCREAMING_SNAKE_CASE )

							# Instantiate scheduler
							__snake_case     			=       get_linear_schedule_with_warmup(
							    optimizer=SCREAMING_SNAKE_CASE							,    num_warmup_steps=1_00							,    num_training_steps=(len(SCREAMING_SNAKE_CASE ) * 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(
							    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

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

														model.eval()
														__snake_case     			=       0
														for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
																					# 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(**SCREAMING_SNAKE_CASE )
																					__snake_case     			=       outputs.logits.argmax(dim=-1 )
																					__snake_case			,   __snake_case     			=       accelerator.gather((predictions, batch["labels"]) )
																					# New Code #
																					# First we check if it's a distributed system
																					if accelerator.use_distributed:
																												# Then see if we're on the last batch of our eval dataloader
																												if step == len(SCREAMING_SNAKE_CASE ) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			__snake_case     			=       predictions[: len(eval_dataloader.dataset ) - samples_seen]
																																			__snake_case     			=       references[: len(eval_dataloader.dataset ) - samples_seen]
																												else:
																																			# Otherwise we add the number of samples seen
																																			samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																					metric.add_batch(
																					    predictions=SCREAMING_SNAKE_CASE							,    references=SCREAMING_SNAKE_CASE							,    )

														__snake_case     			=       metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}:'''							,    SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					)  ->     List[str]:







							"""simple docstring"""


							__snake_case     			=       argparse.ArgumentParser(description="Simple example of training script." )
							parser.add_argument(
							    "--mixed_precision"							,    type=SCREAMING_SNAKE_CASE							,    default=SCREAMING_SNAKE_CASE							,    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(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		main()


"""simple docstring"""







import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
		import onnxruntime as ort







@nightly
@require_onnxruntime
@require_torch_gpu
class 							__magic_name__     (		unittest.TestCase   ):





					@property
					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       ort.SessionOptions()
												__snake_case     			=       False
												return options





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/in_paint/overture-creations-5sI6fQgYIuo.png"    )
												__snake_case     			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"    )
												__snake_case     			=       load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"    )

												# using the PNDM scheduler by default
												__snake_case     			=       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
												    "CompVis/stable-diffusion-v1-4"							,						revision="onnx"							,						safety_checker=snake_case_							,						feature_extractor=snake_case_							,						provider=self.gpu_provider							,						sess_options=self.gpu_options							,						)
												pipe.set_progress_bar_config(disable=snake_case_    )

												__snake_case     			=       "A red cat sitting on a park bench"

												__snake_case     			=       np.random.RandomState(0    )
												__snake_case     			=       pipe(
												    prompt=snake_case_							,						image=snake_case_							,						mask_image=snake_case_							,						strength=0.75							,						guidance_scale=7.5							,						num_inference_steps=15							,						generator=snake_case_							,						output_type="np"							,						)

												__snake_case     			=       output.images[0]

												assert image.shape == (512, 512, 3)
												assert np.abs(expected_image - image    ).max() < 1e-2
