



'''simple docstring'''



import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase_  :							int             =    16
lowerCAmelCase_  :							Optional[Any]             =    32
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					Optional[int] = 16       ,		A	:					int = "bert-base-cased"					):
							SCREAMING_SNAKE_CASE       :   List[Any]         =      AutoTokenizer.from_pretrained(_a					)
							SCREAMING_SNAKE_CASE       :   str         =      load_dataset('''glue'''       ,		'''mrpc'''					)

							def tokenize_function(A	:					str					):
														# max_length=None => use the model max length (it's actually the default)
														SCREAMING_SNAKE_CASE       :   Any         =      tokenizer(examples['''sentence1''']       ,		examples['''sentence2''']       ,		truncation=_a       ,		max_length=_a					)
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      datasets.map(
							    _a       ,		batched=_a       ,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,		load_from_cache_file=_a					)

							# 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       :   Union[str, Any]         =      tokenized_datasets.rename_column('''label'''       ,		'''labels'''					)

							def collate_fn(A	:					Tuple					):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(_a       ,		padding='''max_length'''       ,		max_length=128       ,		return_tensors='''pt'''					)
														return tokenizer.pad(_a       ,		padding='''longest'''       ,		return_tensors='''pt'''					)

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE       :   Any         =      DataLoader(
							    tokenized_datasets['''train''']       ,		shuffle=_a       ,		collate_fn=_a       ,		batch_size=_a					)
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      DataLoader(
							    tokenized_datasets['''validation''']       ,		shuffle=_a       ,		collate_fn=_a       ,		batch_size=_a					)

							return train_dataloader, eval_dataloader
def        UpperCAmelCase (    A	:					Dict       ,		A	:					int					):
							# Initialize accelerator
							SCREAMING_SNAKE_CASE       :   Dict         =      Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							SCREAMING_SNAKE_CASE       :   int         =      config["lr"]
							SCREAMING_SNAKE_CASE       :   Any         =      int(config['''num_epochs''']					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      int(config['''seed''']					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      int(config['''batch_size''']					)
							SCREAMING_SNAKE_CASE       :   Dict         =      args.model_name_or_path

							set_seed(_a					)
							SCREAMING_SNAKE_CASE       :   str         =      get_dataloaders(_a       ,		_a       ,		_a					)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							SCREAMING_SNAKE_CASE       :   Any         =      AutoModelForSequenceClassification.from_pretrained(_a       ,		return_dict=_a					)

							# Instantiate optimizer
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							SCREAMING_SNAKE_CASE       :   Dict         =      optimizer_cls(params=model.parameters()       ,		lr=_a					)

							if accelerator.state.deepspeed_plugin is not None:
														SCREAMING_SNAKE_CASE       :   Tuple         =      accelerator.state.deepspeed_plugin.deepspeed_config[
														    "gradient_accumulation_steps"
														]
							else:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      1
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      (len(_a					) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      get_linear_schedule_with_warmup(
														    optimizer=_a       ,		num_warmup_steps=0       ,		num_training_steps=_a       ,		)
							else:
														SCREAMING_SNAKE_CASE       :   Dict         =      DummyScheduler(_a       ,		total_num_steps=_a       ,		warmup_num_steps=0					)

							# 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       :   Dict         =      accelerator.prepare(
							    _a       ,		_a       ,		_a       ,		_a       ,		_a					)

							# We need to keep track of how many total steps we have iterated over
							SCREAMING_SNAKE_CASE       :   Dict         =      0
							# We also need to keep track of the stating epoch so files are named properly
							SCREAMING_SNAKE_CASE       :   Any         =      0

							# Now we train the model
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      evaluate.load('''glue'''       ,		'''mrpc'''					)
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      0
							SCREAMING_SNAKE_CASE       :   str         =      {}
							for epoch in range(_a       ,		_a					):
														model.train()
														for step, batch in enumerate(_a					):
																					SCREAMING_SNAKE_CASE       :   Dict         =      model(**_a					)
																					SCREAMING_SNAKE_CASE       :   int         =      outputs.loss
																					SCREAMING_SNAKE_CASE       :   Tuple         =      loss / gradient_accumulation_steps
																					accelerator.backward(_a					)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1

														model.eval()
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      0
														for step, batch in enumerate(_a					):
																					# 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       :   List[str]         =      model(**_a					)
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      outputs.logits.argmax(dim=-1					)
																					# It is slightly faster to call this once, than multiple times
																					SCREAMING_SNAKE_CASE       :   List[str]         =      accelerator.gather(
																					    (predictions, batch['''labels'''])					)  # If we are in a multiprocess environment, the last batch has duplicates
																					if accelerator.use_distributed:
																												if step == len(_a					) - 1:
																																			SCREAMING_SNAKE_CASE       :   str         =      predictions[: len(eval_dataloader.dataset					) - samples_seen]
																																			SCREAMING_SNAKE_CASE       :   Tuple         =      references[: len(eval_dataloader.dataset					) - samples_seen]
																												else:
																																			samples_seen += references.shape[0]
																					metric.add_batch(
																					    predictions=_a       ,		references=_a       ,		)

														SCREAMING_SNAKE_CASE       :   Tuple         =      metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"""epoch {epoch}:"""       ,		_a					)
														SCREAMING_SNAKE_CASE       :   Tuple         =      eval_metric["accuracy"]

														if best_performance < eval_metric["accuracy"]:
																					SCREAMING_SNAKE_CASE       :   str         =      eval_metric["accuracy"]

							if args.performance_lower_bound is not None:
														assert (
														    args.performance_lower_bound <= best_performance
														), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

							accelerator.wait_for_everyone()
							if accelerator.is_main_process:
														with open(os.path.join(args.output_dir       ,		'''all_results.json'''					)       ,		'''w'''					) as f:
																					json.dump(_a       ,		_a					)
def        UpperCAmelCase (    ):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''					)
							parser.add_argument(
							    '''--model_name_or_path'''       ,		type=_a       ,		default='''bert-base-cased'''       ,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,		required=_a       ,		)
							parser.add_argument(
							    '''--output_dir'''       ,		type=_a       ,		default='''.'''       ,		help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''       ,		)
							parser.add_argument(
							    '''--performance_lower_bound'''       ,		type=_a       ,		default=_a       ,		help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''       ,		)
							parser.add_argument(
							    '''--num_epochs'''       ,		type=_a       ,		default=3       ,		help='''Number of train epochs.'''       ,		)
							SCREAMING_SNAKE_CASE       :   str         =      parser.parse_args()
							SCREAMING_SNAKE_CASE       :   List[Any]         =      {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
							training_function(_a       ,		_a					)


if __name__ == "__main__":
							main()







import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
			import onnxruntime as ort






class _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			pass






@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[str]:

				"""simple docstring"""

				return (
				    "CUDAExecutionProvider",
				    {
				        "gpu_mem_limit": "15000000000",  # 15GB
				        "arena_extend_strategy": "kSameAsRequested",
				    },
				)
			@property
			def 				__UpperCamelCase			(      self				:			int						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							ort.SessionOptions()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							False
				return options
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   int					  =							OnnxStableDiffusionInpaintPipeline.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   revision="onnx"   ,   safety_checker=a   ,   feature_extractor=a   ,   provider=self.gpu_provider   ,   sess_options=self.gpu_options   ,   )
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							"A red cat sitting on a park bench"

				SCREAMING_SNAKE_CASE :   Tuple					  =							np.random.RandomState(0						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   guidance_scale=7.5   ,   num_inference_steps=10   ,   generator=a   ,   output_type="np"   ,   )
				SCREAMING_SNAKE_CASE :   List[Any]					  =							output.images
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3






			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							LMSDiscreteScheduler.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   subfolder="scheduler"   ,   revision="onnx"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							OnnxStableDiffusionInpaintPipeline.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   revision="onnx"   ,   scheduler=a   ,   safety_checker=a   ,   feature_extractor=a   ,   provider=self.gpu_provider   ,   sess_options=self.gpu_options   ,   )
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							"A red cat sitting on a park bench"

				SCREAMING_SNAKE_CASE :   Dict					  =							np.random.RandomState(0						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   guidance_scale=7.5   ,   num_inference_steps=20   ,   generator=a   ,   output_type="np"   ,   )
				SCREAMING_SNAKE_CASE :   List[str]					  =							output.images
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Any					  =							np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3