
import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


UpperCAmelCase       =					'''true'''






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=82					,				__SCREAMING_SNAKE_CASE=16				):
	set_seed(42				)
	lowercase	    =       RegressionModel()
	lowercase	    =       deepcopy(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       RegressionDataset(length=__SCREAMING_SNAKE_CASE				)
	lowercase	    =       DataLoader(__SCREAMING_SNAKE_CASE					,				batch_size=__SCREAMING_SNAKE_CASE				)
	model.to(accelerator.device				)
	lowercase    ,   lowercase	    =       accelerator.prepare(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	return model, ddp_model, dataloader






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=False				):
	lowercase	    =       AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'				)
	lowercase	    =       load_dataset('glue'					,				'mrpc'					,				split='validation'				)

	def tokenize_function(__SCREAMING_SNAKE_CASE				):
		lowercase	    =       tokenizer(examples['sentence1']					,				examples['sentence2']					,				truncation=__SCREAMING_SNAKE_CASE					,				max_length=__SCREAMING_SNAKE_CASE				)
		return outputs

	with accelerator.main_process_first():
		lowercase	    =       dataset.map(
		    __SCREAMING_SNAKE_CASE					,				batched=__SCREAMING_SNAKE_CASE					,				remove_columns=['idx', 'sentence1', 'sentence2']					,				)

	lowercase	    =       tokenized_datasets.rename_column('label'					,				'labels'				)

	def collate_fn(__SCREAMING_SNAKE_CASE				):
		if use_longest:
			return tokenizer.pad(__SCREAMING_SNAKE_CASE					,				padding='longest'					,				return_tensors='pt'				)
		return tokenizer.pad(__SCREAMING_SNAKE_CASE					,				padding='max_length'					,				max_length=128					,				return_tensors='pt'				)

	return DataLoader(__SCREAMING_SNAKE_CASE					,				shuffle=__SCREAMING_SNAKE_CASE					,				collate_fn=__SCREAMING_SNAKE_CASE					,				batch_size=16				)






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       Accelerator(dispatch_batches=__SCREAMING_SNAKE_CASE					,				split_batches=__SCREAMING_SNAKE_CASE				)
	lowercase	    =       get_dataloader(__SCREAMING_SNAKE_CASE					,				not dispatch_batches				)
	lowercase	    =       AutoModelForSequenceClassification.from_pretrained(
	    'hf-internal-testing/mrpc-bert-base-cased'					,				return_dict=__SCREAMING_SNAKE_CASE				)
	lowercase    ,   lowercase	    =       accelerator.prepare(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       []
	for batch in dataloader:
		lowercase    ,   lowercase	    =       batch.values()
		with torch.no_grad():
			lowercase	    =       model(__SCREAMING_SNAKE_CASE				)
			lowercase    ,   lowercase	    =       accelerator.gather_for_metrics((logit, target)				)
			logits_and_targets.append((logit, target)				)
	lowercase    ,   lowercase	    =       [], []
	for logit, targ in logits_and_targets:
		logits.append(__SCREAMING_SNAKE_CASE				)
		targs.append(__SCREAMING_SNAKE_CASE				)
	lowercase    ,   lowercase	    =       torch.cat(__SCREAMING_SNAKE_CASE				), torch.cat(__SCREAMING_SNAKE_CASE				)
	return logits, targs






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=82					,				__SCREAMING_SNAKE_CASE=False					,				__SCREAMING_SNAKE_CASE=False					,				__SCREAMING_SNAKE_CASE=16				):
	lowercase    ,   lowercase    ,   lowercase	    =       get_basic_setup(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	lowercase    ,   lowercase	    =       generate_predictions(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	assert (
	    len(__SCREAMING_SNAKE_CASE				) == num_samples
	), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__SCREAMING_SNAKE_CASE				)}'''






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE = False					,				__SCREAMING_SNAKE_CASE = False				):
	lowercase	    =       evaluate.load('glue'					,				'mrpc'				)
	lowercase    ,   lowercase	    =       get_mrpc_setup(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	# First do baseline
	lowercase    ,   lowercase    ,   lowercase	    =       setup['no']
	model.to(__SCREAMING_SNAKE_CASE				)
	model.eval()
	for batch in dataloader:
		batch.to(__SCREAMING_SNAKE_CASE				)
		with torch.inference_mode():
			lowercase	    =       model(**__SCREAMING_SNAKE_CASE				)
		lowercase	    =       outputs.logits.argmax(dim=-1				)
		metric.add_batch(predictions=__SCREAMING_SNAKE_CASE					,				references=batch['labels']				)
	lowercase	    =       metric.compute()

	# Then do distributed
	lowercase    ,   lowercase    ,   lowercase	    =       setup['ddp']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			lowercase	    =       model(**__SCREAMING_SNAKE_CASE				)
		lowercase	    =       outputs.logits.argmax(dim=-1				)
		lowercase	    =       batch['labels']
		lowercase    ,   lowercase	    =       accelerator.gather_for_metrics((preds, references)				)
		metric.add_batch(predictions=__SCREAMING_SNAKE_CASE					,				references=__SCREAMING_SNAKE_CASE				)
	lowercase	    =       metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]					,				distributed[key]				), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''






def 				UpperCAmelCase_						(				):
	lowercase	    =       Accelerator(split_batches=__SCREAMING_SNAKE_CASE					,				dispatch_batches=__SCREAMING_SNAKE_CASE				)
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print('**Testing gather_for_metrics**'				)
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''				)
				test_mrpc(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test torch metrics**'				)
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			lowercase	    =       Accelerator(split_batches=__SCREAMING_SNAKE_CASE					,				dispatch_batches=__SCREAMING_SNAKE_CASE				)
			if accelerator.is_local_main_process:
				print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''				)
			test_torch_metrics(__SCREAMING_SNAKE_CASE					,				99				)
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test last batch is not dropped when perfectly divisible**'				)
	lowercase	    =       Accelerator()
	test_torch_metrics(__SCREAMING_SNAKE_CASE					,				512				)
	accelerator.state._reset_state()






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()



"""simple docstring"""





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 __lowerCAmelCase					(       _lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			pass





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




			"""simple docstring"""







			@property
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)




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







								"""simple docstring"""
								A_								=    ort.SessionOptions()
								A_								=    False
								return options




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, 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						)

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

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=10							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]						)

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






			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Tuple:







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    LMSDiscreteScheduler.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, subfolder="scheduler"							, revision="onnx"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, scheduler=_snake_case							, 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						)

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

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=20							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]						)

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




