

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


lowerCAmelCase							:  Any            =						"""true"""







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=82    ,     _UpperCAmelCase=16   ):
						set_seed(42   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	RegressionModel()
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	deepcopy(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			int  		=	RegressionDataset(length=_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	DataLoader(_UpperCAmelCase    ,     batch_size=_UpperCAmelCase   )
						model.to(accelerator.device   )
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			str  		=	accelerator.prepare(_UpperCAmelCase    ,     _UpperCAmelCase   )
						return model, ddp_model, dataloader







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	load_dataset("glue"    ,     "mrpc"    ,     split="validation"   )

						def tokenize_function(_UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer(examples["sentence1"]    ,     examples["sentence2"]    ,     truncation=_UpperCAmelCase    ,     max_length=_UpperCAmelCase   )
												return outputs

						with accelerator.main_process_first():
												SCREAMING_SNAKE_CASE_:			List[str]  		=	dataset.map(
												    _UpperCAmelCase    ,     batched=_UpperCAmelCase    ,     remove_columns=["idx", "sentence1", "sentence2"]    ,     )

						SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenized_datasets.rename_column("label"    ,     "labels"   )

						def collate_fn(_UpperCAmelCase   ):
												if use_longest:
																		return tokenizer.pad(_UpperCAmelCase    ,     padding="longest"    ,     return_tensors="pt"   )
												return tokenizer.pad(_UpperCAmelCase    ,     padding="max_length"    ,     max_length=1_28    ,     return_tensors="pt"   )

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







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







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	[]
						for batch in dataloader:
												SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	batch.values()
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE_:			Optional[int]  		=	model(_UpperCAmelCase   )
																		SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[Any]  		=	accelerator.gather_for_metrics((logit, target)   )
																		logits_and_targets.append((logit, target)   )
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	[], []
						for logit, targ in logits_and_targets:
												logits.append(_UpperCAmelCase   )
												targs.append(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.cat(_UpperCAmelCase   ), torch.cat(_UpperCAmelCase   )
						return logits, targs







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=82    ,     _UpperCAmelCase=False    ,     _UpperCAmelCase=False    ,     _UpperCAmelCase=16   ):
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Dict  		=	get_basic_setup(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[int]  		=	generate_predictions(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
						assert (
						    len(_UpperCAmelCase   ) == num_samples
						), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCAmelCase   )}"







def       A_				(   _UpperCAmelCase = False    ,     _UpperCAmelCase = False   ):
						SCREAMING_SNAKE_CASE_:			int  		=	evaluate.load("glue"    ,     "mrpc"   )
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[int]  		=	get_mrpc_setup(_UpperCAmelCase    ,     _UpperCAmelCase   )
						# First do baseline
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	setup["no"]
						model.to(_UpperCAmelCase   )
						model.eval()
						for batch in dataloader:
												batch.to(_UpperCAmelCase   )
												with torch.inference_mode():
																		SCREAMING_SNAKE_CASE_:			Tuple  		=	model(**_UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_:			int  		=	outputs.logits.argmax(dim=-1   )
												metric.add_batch(predictions=_UpperCAmelCase    ,     references=batch["labels"]   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	metric.compute()

						# Then do distributed
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	setup["ddp"]
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	model(**_UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_:			Dict  		=	outputs.logits.argmax(dim=-1   )
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	batch["labels"]
												SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	accelerator.gather_for_metrics((preds, references)   )
												metric.add_batch(predictions=_UpperCAmelCase    ,     references=_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Any  		=	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       A_				(   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	Accelerator(split_batches=_UpperCAmelCase    ,     dispatch_batches=_UpperCAmelCase   )
						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(_UpperCAmelCase    ,     _UpperCAmelCase   )
																								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]:
																		SCREAMING_SNAKE_CASE_:			Any  		=	Accelerator(split_batches=_UpperCAmelCase    ,     dispatch_batches=_UpperCAmelCase   )
																		if accelerator.is_local_main_process:
																								print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"   )
																		test_torch_metrics(_UpperCAmelCase    ,     99   )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print("**Test last batch is not dropped when perfectly divisible**"   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	Accelerator()
						test_torch_metrics(_UpperCAmelCase    ,     5_12   )
						accelerator.state._reset_state()







def       A_				(   _UpperCAmelCase   ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Dict      							=					StableDiffusionInpaintPipeline
			_UpperCAmelCase       :							Tuple      							=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_UpperCAmelCase       :							List[str]      							=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCAmelCase       :							Tuple      							=					frozenset(
			    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			_UpperCAmelCase       :							Optional[int]      							=					frozenset([]     )



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	UNetaDConditionModel(
									    block_out_channels=(32, 64)       ,						layers_per_block=2       ,						sample_size=32       ,						in_channels=9       ,						out_channels=4       ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,						cross_attention_dim=32       ,						attention_head_dim=(2, 4)       ,						use_linear_projection=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	PNDMScheduler(skip_prk_steps=lowerCAmelCase__)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AutoencoderKL(
									    block_out_channels=[32, 64]       ,						in_channels=3       ,						out_channels=3       ,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,						latent_channels=4       ,						sample_size=128       ,						)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTextConfig(
									    bos_token_id=0       ,						eos_token_id=2       ,						hidden_size=32       ,						intermediate_size=37       ,						layer_norm_eps=1E-05       ,						num_attention_heads=4       ,						num_hidden_layers=5       ,						pad_token_id=1       ,						vocab_size=1000       ,						hidden_act="gelu"       ,						projection_dim=512       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTextModel(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	{
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Optional[int]=0):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									SCREAMING_SNAKE_CASE_:			Dict  		=	floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	image.cpu().permute(0       ,						2       ,						3       ,						1)[0]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	Image.fromarray(np.uinta(lowerCAmelCase__)).convert("RGB").resize((64, 64))
									SCREAMING_SNAKE_CASE_:			List[str]  		=	Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			Any  		=	torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	{
									    "prompt": "A painting of a squirrel eating a burger",
									    "image": init_image,
									    "mask_image": mask_image,
									    "generator": generator,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	"cpu"  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE_:			int  		=	self.get_dummy_components()
									SCREAMING_SNAKE_CASE_:			int  		=	StableDiffusionInpaintPipeline(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	sd_pipe.to(lowerCAmelCase__)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Any  		=	self.get_dummy_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	sd_pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Dict  		=	image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])

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




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			int  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench.npy")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Any  		=	StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase__       ,						safety_checker=lowerCAmelCase__)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_:			str  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image).max() < 9E-3



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_:			Any  		=	load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench_fp16.npy")

									SCREAMING_SNAKE_CASE_:			str  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Dict  		=	StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCAmelCase__       ,						torch_dtype=torch.floataa       ,						safety_checker=lowerCAmelCase__       ,						)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Dict  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Any  		=	output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image).max() < 5E-1




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									SCREAMING_SNAKE_CASE_:			Tuple  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	PNDMScheduler.from_pretrained(lowerCAmelCase__       ,						subfolder="scheduler")
									SCREAMING_SNAKE_CASE_:			Any  		=	StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCAmelCase__       ,						safety_checker=lowerCAmelCase__       ,						scheduler=lowerCAmelCase__       ,						torch_dtype=torch.floataa       ,						)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing(1)
									pipe.enable_sequential_cpu_offload()

									SCREAMING_SNAKE_CASE_:			Any  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Any  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						num_inference_steps=2       ,						output_type="np"       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9






