






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	):
	a_         =StableDiffusionInpaintPipeline
	a_         =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	a_         =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	a_         =frozenset(
	    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	a_         =frozenset([]	)

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							torch.manual_seed(0       )
							lowerCAmelCase__       =						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=__UpperCAmelCase		,    )
							lowerCAmelCase__       =						PNDMScheduler(skip_prk_steps=__UpperCAmelCase       )
							torch.manual_seed(0       )
							lowerCAmelCase__       =						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       )
							lowerCAmelCase__       =						CLIPTextConfig(
							    bos_token_id=0		,    eos_token_id=2		,    hidden_size=32		,    intermediate_size=37		,    layer_norm_eps=1E-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    pad_token_id=1		,    vocab_size=1000		,    hidden_act="gelu"		,    projection_dim=512		,    )
							lowerCAmelCase__       =						CLIPTextModel(__UpperCAmelCase       )
							lowerCAmelCase__       =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

							lowerCAmelCase__       =						{
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "safety_checker": None,
							    "feature_extractor": None,
							}
							return components

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase=0       )->					List[str]:







							'''simple docstring'''





							lowerCAmelCase__       =						floats_tensor((1, 3, 32, 32)		,    rng=random.Random(__UpperCAmelCase       )       ).to(__UpperCAmelCase       )
							lowerCAmelCase__       =						image.cpu().permute(0		,    2		,    3		,    1       )[0]
							lowerCAmelCase__       =						Image.fromarray(np.uinta(__UpperCAmelCase       )       ).convert("RGB"       ).resize((64, 64)       )
							lowerCAmelCase__       =						Image.fromarray(np.uinta(image + 4       )       ).convert("RGB"       ).resize((64, 64)       )
							if str(__UpperCAmelCase       ).startswith("mps"       ):
													lowerCAmelCase__       =						torch.manual_seed(__UpperCAmelCase       )
							else:
													lowerCAmelCase__       =						torch.Generator(device=__UpperCAmelCase       ).manual_seed(__UpperCAmelCase       )
							lowerCAmelCase__       =						{
							    "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 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__       =						self.get_dummy_components()
							lowerCAmelCase__       =						StableDiffusionInpaintPipeline(**__UpperCAmelCase       )
							lowerCAmelCase__       =						sd_pipe.to(__UpperCAmelCase       )
							sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

							lowerCAmelCase__       =						self.get_dummy_inputs(__UpperCAmelCase       )
							lowerCAmelCase__       =						sd_pipe(**__UpperCAmelCase       ).images
							lowerCAmelCase__       =						image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							lowerCAmelCase__       =						np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]       )

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


	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							super().test_inference_batch_single_identical(expected_max_diff=3E-3       )





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

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-inpaint/init_image.png"       )
							lowerCAmelCase__       =						load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"       )
							lowerCAmelCase__       =						load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
							    "/yellow_cat_sitting_on_a_park_bench.npy"       )

							lowerCAmelCase__       =						"stabilityai/stable-diffusion-2-inpainting"
							lowerCAmelCase__       =						StableDiffusionInpaintPipeline.from_pretrained(__UpperCAmelCase		,    safety_checker=__UpperCAmelCase       )
							pipe.to(__UpperCAmelCase       )
							pipe.set_progress_bar_config(disable=__UpperCAmelCase       )
							pipe.enable_attention_slicing()

							lowerCAmelCase__       =						"Face of a yellow cat, high resolution, sitting on a park bench"

							lowerCAmelCase__       =						torch.manual_seed(0       )
							lowerCAmelCase__       =						pipe(
							    prompt=__UpperCAmelCase		,    image=__UpperCAmelCase		,    mask_image=__UpperCAmelCase		,    generator=__UpperCAmelCase		,    output_type="np"		,    )
							lowerCAmelCase__       =						output.images[0]

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

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-inpaint/init_image.png"       )
							lowerCAmelCase__       =						load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"       )
							lowerCAmelCase__       =						load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
							    "/yellow_cat_sitting_on_a_park_bench_fp16.npy"       )

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

							lowerCAmelCase__       =						"Face of a yellow cat, high resolution, sitting on a park bench"

							lowerCAmelCase__       =						torch.manual_seed(0       )
							lowerCAmelCase__       =						pipe(
							    prompt=__UpperCAmelCase		,    image=__UpperCAmelCase		,    mask_image=__UpperCAmelCase		,    generator=__UpperCAmelCase		,    output_type="np"		,    )
							lowerCAmelCase__       =						output.images[0]

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


	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase__       =						load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-inpaint/init_image.png"       )
							lowerCAmelCase__       =						load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"       )

							lowerCAmelCase__       =						"stabilityai/stable-diffusion-2-inpainting"
							lowerCAmelCase__       =						PNDMScheduler.from_pretrained(__UpperCAmelCase		,    subfolder="scheduler"       )
							lowerCAmelCase__       =						StableDiffusionInpaintPipeline.from_pretrained(
							    __UpperCAmelCase		,    safety_checker=__UpperCAmelCase		,    scheduler=__UpperCAmelCase		,    torch_dtype=torch.floataa		,    )
							pipe.to(__UpperCAmelCase       )
							pipe.set_progress_bar_config(disable=__UpperCAmelCase       )
							pipe.enable_attention_slicing(1       )
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase__       =						"Face of a yellow cat, high resolution, sitting on a park bench"

							lowerCAmelCase__       =						torch.manual_seed(0       )
							lowerCAmelCase__       =						pipe(
							    prompt=__UpperCAmelCase		,    image=__UpperCAmelCase		,    mask_image=__UpperCAmelCase		,    generator=__UpperCAmelCase		,    num_inference_steps=2		,    output_type="np"		,    )

							lowerCAmelCase__       =						torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.65 * 10**9














import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

a_							=    logging.getLogger()


def        _a  (     UpperCamelCase_					:				Union[str, Any]   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						{}
						lowerCAmelCase__       =						os.path.join(UpperCamelCase_    ,   "all_results.json"   )
						if os.path.exists(UpperCamelCase_   ):
												with open(UpperCamelCase_    ,   "r"   ) as f:
																		lowerCAmelCase__       =						json.load(UpperCamelCase_   )
						else:
												raise ValueError(F"can't find {path}"   )
						return results


a_							=    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





@require_torch_tpu
class      lowercase__     (			_UpperCAmelCase	):

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							import xla_spawn

							lowerCAmelCase__       =						self.get_auto_remove_tmp_dir()
							lowerCAmelCase__       =						F"\n            ./examples/pytorch/text-classification/run_glue.py\n            --num_cores=8\n            ./examples/pytorch/text-classification/run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --debug tpu_metrics_debug\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

							with patch.object(__UpperCAmelCase		,    "argv"		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						time()
													xla_spawn.main()
													lowerCAmelCase__       =						time()

													lowerCAmelCase__       =						get_results(__UpperCAmelCase       )
													self.assertGreaterEqual(result["eval_accuracy"]		,    0.75       )

													# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
													self.assertLess(end - start		,    500       )


	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							import xla_spawn

							lowerCAmelCase__       =						"\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            ".split()
							with patch.object(__UpperCAmelCase		,    "argv"		,    __UpperCAmelCase       ):
													xla_spawn.main()






