






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__     (			lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					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       )->					List[Any]:







       '''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=_SCREAMING_SNAKE_CASE		,    )
       lowerCAmelCase__       =						PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE       )
       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(_SCREAMING_SNAKE_CASE       )
       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       )->					str:







       '''simple docstring'''





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







       '''simple docstring'''





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

       lowerCAmelCase__       =						self.get_dummy_inputs(_SCREAMING_SNAKE_CASE       )
       lowerCAmelCase__       =						sd_pipe(**_SCREAMING_SNAKE_CASE       ).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       )->					Tuple:







       '''simple docstring'''





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





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

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







       '''simple docstring'''





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

 def 		UpperCAmelCase  (							self       )->					List[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.npy"       )

       lowerCAmelCase__       =						"stabilityai/stable-diffusion-2-inpainting"
       lowerCAmelCase__       =						StableDiffusionInpaintPipeline.from_pretrained(_SCREAMING_SNAKE_CASE		,    safety_checker=_SCREAMING_SNAKE_CASE       )
       pipe.to(_SCREAMING_SNAKE_CASE       )
       pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE       )
       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=_SCREAMING_SNAKE_CASE		,    image=_SCREAMING_SNAKE_CASE		,    mask_image=_SCREAMING_SNAKE_CASE		,    generator=_SCREAMING_SNAKE_CASE		,    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[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_fp16.npy"       )

       lowerCAmelCase__       =						"stabilityai/stable-diffusion-2-inpainting"
       lowerCAmelCase__       =						StableDiffusionInpaintPipeline.from_pretrained(
           _SCREAMING_SNAKE_CASE		,    torch_dtype=torch.floataa		,    safety_checker=_SCREAMING_SNAKE_CASE		,    )
       pipe.to(_SCREAMING_SNAKE_CASE       )
       pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE       )
       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=_SCREAMING_SNAKE_CASE		,    image=_SCREAMING_SNAKE_CASE		,    mask_image=_SCREAMING_SNAKE_CASE		,    generator=_SCREAMING_SNAKE_CASE		,    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       )->					Optional[int]:







       '''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(_SCREAMING_SNAKE_CASE		,    subfolder="scheduler"       )
       lowerCAmelCase__       =						StableDiffusionInpaintPipeline.from_pretrained(
           _SCREAMING_SNAKE_CASE		,    safety_checker=_SCREAMING_SNAKE_CASE		,    scheduler=_SCREAMING_SNAKE_CASE		,    torch_dtype=torch.floataa		,    )
       pipe.to(_SCREAMING_SNAKE_CASE       )
       pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE       )
       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=_SCREAMING_SNAKE_CASE		,    image=_SCREAMING_SNAKE_CASE		,    mask_image=_SCREAMING_SNAKE_CASE		,    generator=_SCREAMING_SNAKE_CASE		,    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














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ['''PLBartTokenizer''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''PLBartForCausalLM''',
	    '''PLBartForConditionalGeneration''',
	    '''PLBartForSequenceClassification''',
	    '''PLBartModel''',
	    '''PLBartPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)






