



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   UpperCAmelCase_    (       a   ,					a   ,					a   ,					unittest.TestCase):
							lowerCamelCase__   			=		StableDiffusionInpaintPipeline
							lowerCamelCase__   			=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							lowerCamelCase__   			=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowerCamelCase__   			=		frozenset(
							    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCamelCase__   			=		frozenset([])







							def    snake_case__      (   self):

														'''simple docstring'''

														torch.manual_seed(0)
														_lowerCAmelCase     :							str    =    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=__a,			)
														_lowerCAmelCase     :							Optional[int]    =    PNDMScheduler(skip_prk_steps=__a)
														torch.manual_seed(0)
														_lowerCAmelCase     :							List[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)
														_lowerCAmelCase     :							Union[str, Any]    =    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,			)
														_lowerCAmelCase     :							List[Any]    =    CLIPTextModel(__a)
														_lowerCAmelCase     :							Optional[int]    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

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







							def    snake_case__      (   self,			__a,			__a=0):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    floats_tensor((1, 3, 32, 32),			rng=random.Random(__a)).to(__a)
														_lowerCAmelCase     :							Optional[int]    =    image.cpu().permute(0,			2,			3,			1)[0]
														_lowerCAmelCase     :							Dict    =    Image.fromarray(np.uinta(__a)).convert("RGB").resize((64, 64))
														_lowerCAmelCase     :							List[str]    =    Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
														if str(__a).startswith("mps"):
																					_lowerCAmelCase     :							Any    =    torch.manual_seed(__a)
														else:
																					_lowerCAmelCase     :							int    =    torch.Generator(device=__a).manual_seed(__a)
														_lowerCAmelCase     :							Optional[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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    "cpu"  # ensure determinism for the device-dependent torch.Generator
														_lowerCAmelCase     :							Union[str, Any]    =    self.get_dummy_components()
														_lowerCAmelCase     :							List[str]    =    StableDiffusionInpaintPipeline(**__a)
														_lowerCAmelCase     :							Tuple    =    sd_pipe.to(__a)
														sd_pipe.set_progress_bar_config(disable=__a)

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

														assert image.shape == (1, 64, 64, 3)
														_lowerCAmelCase     :							Tuple    =    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    snake_case__      (   self):

														'''simple docstring'''

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



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







							def    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/sd2-inpaint/init_image.png")
														_lowerCAmelCase     :							Dict    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
														_lowerCAmelCase     :							Tuple    =    load_numpy(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
														    "/yellow_cat_sitting_on_a_park_bench.npy")

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

														_lowerCAmelCase     :							Tuple    =    "Face of a yellow cat, high resolution, sitting on a park bench"

														_lowerCAmelCase     :							List[str]    =    torch.manual_seed(0)
														_lowerCAmelCase     :							Optional[Any]    =    pipe(
														    prompt=__a,			image=__a,			mask_image=__a,			generator=__a,			output_type="np",			)
														_lowerCAmelCase     :							Any    =    output.images[0]

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/sd2-inpaint/init_image.png")
														_lowerCAmelCase     :							Tuple    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
														_lowerCAmelCase     :							str    =    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     :							Optional[Any]    =    "stabilityai/stable-diffusion-2-inpainting"
														_lowerCAmelCase     :							Any    =    StableDiffusionInpaintPipeline.from_pretrained(
														    __a,			torch_dtype=torch.floataa,			safety_checker=__a,			)
														pipe.to(__a)
														pipe.set_progress_bar_config(disable=__a)
														pipe.enable_attention_slicing()

														_lowerCAmelCase     :							Optional[Any]    =    "Face of a yellow cat, high resolution, sitting on a park bench"

														_lowerCAmelCase     :							Optional[Any]    =    torch.manual_seed(0)
														_lowerCAmelCase     :							Tuple    =    pipe(
														    prompt=__a,			image=__a,			mask_image=__a,			generator=__a,			output_type="np",			)
														_lowerCAmelCase     :							List[Any]    =    output.images[0]

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







							def    snake_case__      (   self):

														'''simple docstring'''

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

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

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

														_lowerCAmelCase     :							Tuple    =    "Face of a yellow cat, high resolution, sitting on a park bench"

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

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





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase : List[Any]      						=						{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : Optional[Any]      						=						[
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : str      						=						[
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		_lowercase : List[str]      						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




