




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        __snake_case  (_UpperCamelCase						,	_UpperCamelCase						,	_UpperCamelCase						,	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       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Dict:





								'''simple docstring'''




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

								_lowerCAmelCase    :    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      :		str		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]=0      )  ->    Optional[Any]:





								'''simple docstring'''




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





								'''simple docstring'''




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

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

								assert image.shape == (1, 64, 64, 3)
								_lowerCAmelCase    :    Union[str, Any]										=      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      :		str      )  ->    Optional[Any]:





								'''simple docstring'''




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






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



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[str]:





								'''simple docstring'''




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



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





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      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    :    Dict										=      load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench.npy"""      )

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

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

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

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[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    :    List[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"""      )

								_lowerCAmelCase    :    Any										=      "stabilityai/stable-diffusion-2-inpainting"
								_lowerCAmelCase    :    str										=      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    :    Any										=      "Face of a yellow cat, high resolution, sitting on a park bench"

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

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



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





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

								_lowerCAmelCase    :    Dict										=      "stabilityai/stable-diffusion-2-inpainting"
								_lowerCAmelCase    :    str										=      PNDMScheduler.from_pretrained(_UpperCAmelCase		,	subfolder="""scheduler"""      )
								_lowerCAmelCase    :    Union[str, Any]										=      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    :    List[Any]										=      "Face of a yellow cat, high resolution, sitting on a park bench"

								_lowerCAmelCase    :    List[Any]										=      torch.manual_seed(0      )
								_lowerCAmelCase    :    List[Any]										=      pipe(
								    prompt=_UpperCAmelCase		,	image=_UpperCAmelCase		,	mask_image=_UpperCAmelCase		,	generator=_UpperCAmelCase		,	num_inference_steps=2		,	output_type="""np"""		,	)

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







from __future__ import annotations

from decimal import Decimal

from numpy import array




def 							_UpperCAmelCase  (UpperCamelCase_ : list[list[float]]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      Decimal

				# Check if the provided matrix has 2 rows and 2 columns
				# since this implementation only works for 2x2 matrices
				if len(UpperCamelCase_       ) == 2 and len(matrix[0]       ) == 2 and len(matrix[1]       ) == 2:
								# Calculate the determinant of the matrix
								_lowerCAmelCase    :    Optional[Any]										=      float(
								    d(matrix[0][0]       ) * d(matrix[1][1]       ) - d(matrix[1][0]       ) * d(matrix[0][1]       )       )
								if determinant == 0:
												raise ValueError("""This matrix has no inverse."""       )

								# Creates a copy of the matrix with swapped positions of the elements
								_lowerCAmelCase    :    Union[str, Any]										=      [[0.0, 0.0], [0.0, 0.0]]
								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      matrix[1][1], matrix[0][0]
								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[Any]										=      -matrix[1][0], -matrix[0][1]

								# Calculate the inverse of the matrix
								return [
								    [(float(d(UpperCamelCase_       )       ) / determinant) or 0.0 for n in row] for row in swapped_matrix
								]
				elif (
				    len(UpperCamelCase_       ) == 3
				    and len(matrix[0]       ) == 3
				    and len(matrix[1]       ) == 3
				    and len(matrix[2]       ) == 3
				):
								# Calculate the determinant of the matrix using Sarrus rule
								_lowerCAmelCase    :    List[str]										=      float(
								    (
								        (d(matrix[0][0]       ) * d(matrix[1][1]       ) * d(matrix[2][2]       ))
								        + (d(matrix[0][1]       ) * d(matrix[1][2]       ) * d(matrix[2][0]       ))
								        + (d(matrix[0][2]       ) * d(matrix[1][0]       ) * d(matrix[2][1]       ))
								    )
								    - (
								        (d(matrix[0][2]       ) * d(matrix[1][1]       ) * d(matrix[2][0]       ))
								        + (d(matrix[0][1]       ) * d(matrix[1][0]       ) * d(matrix[2][2]       ))
								        + (d(matrix[0][0]       ) * d(matrix[1][2]       ) * d(matrix[2][1]       ))
								    )       )
								if determinant == 0:
												raise ValueError("""This matrix has no inverse."""       )

								# Creating cofactor matrix
								_lowerCAmelCase    :    Optional[Any]										=      [
								    [d(0.0       ), d(0.0       ), d(0.0       )],
								    [d(0.0       ), d(0.0       ), d(0.0       )],
								    [d(0.0       ), d(0.0       ), d(0.0       )],
								]
								_lowerCAmelCase    :    Optional[Any]										=      (d(matrix[1][1]       ) * d(matrix[2][2]       )) - (
								    d(matrix[1][2]       ) * d(matrix[2][1]       )
								)
								_lowerCAmelCase    :    str										=      -(
								    (d(matrix[1][0]       ) * d(matrix[2][2]       )) - (d(matrix[1][2]       ) * d(matrix[2][0]       ))
								)
								_lowerCAmelCase    :    Any										=      (d(matrix[1][0]       ) * d(matrix[2][1]       )) - (
								    d(matrix[1][1]       ) * d(matrix[2][0]       )
								)
								_lowerCAmelCase    :    List[str]										=      -(
								    (d(matrix[0][1]       ) * d(matrix[2][2]       )) - (d(matrix[0][2]       ) * d(matrix[2][1]       ))
								)
								_lowerCAmelCase    :    Tuple										=      (d(matrix[0][0]       ) * d(matrix[2][2]       )) - (
								    d(matrix[0][2]       ) * d(matrix[2][0]       )
								)
								_lowerCAmelCase    :    Any										=      -(
								    (d(matrix[0][0]       ) * d(matrix[2][1]       )) - (d(matrix[0][1]       ) * d(matrix[2][0]       ))
								)
								_lowerCAmelCase    :    int										=      (d(matrix[0][1]       ) * d(matrix[1][2]       )) - (
								    d(matrix[0][2]       ) * d(matrix[1][1]       )
								)
								_lowerCAmelCase    :    Optional[Any]										=      -(
								    (d(matrix[0][0]       ) * d(matrix[1][2]       )) - (d(matrix[0][2]       ) * d(matrix[1][0]       ))
								)
								_lowerCAmelCase    :    Tuple										=      (d(matrix[0][0]       ) * d(matrix[1][1]       )) - (
								    d(matrix[0][1]       ) * d(matrix[1][0]       )
								)

								# Transpose the cofactor matrix (Adjoint matrix)
								_lowerCAmelCase    :    List[str]										=      array(UpperCamelCase_       )
								for i in range(3       ):
												for j in range(3       ):
																_lowerCAmelCase    :    List[Any]										=      cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
								_lowerCAmelCase    :    Tuple										=      array(UpperCamelCase_       )
								for i in range(3       ):
												for j in range(3       ):
																inverse_matrix[i][j] /= d(UpperCamelCase_       )

        # Calculate the inverse of the matrix
								return [[float(d(UpperCamelCase_       )       ) or 0.0 for n in row] for row in inverse_matrix]
				raise ValueError("""Please provide a matrix of size 2x2 or 3x3."""       )

