




from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   ['pixel_values']
      def __init__(				self: Any    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[int, float] = 1 / 2_55    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: int = 8    ,       **UpperCamelCase_: Tuple    ,       ):
             super().__init__(**UpperCamelCase_   )

             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						do_pad
             __lowerCamelCase   =						pad_size
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: float    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Tuple   ):
             return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None   ):
             __lowerCamelCase,   __lowerCamelCase   =						get_image_size(UpperCamelCase_   )
             __lowerCamelCase   =						(old_height // size + 1) * size - old_height
             __lowerCamelCase   =						(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase_    ,       ((0, pad_height), (0, pad_width))    ,       mode="""symmetric"""    ,       data_format=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[float] = None    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_: Union[str, ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCamelCase_: Any    ,       ):
             __lowerCamelCase   =						do_rescale if do_rescale is not None else self.do_rescale
             __lowerCamelCase   =						rescale_factor if rescale_factor is not None else self.rescale_factor
             __lowerCamelCase   =						do_pad if do_pad is not None else self.do_pad
             __lowerCamelCase   =						pad_size if pad_size is not None else self.pad_size

             __lowerCamelCase   =						make_list_of_images(UpperCamelCase_   )

             if not valid_images(UpperCamelCase_   ):
                    raise ValueError(
                        """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                        """torch.Tensor, tf.Tensor or jax.ndarray."""   )

             if do_rescale and rescale_factor is None:
                    raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

             # All transformations expect numpy arrays.
             __lowerCamelCase   =						[to_numpy_array(UpperCamelCase_   ) for image in images]

             if do_rescale:
                    __lowerCamelCase   =						[self.rescale(image=UpperCamelCase_    ,       scale=UpperCamelCase_   ) for image in images]

             if do_pad:
                    __lowerCamelCase   =						[self.pad(UpperCamelCase_    ,       size=UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						[to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						{"""pixel_values""": images}
             return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_   )







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     lowerCamelCase__(					__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Union[str, Any]    		=   StableDiffusionInpaintPipeline
      UpperCAmelCase__      :	Dict    		=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase__      :	str    		=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase__      :	int    		=   frozenset(
          [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      UpperCAmelCase__      :	Union[str, Any]    		=   frozenset([])
      def 			lowerCAmelCase__						(				self: str   ):
             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=1_28    ,       )
             torch.manual_seed(0   )
             __lowerCamelCase   =						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=10_00    ,       hidden_act="""gelu"""    ,       projection_dim=5_12    ,       )
             __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 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: List[Any]=0   ):
             # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
             __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 			lowerCAmelCase__						(				self: str   ):
             __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([0.4727, 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 			lowerCAmelCase__						(				self: int   ):
             super().test_inference_batch_single_identical(expected_max_diff=3E-3   )





@slow
@require_torch_gpu
class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: str   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __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 == (5_12, 5_12, 3)
             assert np.abs(expected_image - image   ).max() < 9E-3
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __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 == (5_12, 5_12, 3)
             assert np.abs(expected_image - image   ).max() < 5E-1




      def 			lowerCAmelCase__						(				self: int   ):
             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

