



'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase							:Dict       = logging.get_logger(__name__)


if is_vision_available():
						import PIL







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Any		      = ['pixel_values']


	def __init__(self	,				lowercase = True	,				lowercase = None	,				lowercase = PILImageResampling.BICUBIC	,				lowercase = True	,				lowercase = None	,				lowercase = True	,				lowercase = 1 / 255	,				lowercase = True	,				lowercase = None	,				lowercase = None	,				lowercase = True	,				**lowercase	,				):
				super().__init__(**lowerCamelCase_							)
				A_     :						List[Any]     =					size if size is not None else {"""shortest_edge""": 224}
				A_     :						Dict     =					get_size_dict(lowerCamelCase_	,				default_to_square=lowerCamelCase_							)
				A_     :						Union[str, Any]     =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
				A_     :						Dict     =					get_size_dict(lowerCamelCase_	,				default_to_square=lowerCamelCase_	,				param_name="""crop_size"""							)

				A_     :						int     =					do_resize
				A_     :						List[str]     =					size
				A_     :						str     =					resample
				A_     :						Dict     =					do_center_crop
				A_     :						int     =					crop_size
				A_     :						str     =					do_rescale
				A_     :						List[str]     =					rescale_factor
				A_     :						Any     =					do_normalize
				A_     :						Dict     =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				A_     :						Union[str, Any]     =					image_std if image_std is not None else OPENAI_CLIP_STD
				A_     :						Any     =					do_convert_rgb


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = PILImageResampling.BICUBIC	,				lowercase = None	,				**lowercase	,				):
				A_     :						Optional[Any]     =					get_size_dict(lowerCamelCase_	,				default_to_square=lowerCamelCase_							)
				if "shortest_edge" not in size:
							raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'							)
				A_     :						Dict     =					get_resize_output_image_size(lowerCamelCase_	,				size=size["""shortest_edge"""]	,				default_to_square=lowerCamelCase_							)
				return resize(lowerCamelCase_	,				size=lowerCamelCase_	,				resample=lowerCamelCase_	,				data_format=lowerCamelCase_	,				**lowerCamelCase_							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				A_     :						List[str]     =					get_size_dict(lowerCamelCase_							)
				if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'							)
				return center_crop(lowerCamelCase_	,				size=(size["""height"""], size["""width"""])	,				data_format=lowerCamelCase_	,				**lowerCamelCase_							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				return rescale(lowerCamelCase_	,				scale=lowerCamelCase_	,				data_format=lowerCamelCase_	,				**lowerCamelCase_							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				return normalize(lowerCamelCase_	,				mean=lowerCamelCase_	,				std=lowerCamelCase_	,				data_format=lowerCamelCase_	,				**lowerCamelCase_							)


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = ChannelDimension.FIRST	,				**lowercase	,				):
				A_     :						int     =					do_resize if do_resize is not None else self.do_resize
				A_     :						Dict     =					size if size is not None else self.size
				A_     :						str     =					get_size_dict(lowerCamelCase_	,				param_name="""size"""	,				default_to_square=lowerCamelCase_							)
				A_     :						Optional[int]     =					resample if resample is not None else self.resample
				A_     :						str     =					do_center_crop if do_center_crop is not None else self.do_center_crop
				A_     :						List[str]     =					crop_size if crop_size is not None else self.crop_size
				A_     :						Union[str, Any]     =					get_size_dict(lowerCamelCase_	,				param_name="""crop_size"""	,				default_to_square=lowerCamelCase_							)
				A_     :						List[Any]     =					do_rescale if do_rescale is not None else self.do_rescale
				A_     :						Any     =					rescale_factor if rescale_factor is not None else self.rescale_factor
				A_     :						List[str]     =					do_normalize if do_normalize is not None else self.do_normalize
				A_     :						str     =					image_mean if image_mean is not None else self.image_mean
				A_     :						str     =					image_std if image_std is not None else self.image_std
				A_     :						Optional[int]     =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				A_     :						Dict     =					make_list_of_images(lowerCamelCase_							)

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

				if do_resize and size is None:
							raise ValueError("""Size must be specified if do_resize is True."""							)

				if do_center_crop and crop_size is None:
							raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

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

				if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
							A_     :						Union[str, Any]     =					[convert_to_rgb(lowerCamelCase_							) for image in images]

				# All transformations expect numpy arrays.
				A_     :						Optional[Any]     =					[to_numpy_array(lowerCamelCase_							) for image in images]

				if do_resize:
							A_     :						Dict     =					[self.resize(image=lowerCamelCase_	,				size=lowerCamelCase_	,				resample=lowerCamelCase_							) for image in images]

				if do_center_crop:
							A_     :						List[str]     =					[self.center_crop(image=lowerCamelCase_	,				size=lowerCamelCase_							) for image in images]

				if do_rescale:
							A_     :						Union[str, Any]     =					[self.rescale(image=lowerCamelCase_	,				scale=lowerCamelCase_							) for image in images]

				if do_normalize:
							A_     :						Union[str, Any]     =					[self.normalize(image=lowerCamelCase_	,				mean=lowerCamelCase_	,				std=lowerCamelCase_							) for image in images]

				A_     :						List[Any]     =					[to_channel_dimension_format(lowerCamelCase_	,				lowerCamelCase_							) for image in images]

				A_     :						List[str]     =					{"""pixel_values""": images}
				return BatchFeature(data=lowerCamelCase_	,				tensor_type=lowerCamelCase_							)







'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class      _lowerCAmelCase							(      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = KandinskyVaaInpaintPipeline
	__SCREAMING_SNAKE_CASE       :		int		      = ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
	__SCREAMING_SNAKE_CASE       :		str		      = [
	    'image_embeds',
	    'negative_image_embeds',
	    'image',
	    'mask_image',
	]
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = [
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	__SCREAMING_SNAKE_CASE       :		List[str]		      = False


	@property
	def    _a     (self							):
				return 32


	@property
	def    _a     (self							):
				return 32


	@property
	def    _a     (self							):
				return self.time_input_dim


	@property
	def    _a     (self							):
				return self.time_input_dim * 4


	@property
	def    _a     (self							):
				return 100


	@property
	def    _a     (self							):
				torch.manual_seed(0							)

				A_     :						str     =					{
				    """in_channels""": 9,
				    # Out channels is double in channels because predicts mean and variance
				    """out_channels""": 8,
				    """addition_embed_type""": """image""",
				    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
				    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
				    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
				    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    """layers_per_block""": 1,
				    """encoder_hid_dim""": self.text_embedder_hidden_size,
				    """encoder_hid_dim_type""": """image_proj""",
				    """cross_attention_dim""": self.cross_attention_dim,
				    """attention_head_dim""": 4,
				    """resnet_time_scale_shift""": """scale_shift""",
				    """class_embed_type""": None,
				}

				A_     :						str     =					UNetaDConditionModel(**lowercase							)
				return model


	@property
	def    _a     (self							):
				return {
				    "block_out_channels": [32, 64],
				    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 12,
				    "out_channels": 3,
				    "up_block_types": [
				        "AttnUpDecoderBlock2D",
				        "UpDecoderBlock2D",
				    ],
				    "vq_embed_dim": 4,
				}


	@property
	def    _a     (self							):
				torch.manual_seed(0							)
				A_     :						int     =					VQModel(**self.dummy_movq_kwargs							)
				return model


	def    _a     (self							):
				A_     :						Optional[Any]     =					self.dummy_unet
				A_     :						Dict     =					self.dummy_movq

				A_     :						List[Any]     =					DDIMScheduler(
				    num_train_timesteps=1000	,				beta_schedule="""linear"""	,				beta_start=0.0_00_85	,				beta_end=0.0_12	,				clip_sample=lowercase	,				set_alpha_to_one=lowercase	,				steps_offset=1	,				prediction_type="""epsilon"""	,				thresholding=lowercase	,				)

				A_     :						Any     =					{
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """movq""": movq,
				}

				return components


	def    _a     (self	,				lowercase	,				lowercase=0							):
				A_     :						Optional[Any]     =					floats_tensor((1, self.text_embedder_hidden_size)	,				rng=random.Random(lowercase							)							).to(lowercase							)
				A_     :						List[Any]     =					floats_tensor((1, self.text_embedder_hidden_size)	,				rng=random.Random(seed + 1							)							).to(
				    lowercase							)
				# create init_image
				A_     :						Any     =					floats_tensor((1, 3, 64, 64)	,				rng=random.Random(lowercase							)							).to(lowercase							)
				A_     :						Union[str, Any]     =					image.cpu().permute(0	,				2	,				3	,				1							)[0]
				A_     :						Tuple     =					Image.fromarray(np.uinta(lowercase							)							).convert("""RGB"""							).resize((256, 256)							)
				# create mask
				A_     :						List[str]     =					np.ones((64, 64)	,				dtype=np.floataa							)
				A_     :						Any     =					0

				if str(lowercase							).startswith("""mps"""							):
							A_     :						int     =					torch.manual_seed(lowercase							)
				else:
							A_     :						Dict     =					torch.Generator(device=lowercase							).manual_seed(lowercase							)
				A_     :						Any     =					{
				    """image""": init_image,
				    """mask_image""": mask,
				    """image_embeds""": image_embeds,
				    """negative_image_embeds""": negative_image_embeds,
				    """generator""": generator,
				    """height""": 64,
				    """width""": 64,
				    """num_inference_steps""": 2,
				    """guidance_scale""": 4.0,
				    """output_type""": """np""",
				}
				return inputs


	def    _a     (self							):
				A_     :						List[str]     =					"""cpu"""

				A_     :						str     =					self.get_dummy_components()

				A_     :						Any     =					self.pipeline_class(**lowercase							)
				A_     :						List[Any]     =					pipe.to(lowercase							)

				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						Union[str, Any]     =					pipe(**self.get_dummy_inputs(lowercase							)							)
				A_     :						List[str]     =					output.images

				A_     :						Union[str, Any]     =					pipe(
				    **self.get_dummy_inputs(lowercase							)	,				return_dict=lowercase	,				)[0]

				A_     :						List[str]     =					image[0, -3:, -3:, -1]
				A_     :						Any     =					image_from_tuple[0, -3:, -3:, -1]

				print(F'image.shape {image.shape}'							)

				assert image.shape == (1, 64, 64, 3)

				A_     :						Optional[int]     =					np.array(
				    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]							)

				assert (
				    np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
				), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
				assert (
				    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
				), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'


	def    _a     (self							):
				super().test_inference_batch_single_identical(expected_max_diff=3E-3							)







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


	def    _a     (self							):
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


	def    _a     (self							):
				A_     :						Optional[Any]     =					load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""							)

				A_     :						Optional[Any]     =					load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
				A_     :						Dict     =					np.ones((768, 768)	,				dtype=np.floataa							)
				A_     :						Any     =					0

				A_     :						str     =					"""a hat"""

				A_     :						Union[str, Any]     =					KandinskyVaaPriorPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-prior"""	,				torch_dtype=torch.floataa							)
				pipe_prior.to(lowercase							)

				A_     :						Union[str, Any]     =					KandinskyVaaInpaintPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""	,				torch_dtype=torch.floataa							)
				A_     :						str     =					pipeline.to(lowercase							)
				pipeline.set_progress_bar_config(disable=lowercase							)

				A_     :						int     =					torch.Generator(device="""cpu"""							).manual_seed(0							)
				A_,   A_     :						Dict     =					pipe_prior(
				    lowercase	,				generator=lowercase	,				num_inference_steps=5	,				negative_prompt=""""""	,				).to_tuple()

				A_     :						List[Any]     =					pipeline(
				    image=lowercase	,				mask_image=lowercase	,				image_embeds=lowercase	,				negative_image_embeds=lowercase	,				generator=lowercase	,				num_inference_steps=100	,				height=768	,				width=768	,				output_type="""np"""	,				)

				A_     :						Tuple     =					output.images[0]

				assert image.shape == (768, 768, 3)

				assert_mean_pixel_difference(lowercase	,				lowercase							)