



'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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       :		List[str]		      = KandinskyVaaControlnetPipeline
	__SCREAMING_SNAKE_CASE       :		int		      = ['image_embeds', 'negative_image_embeds', 'hint']
	__SCREAMING_SNAKE_CASE       :		int		      = ['image_embeds', 'negative_image_embeds', 'hint']
	__SCREAMING_SNAKE_CASE       :		Dict		      = [
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	__SCREAMING_SNAKE_CASE       :		Any		      = 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_     :						int     =					{
				    """in_channels""": 8,
				    # Out channels is double in channels because predicts mean and variance
				    """out_channels""": 8,
				    """addition_embed_type""": """image_hint""",
				    """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_     :						int     =					UNetaDConditionModel(**lowercase							)
				return model


	@property
	def    _a     (self							):
				return {
				    "block_out_channels": [32, 32, 64, 64],
				    "down_block_types": [
				        "DownEncoderBlock2D",
				        "DownEncoderBlock2D",
				        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
				    "vq_embed_dim": 4,
				}


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


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

				A_     :						Optional[int]     =					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_     :						Tuple     =					{
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """movq""": movq,
				}
				return components


	def    _a     (self	,				lowercase	,				lowercase=0							):
				A_     :						Optional[int]     =					floats_tensor((1, self.text_embedder_hidden_size)	,				rng=random.Random(lowercase							)							).to(lowercase							)
				A_     :						Union[str, Any]     =					floats_tensor((1, self.text_embedder_hidden_size)	,				rng=random.Random(seed + 1							)							).to(
				    lowercase							)

				# create hint
				A_     :						Dict     =					floats_tensor((1, 3, 64, 64)	,				rng=random.Random(lowercase							)							).to(lowercase							)

				if str(lowercase							).startswith("""mps"""							):
							A_     :						Union[str, Any]     =					torch.manual_seed(lowercase							)
				else:
							A_     :						int     =					torch.Generator(device=lowercase							).manual_seed(lowercase							)
				A_     :						Tuple     =					{
				    """image_embeds""": image_embeds,
				    """negative_image_embeds""": negative_image_embeds,
				    """hint""": hint,
				    """generator""": generator,
				    """height""": 64,
				    """width""": 64,
				    """guidance_scale""": 4.0,
				    """num_inference_steps""": 2,
				    """output_type""": """np""",
				}
				return inputs


	def    _a     (self							):
				A_     :						Union[str, Any]     =					"""cpu"""

				A_     :						Tuple     =					self.get_dummy_components()

				A_     :						int     =					self.pipeline_class(**lowercase							)
				A_     :						Tuple     =					pipe.to(lowercase							)

				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						Optional[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_     :						Any     =					image[0, -3:, -3:, -1]
				A_     :						Optional[Any]     =					image_from_tuple[0, -3:, -3:, -1]

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

				A_     :						Union[str, Any]     =					np.array(
				    [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]							)

				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()}'







@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_     :						List[Any]     =					load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""							)

				A_     :						Optional[int]     =					load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/kandinskyv22/hint_image_cat.png"""							)
				A_     :						Optional[int]     =					torch.from_numpy(np.array(lowercase							)							).float() / 2_55.0
				A_     :						int     =					hint.permute(2	,				0	,				1							).unsqueeze(0							)

				A_     :						Tuple     =					KandinskyVaaPriorPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-prior"""	,				torch_dtype=torch.floataa							)
				pipe_prior.to(lowercase							)

				A_     :						Tuple     =					KandinskyVaaControlnetPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-controlnet-depth"""	,				torch_dtype=torch.floataa							)
				A_     :						List[str]     =					pipeline.to(lowercase							)
				pipeline.set_progress_bar_config(disable=lowercase							)

				A_     :						str     =					"""A robot, 4k photo"""

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

				A_     :						Dict     =					torch.Generator(device="""cuda"""							).manual_seed(0							)
				A_     :						Union[str, Any]     =					pipeline(
				    image_embeds=lowercase	,				negative_image_embeds=lowercase	,				hint=lowercase	,				generator=lowercase	,				num_inference_steps=100	,				output_type="""np"""	,				)

				A_     :						List[Any]     =					output.images[0]

				assert image.shape == (512, 512, 3)

				assert_mean_pixel_difference(lowercase	,				lowercase							)




'''simple docstring'''




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


lowerCamelCase							:str       = logging.get_logger(__name__)







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


	def __init__(self	,				lowercase = True	,				lowercase = None	,				lowercase = PILImageResampling.BILINEAR	,				lowercase = True	,				lowercase = 1 / 255	,				lowercase = True	,				lowercase = None	,				lowercase = True	,				**lowercase	,				):
				super().__init__(**lowercase							)
				A_     :						Union[str, Any]     =					size if size is not None else {"""shortest_edge""": 224}
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Optional[Any]     =					crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
				A_     :						Tuple     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						List[Any]     =					do_resize
				A_     :						List[str]     =					size
				A_     :						Dict     =					resample
				A_     :						int     =					do_rescale
				A_     :						str     =					rescale_factor
				A_     :						Tuple     =					do_center_crop
				A_     :						Tuple     =					crop_size
				A_     :						List[str]     =					do_flip_channel_order


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


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


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


	def    _a     (self	,				lowercase	,				lowercase = None							):
				return flip_channel_order(lowercase	,				data_format=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = ChannelDimension.FIRST	,				**lowercase	,				):
				A_     :						str     =					do_resize if do_resize is not None else self.do_resize
				A_     :						Optional[int]     =					resample if resample is not None else self.resample
				A_     :						str     =					do_rescale if do_rescale is not None else self.do_rescale
				A_     :						str     =					rescale_factor if rescale_factor is not None else self.rescale_factor
				A_     :						str     =					do_center_crop if do_center_crop is not None else self.do_center_crop
				A_     :						Dict     =					(
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

				A_     :						Union[str, Any]     =					size if size is not None else self.size
				A_     :						Dict     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Any     =					crop_size if crop_size is not None else self.crop_size
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						Union[str, Any]     =					make_list_of_images(lowercase							)

				if not valid_images(lowercase							):
							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_rescale and rescale_factor is None:
							raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

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

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

				if do_resize:
							A_     :						List[Any]     =					[self.resize(image=lowercase	,				size=lowercase	,				resample=lowercase							) for image in images]

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

				if do_rescale:
							A_     :						Dict     =					[self.rescale(image=lowercase	,				scale=lowercase							) for image in images]

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
							A_     :						List[str]     =					[self.flip_channel_order(image=lowercase							) for image in images]

				A_     :						List[str]     =					[to_channel_dimension_format(lowercase	,				lowercase							) for image in images]

				A_     :						str     =					{"""pixel_values""": images}
				return BatchFeature(data=lowercase	,				tensor_type=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Optional[Any]     =					outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
							if len(lowercase							) != len(lowercase							):
										raise ValueError(
										    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""							)

							if is_torch_tensor(lowercase							):
										A_     :						Dict     =					target_sizes.numpy()

							A_     :						Union[str, Any]     =					[]

							for idx in range(len(lowercase							)							):
										A_     :						Union[str, Any]     =					torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0							)	,				size=target_sizes[idx]	,				mode="""bilinear"""	,				align_corners=lowercase							)
										A_     :						Any     =					resized_logits[0].argmax(dim=0							)
										semantic_segmentation.append(lowercase							)
				else:
							A_     :						str     =					logits.argmax(dim=1							)
							A_     :						Optional[Any]     =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

				return semantic_segmentation