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

import numpy as np

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


_UpperCAmelCase :			int              =	logging.get_logger(__name__)


class     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	Any     					=    ["pixel_values"]



		def __init__(			self    ,				A_ = True    ,				A_ = None    ,				A_ = PILImageResampling.BICUBIC    ,				A_ = True    ,				A_ = None    ,				A_ = True    ,				A_ = 1 / 255    ,				A_ = True    ,				A_ = IMAGENET_DEFAULT_MEAN    ,				A_ = IMAGENET_DEFAULT_STD    ,				**A_    ,				)  -> None:
				"""simple docstring"""
				super().__init__(**A_	)
				UpperCamelCase          =					size if size is not None else {'shortest_edge': 224}
				UpperCamelCase          =					get_size_dict(A_    ,				default_to_square=A_	)
				UpperCamelCase          =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
				UpperCamelCase          =					get_size_dict(A_    ,				param_name='crop_size'	)

				UpperCamelCase          =					do_resize
				UpperCamelCase          =					size
				UpperCamelCase          =					resample
				UpperCamelCase          =					do_center_crop
				UpperCamelCase          =					crop_size
				UpperCamelCase          =					do_rescale
				UpperCamelCase          =					rescale_factor
				UpperCamelCase          =					do_normalize
				UpperCamelCase          =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				UpperCamelCase          =					image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_ = PILImageResampling.BICUBIC    ,				A_ = None    ,				**A_    ,				)  -> np.ndarray:
				"""simple docstring"""
				UpperCamelCase          =					get_size_dict(A_    ,				default_to_square=A_	)
				# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
				if "shortest_edge" in size:
						UpperCamelCase          =					int((256 / 224) * size['shortest_edge']	)
						UpperCamelCase          =					get_resize_output_image_size(A_    ,				size=A_    ,				default_to_square=A_	)
						UpperCamelCase          =					{'height': output_size[0], 'width': output_size[1]}
				if "height" not in size_dict or "width" not in size_dict:
						raise ValueError(
						    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''	)
				return resize(
				    A_    ,				size=(size_dict['height'], size_dict['width'])    ,				resample=A_    ,				data_format=A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_ = None    ,				**A_    ,				)  -> np.ndarray:
				"""simple docstring"""
				UpperCamelCase          =					get_size_dict(A_	)
				if "height" not in size or "width" not in size:
						raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
				return center_crop(A_    ,				size=(size['height'], size['width'])    ,				data_format=A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_ = None    ,				**A_    ,				)  -> np.ndarray:
				"""simple docstring"""
				return rescale(A_    ,				scale=A_    ,				data_format=A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_ = None    ,				**A_    ,				)  -> np.ndarray:
				"""simple docstring"""
				return normalize(A_    ,				mean=A_    ,				std=A_    ,				data_format=A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = ChannelDimension.FIRST    ,				**A_    ,				)  -> BatchFeature:
				"""simple docstring"""
				UpperCamelCase          =					do_resize if do_resize is not None else self.do_resize
				UpperCamelCase          =					resample if resample is not None else self.resample
				UpperCamelCase          =					do_center_crop if do_center_crop is not None else self.do_center_crop
				UpperCamelCase          =					do_rescale if do_rescale is not None else self.do_rescale
				UpperCamelCase          =					rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCamelCase          =					do_normalize if do_normalize is not None else self.do_normalize
				UpperCamelCase          =					image_mean if image_mean is not None else self.image_mean
				UpperCamelCase          =					image_std if image_std is not None else self.image_std

				UpperCamelCase          =					size if size is not None else self.size
				UpperCamelCase          =					get_size_dict(A_    ,				default_to_square=A_	)
				UpperCamelCase          =					crop_size if crop_size is not None else self.crop_size
				UpperCamelCase          =					get_size_dict(A_    ,				param_name='crop_size'	)

				UpperCamelCase          =					make_list_of_images(A_	)

				if not valid_images(A_	):
						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.'	)

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

				if do_resize:
						UpperCamelCase          =					[self.resize(A_    ,				A_    ,				A_	) for image in images]

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

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

				if do_normalize:
						UpperCamelCase          =					[self.normalize(A_    ,				A_    ,				A_	) for image in images]

				UpperCamelCase          =					[to_channel_dimension_format(A_    ,				A_	) for image in images]

				UpperCamelCase          =					{'pixel_values': images}
				return BatchFeature(data=A_    ,				tensor_type=A_	)


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	List[Any]     					=    KandinskyVaaControlnetImgaImgPipeline
		__lowercase	:	Optional[Any]     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Any     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Union[str, Any]     					=    [
		    "generator",
		    "height",
		    "width",
		    "strength",
		    "guidance_scale",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		__lowercase	:	Optional[int]     					=    False



		@property
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim * 4



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				return 100



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				torch.manual_seed(0	)

				UpperCamelCase          =					{
				    '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,
				}

				UpperCamelCase          =					UNetaDConditionModel(**A_	)
				return model



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				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 __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				torch.manual_seed(0	)
				UpperCamelCase          =					VQModel(**self.dummy_movq_kwargs	)
				return model



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.dummy_unet
				UpperCamelCase          =					self.dummy_movq

				UpperCamelCase          =					{
				    'num_train_timesteps': 1_000,
				    'beta_schedule': 'linear',
				    'beta_start': 0.0_0085,
				    'beta_end': 0.012,
				    'clip_sample': False,
				    'set_alpha_to_one': False,
				    'steps_offset': 0,
				    'prediction_type': 'epsilon',
				    'thresholding': False,
				}

				UpperCamelCase          =					DDIMScheduler(**A_	)

				UpperCamelCase          =					{
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}

				return components



		def __UpperCamelCase			(			self    ,				A_    ,				A_=0	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(A_	)	).to(A_	)
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(seed + 1	)	).to(
				    A_	)
				# create init_image
				UpperCamelCase          =					floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(A_	)	).to(A_	)
				UpperCamelCase          =					image.cpu().permute(0    ,				2    ,				3    ,				1	)[0]
				UpperCamelCase          =					Image.fromarray(np.uinta(A_	)	).convert('RGB'	).resize((256, 256)	)
				# create hint
				UpperCamelCase          =					floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(A_	)	).to(A_	)

				if str(A_	).startswith('mps'	):
						UpperCamelCase          =					torch.manual_seed(A_	)
				else:
						UpperCamelCase          =					torch.Generator(device=A_	).manual_seed(A_	)
				UpperCamelCase          =					{
				    'image': init_image,
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    'hint': hint,
				    'generator': generator,
				    'height': 64,
				    'width': 64,
				    'num_inference_steps': 10,
				    'guidance_scale': 7.0,
				    'strength': 0.2,
				    'output_type': 'np',
				}
				return inputs



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					'cpu'

				UpperCamelCase          =					self.get_dummy_components()

				UpperCamelCase          =					self.pipeline_class(**A_	)
				UpperCamelCase          =					pipe.to(A_	)

				pipe.set_progress_bar_config(disable=A_	)

				UpperCamelCase          =					pipe(**self.get_dummy_inputs(A_	)	)
				UpperCamelCase          =					output.images

				UpperCamelCase          =					pipe(
				    **self.get_dummy_inputs(A_	)    ,				return_dict=A_    ,				)[0]

				UpperCamelCase          =					image[0, -3:, -3:, -1]
				UpperCamelCase          =					image_from_tuple[0, -3:, -3:, -1]

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

				UpperCamelCase          =					np.array(
				    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]	)
				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     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'	)
				UpperCamelCase          =					init_image.resize((512, 512)	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/hint_image_cat.png'	)
				UpperCamelCase          =					torch.from_numpy(np.array(A_	)	).float() / 255.0
				UpperCamelCase          =					hint.permute(2    ,				0    ,				1	).unsqueeze(0	)

				UpperCamelCase          =					'A robot, 4k photo'

				UpperCamelCase          =					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-prior'    ,				torch_dtype=torch.floataa	)
				pipe_prior.to(A_	)

				UpperCamelCase          =					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-controlnet-depth'    ,				torch_dtype=torch.floataa	)
				UpperCamelCase          =					pipeline.to(A_	)

				pipeline.set_progress_bar_config(disable=A_	)

				UpperCamelCase          =					torch.Generator(device='cpu'	).manual_seed(0	)

				UpperCamelCase     ,  UpperCamelCase          =					pipe_prior(
				    A_    ,				image=A_    ,				strength=0.85    ,				generator=A_    ,				negative_prompt=''    ,				).to_tuple()

				UpperCamelCase          =					pipeline(
				    image=A_    ,				image_embeds=A_    ,				negative_image_embeds=A_    ,				hint=A_    ,				generator=A_    ,				num_inference_steps=100    ,				height=512    ,				width=512    ,				strength=0.5    ,				output_type='np'    ,				)

				UpperCamelCase          =					output.images[0]

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

				assert_mean_pixel_difference(A_    ,				A_	)
