

"""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 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       KandinskyVaaInpaintPipeline
					lowercase__				 =       ["image_embeds", "negative_image_embeds", "image", "mask_image"]
					lowercase__				 =       [
					    "image_embeds",
					    "negative_image_embeds",
					    "image",
					    "mask_image",
					]
					lowercase__				 =       [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					lowercase__				 =       False







					@property
					def        __lowerCAmelCase (       self			:				str		):
						return 3_2







					@property
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						return 3_2







					@property
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						return self.time_input_dim







					@property
					def        __lowerCAmelCase (       self			:				List[str]		):
						return self.time_input_dim * 4







					@property
					def        __lowerCAmelCase (       self			:				List[Any]		):
						return 1_0_0







					@property
					def        __lowerCAmelCase (       self			:				Tuple		):
						torch.manual_seed(0		)

						lowerCAmelCase__	:						Union[str, Any]  				=	{
						    '''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,
						}

						lowerCAmelCase__	:						Optional[Any]  				=	UNetaDConditionModel(**lowercase_		)
						return model







					@property
					def        __lowerCAmelCase (       self			:				Dict		):
						return {
						    "block_out_channels": [3_2, 6_4],
						    "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": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}







					@property
					def        __lowerCAmelCase (       self			:				int		):
						torch.manual_seed(0		)
						lowerCAmelCase__	:						Tuple  				=	VQModel(**self.dummy_movq_kwargs		)
						return model







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						int  				=	self.dummy_unet
						lowerCAmelCase__	:						List[str]  				=	self.dummy_movq

						lowerCAmelCase__	:						Optional[Any]  				=	DDIMScheduler(
						    num_train_timesteps=1_0_0_0   ,beta_schedule='''linear'''   ,beta_start=0.0_0085   ,beta_end=0.012   ,clip_sample=lowercase_   ,set_alpha_to_one=lowercase_   ,steps_offset=1   ,prediction_type='''epsilon'''   ,thresholding=lowercase_   ,)

						lowerCAmelCase__	:						Optional[Any]  				=	{
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''movq''': movq,
						}

						return components







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				Tuple=0		):
						lowerCAmelCase__	:						Union[str, Any]  				=	floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(lowercase_		)		).to(lowercase_		)
						lowerCAmelCase__	:						int  				=	floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(seed + 1		)		).to(
						    lowercase_		)
						# create init_image
						lowerCAmelCase__	:						Dict  				=	floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(lowercase_		)		).to(lowercase_		)
						lowerCAmelCase__	:						Dict  				=	image.cpu().permute(0   ,2   ,3   ,1		)[0]
						lowerCAmelCase__	:						Optional[int]  				=	Image.fromarray(np.uinta(lowercase_		)		).convert('''RGB'''		).resize((2_5_6, 2_5_6)		)
						# create mask
						lowerCAmelCase__	:						int  				=	np.ones((6_4, 6_4)   ,dtype=np.floataa		)
						lowerCAmelCase__	:						Dict  				=	0

						if str(lowercase_		).startswith('''mps'''		):
							lowerCAmelCase__	:						Union[str, Any]  				=	torch.manual_seed(lowercase_		)
						else:
							lowerCAmelCase__	:						Optional[Any]  				=	torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
						lowerCAmelCase__	:						Dict  				=	{
						    '''image''': init_image,
						    '''mask_image''': mask,
						    '''image_embeds''': image_embeds,
						    '''negative_image_embeds''': negative_image_embeds,
						    '''generator''': generator,
						    '''height''': 6_4,
						    '''width''': 6_4,
						    '''num_inference_steps''': 2,
						    '''guidance_scale''': 4.0,
						    '''output_type''': '''np''',
						}
						return inputs







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	'''cpu'''

						lowerCAmelCase__	:						Any  				=	self.get_dummy_components()

						lowerCAmelCase__	:						Dict  				=	self.pipeline_class(**lowercase_		)
						lowerCAmelCase__	:						Any  				=	pipe.to(lowercase_		)

						pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						str  				=	pipe(**self.get_dummy_inputs(lowercase_		)		)
						lowerCAmelCase__	:						Union[str, Any]  				=	output.images

						lowerCAmelCase__	:						Tuple  				=	pipe(
						    **self.get_dummy_inputs(lowercase_		)   ,return_dict=lowercase_   ,)[0]

						lowerCAmelCase__	:						Optional[Any]  				=	image[0, -3:, -3:, -1]
						lowerCAmelCase__	:						Dict  				=	image_from_tuple[0, -3:, -3:, -1]

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

						assert image.shape == (1, 6_4, 6_4, 3)

						lowerCAmelCase__	:						List[Any]  				=	np.array(
						    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]		)

						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        __lowerCAmelCase (       self			:				Union[str, Any]		):
						super().test_inference_batch_single_identical(expected_max_diff=3E-3		)







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






					"""simple docstring"""



					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__	:						Tuple  				=	load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''		)

						lowerCAmelCase__	:						Union[str, Any]  				=	load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
						lowerCAmelCase__	:						Tuple  				=	np.ones((7_6_8, 7_6_8)   ,dtype=np.floataa		)
						lowerCAmelCase__	:						List[Any]  				=	0

						lowerCAmelCase__	:						int  				=	'''a hat'''

						lowerCAmelCase__	:						Any  				=	KandinskyVaaPriorPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-2-prior'''   ,torch_dtype=torch.floataa		)
						pipe_prior.to(lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	KandinskyVaaInpaintPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''   ,torch_dtype=torch.floataa		)
						lowerCAmelCase__	:						Union[str, Any]  				=	pipeline.to(lowercase_		)
						pipeline.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						List[str]  				=	torch.Generator(device='''cpu'''		).manual_seed(0		)
						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	pipe_prior(
						    lowercase_   ,generator=lowercase_   ,num_inference_steps=5   ,negative_prompt=''''''   ,).to_tuple()

						lowerCAmelCase__	:						Optional[int]  				=	pipeline(
						    image=lowercase_   ,mask_image=lowercase_   ,image_embeds=lowercase_   ,negative_image_embeds=lowercase_   ,generator=lowercase_   ,num_inference_steps=1_0_0   ,height=7_6_8   ,width=7_6_8   ,output_type='''np'''   ,)

						lowerCAmelCase__	:						List[str]  				=	output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(lowercase_   ,lowercase_		)







"""simple docstring"""





import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				List[Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Dict   ,lowercase_			:				str		):
						lowerCAmelCase__	:						int  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	process
						lowerCAmelCase__	:						Dict  				=	params







					def __len__(       self			:				Any		):
						return len(self.dataset		)







					def __getitem__(       self			:				Union[str, Any]   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dataset[i]
						lowerCAmelCase__	:						Optional[Any]  				=	self.process(lowercase_   ,**self.params		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[int]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				List[Any]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				Tuple=None		):
						lowerCAmelCase__	:						List[Any]  				=	loader
						lowerCAmelCase__	:						int  				=	infer
						lowerCAmelCase__	:						List[str]  				=	params
						if loader_batch_size == 1:
							# Let's spare some time by deactivating altogether
							lowerCAmelCase__	:						int  				=	None
						lowerCAmelCase__	:						Dict  				=	loader_batch_size

						# Internal bookkeeping
						lowerCAmelCase__	:						Optional[Any]  				=	None
						lowerCAmelCase__	:						Optional[int]  				=	None







					def __len__(       self			:				Union[str, Any]		):
						return len(self.loader		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				Tuple		):
						if isinstance(self._loader_batch_data   ,torch.Tensor		):
							# Batch data is simple tensor, just fetch the slice
							lowerCAmelCase__	:						Tuple  				=	self._loader_batch_data[self._loader_batch_index]
						else:
							# Batch data is assumed to be BaseModelOutput (or dict)
							lowerCAmelCase__	:						int  				=	{}
							for k, element in self._loader_batch_data.items():
								if isinstance(lowercase_   ,lowercase_		):
									# Convert ModelOutput to tuple first
									lowerCAmelCase__	:						List[Any]  				=	element.to_tuple()
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						List[Any]  				=	tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
									elif isinstance(element[0]   ,np.ndarray		):
										lowerCAmelCase__	:						str  				=	tuple(np.expand_dims(el[self._loader_batch_index]   ,0		) for el in element		)
									continue
								if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowercase_   ,lowercase_		):
									# Those are stored as lists of tensors so need specific unbatching.
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						Dict  				=	tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
									elif isinstance(element[0]   ,np.ndarray		):
										lowerCAmelCase__	:						Optional[int]  				=	tuple(np.expand_dims(el[self._loader_batch_index]   ,0		) for el in element		)
									continue
								if element is None:
									# This can happen for optional data that get passed around
									lowerCAmelCase__	:						Dict  				=	None
								elif isinstance(element[self._loader_batch_index]   ,torch.Tensor		):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers

									lowerCAmelCase__	:						str  				=	element[self._loader_batch_index].unsqueeze(0		)
								elif isinstance(element[self._loader_batch_index]   ,np.ndarray		):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers
									lowerCAmelCase__	:						Tuple  				=	np.expand_dims(element[self._loader_batch_index]   ,0		)
								else:
									# This is typically a list, so no need to `unsqueeze`.
									lowerCAmelCase__	:						int  				=	element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
							lowerCAmelCase__	:						int  				=	self._loader_batch_data.__class__(lowercase_		)
						self._loader_batch_index += 1
						return result







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							# We are currently unrolling a batch so we just need to return
							# the current item within a batch
							return self.loader_batch_item()

						# We're out of items within a batch
						lowerCAmelCase__	:						Dict  				=	next(self.iterator		)
						lowerCAmelCase__	:						List[Any]  				=	self.infer(lowercase_   ,**self.params		)
						# We now have a batch of "inferred things".
						if self.loader_batch_size is not None:
							# Try to infer the size of the batch
							if isinstance(lowercase_   ,torch.Tensor		):
								lowerCAmelCase__	:						int  				=	processed
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	list(processed.keys()		)[0]
								lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
							if isinstance(lowercase_   ,lowercase_		):
								lowerCAmelCase__	:						List[Any]  				=	len(lowercase_		)
							else:
								lowerCAmelCase__	:						List[str]  				=	first_tensor.shape[0]
							if 0 < observed_batch_size < self.loader_batch_size:
								# could be last batch so we can't unroll as many
								# elements.
								lowerCAmelCase__	:						Optional[Any]  				=	observed_batch_size
							# Setting internal index to unwrap the batch
							lowerCAmelCase__	:						str  				=	processed
							lowerCAmelCase__	:						Any  				=	0
							return self.loader_batch_item()
						else:
							# We're not unrolling batches
							return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int=None		):
						super().__init__(lowercase_   ,lowercase_   ,lowercase_		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						Dict  				=	iter(self.loader		)
						lowerCAmelCase__	:						Tuple  				=	None
						return self







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self.subiterator is None:
							lowerCAmelCase__	:						List[Any]  				=	self.infer(next(self.iterator		)   ,**self.params		)
						try:
							# Try to return next item
							lowerCAmelCase__	:						Optional[int]  				=	next(self.subiterator		)
						except StopIteration:
							# When a preprocess iterator ends, we can start lookig at the next item
							# ChunkIterator will keep feeding until ALL elements of iterator
							# all have created their subiterator and have been iterating against.
							#
							# Another way to look at it, is we're basically flattening lists of lists
							# into a single list, but with generators
							lowerCAmelCase__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							lowerCAmelCase__	:						int  				=	next(self.subiterator		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __iter__(       self			:				Tuple		):
						lowerCAmelCase__	:						int  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				List[Any]		):
						# Extremely similar to PipelineIterator in its unpacking mechanism
						# BUT, we have an extra required item which is the presence of `is_last`
						# That is because everything is flattened by `PipelineChunkIterator` we
						# need to keep track of how to regroup here in the original `process`
						# boundaries so that `process` and `postprocess` see the same data.

						# This iterator accumulates items (possibly while unbatching) until it
						# its a `is_last` and then just passes it on to the caller.
						lowerCAmelCase__	:						Tuple  				=	False
						lowerCAmelCase__	:						str  				=	[]
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							while self._loader_batch_index < self.loader_batch_size:
								lowerCAmelCase__	:						Dict  				=	self.loader_batch_item()
								lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
								if is_last:
									return accumulator

						while not is_last:
							lowerCAmelCase__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							if self.loader_batch_size is not None:
								if isinstance(lowercase_   ,torch.Tensor		):
									lowerCAmelCase__	:						Tuple  				=	processed
								else:
									lowerCAmelCase__	:						List[Any]  				=	list(processed.keys()		)[0]
									lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
								if isinstance(lowercase_   ,lowercase_		):
									lowerCAmelCase__	:						Tuple  				=	len(lowercase_		)
								else:
									lowerCAmelCase__	:						str  				=	first_tensor.shape[0]
								if 0 < observed_batch_size < self.loader_batch_size:
									# could be last batch so we can't unroll as many
									# elements.
									lowerCAmelCase__	:						Optional[int]  				=	observed_batch_size
								lowerCAmelCase__	:						Optional[int]  				=	processed
								lowerCAmelCase__	:						Optional[int]  				=	0
								while self._loader_batch_index < self.loader_batch_size:
									lowerCAmelCase__	:						Any  				=	self.loader_batch_item()
									lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
									accumulator.append(lowercase_		)
									if is_last:
										return accumulator
							else:
								lowerCAmelCase__	:						Dict  				=	processed
								lowerCAmelCase__	:						Tuple  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
						return accumulator







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				Dataset   ,lowercase_			:				str		):
						lowerCAmelCase__	:						List[Any]  				=	dataset
						lowerCAmelCase__	:						List[Any]  				=	key







					def __len__(       self			:				List[Any]		):
						return len(self.dataset		)







					def __getitem__(       self			:				str   ,lowercase_			:				Union[str, Any]		):
						return self.dataset[i][self.key]







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Dict   ,lowercase_			:				Dataset   ,lowercase_			:				str   ,lowercase_			:				str		):
						lowerCAmelCase__	:						str  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	keya
						lowerCAmelCase__	:						Optional[Any]  				=	keya







					def __len__(       self			:				str		):
						return len(self.dataset		)







					def __getitem__(       self			:				Optional[int]   ,lowercase_			:				Union[str, Any]		):
						return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




