




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  __magic_name__						(  _snake_case    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							int	      =					KandinskyVaaInpaintPipeline
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
				SCREAMING_SNAKE_CASE__					:							Any	      =					[
				    'image_embeds',
				    'negative_image_embeds',
				    'image',
				    'mask_image',
				]
				SCREAMING_SNAKE_CASE__					:							List[str]	      =					[
				    'generator',
				    'height',
				    'width',
				    'latents',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					False


				@property
				def     _A							(							self:   Union[str, Any]		):
											return 32


				@property
				def     _A							(							self:   str		):
											return 32


				@property
				def     _A							(							self:   List[Any]		):
											return self.time_input_dim


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


				@property
				def     _A							(							self:   Any		):
											return 1_00


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

											SCREAMING_SNAKE_CASE_   	=    {
											    '''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,
											}

											SCREAMING_SNAKE_CASE_   	=    UNetaDConditionModel(**_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   int		):
											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:   Dict		):
											torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    VQModel(**self.dummy_movq_kwargs		)
											return model


				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.dummy_unet
											SCREAMING_SNAKE_CASE_   	=    self.dummy_movq

											SCREAMING_SNAKE_CASE_   	=    DDIMScheduler(
											    num_train_timesteps=10_00		,					beta_schedule='''linear'''		,					beta_start=0.0_00_85		,					beta_end=0.0_12		,					clip_sample=_lowerCamelCase		,					set_alpha_to_one=_lowerCamelCase		,					steps_offset=1		,					prediction_type='''epsilon'''		,					thresholding=_lowerCamelCase		,					)

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

											return components


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple=0		):
											SCREAMING_SNAKE_CASE_   	=    floats_tensor((1, self.text_embedder_hidden_size)		,					rng=random.Random(_lowerCamelCase		)		).to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    floats_tensor((1, self.text_embedder_hidden_size)		,					rng=random.Random(seed + 1		)		).to(
											    _lowerCamelCase		)
											# create init_image
											SCREAMING_SNAKE_CASE_   	=    floats_tensor((1, 3, 64, 64)		,					rng=random.Random(_lowerCamelCase		)		).to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    image.cpu().permute(0		,					2		,					3		,					1		)[0]
											SCREAMING_SNAKE_CASE_   	=    Image.fromarray(np.uinta(_lowerCamelCase		)		).convert('''RGB'''		).resize((2_56, 2_56)		)
											# create mask
											SCREAMING_SNAKE_CASE_   	=    np.ones((64, 64)		,					dtype=np.floataa		)
											SCREAMING_SNAKE_CASE_   	=    0

											if str(_lowerCamelCase		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(_lowerCamelCase		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''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:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    '''cpu'''

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()

											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)

											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    pipe(**self.get_dummy_inputs(_lowerCamelCase		)		)
											SCREAMING_SNAKE_CASE_   	=    output.images

											SCREAMING_SNAKE_CASE_   	=    pipe(
											    **self.get_dummy_inputs(_lowerCamelCase		)		,					return_dict=_lowerCamelCase		,					)[0]

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

											print(f"image.shape {image.shape}"		)

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

											SCREAMING_SNAKE_CASE_   	=    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:   Dict		):
											super().test_inference_batch_single_identical(expected_max_diff=3E-3		)






@slow
@require_torch_gpu
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   Dict		):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''		)

											SCREAMING_SNAKE_CASE_   	=    load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
											SCREAMING_SNAKE_CASE_   	=    np.ones((7_68, 7_68)		,					dtype=np.floataa		)
											SCREAMING_SNAKE_CASE_   	=    0

											SCREAMING_SNAKE_CASE_   	=    '''a hat'''

											SCREAMING_SNAKE_CASE_   	=    KandinskyVaaPriorPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-2-prior'''		,					torch_dtype=torch.floataa		)
											pipe_prior.to(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    KandinskyVaaInpaintPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''		,					torch_dtype=torch.floataa		)
											SCREAMING_SNAKE_CASE_   	=    pipeline.to(_lowerCamelCase		)
											pipeline.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    torch.Generator(device='''cpu'''		).manual_seed(0		)
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    pipe_prior(
											    _lowerCamelCase		,					generator=_lowerCamelCase		,					num_inference_steps=5		,					negative_prompt=''''''		,					).to_tuple()

											SCREAMING_SNAKE_CASE_   	=    pipeline(
											    image=_lowerCamelCase		,					mask_image=_lowerCamelCase		,					image_embeds=_lowerCamelCase		,					negative_image_embeds=_lowerCamelCase		,					generator=_lowerCamelCase		,					num_inference_steps=1_00		,					height=7_68		,					width=7_68		,					output_type='''np'''		,					)

											SCREAMING_SNAKE_CASE_   	=    output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(_lowerCamelCase		,					_lowerCamelCase		)








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
			import torch






class  __magic_name__						(  TensorFormatter[Mapping, "torch.Tensor", Mapping]):
				'''simple docstring'''







				def __init__(							self:   int		,					_lowerCamelCase:   int=None		,					**_lowerCamelCase:   List[Any]		):
											super().__init__(features=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch_tensor_kwargs
											import torch  # noqa import torch at initialization


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											import torch

											if isinstance(_lowerCamelCase		,					_lowerCamelCase		) and column:
																		if all(
																		    isinstance(_lowerCamelCase		,					torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
																		    for x in column		):
																									return torch.stack(_lowerCamelCase		)
											return column


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   List[str]		):
											import torch

											if isinstance(_lowerCamelCase		,					(str, bytes, type(_lowerCamelCase		))		):
																		return value
											elif isinstance(_lowerCamelCase		,					(np.character, np.ndarray)		) and np.issubdtype(value.dtype		,					np.character		):
																		return value.tolist()

											SCREAMING_SNAKE_CASE_   	=    {}

											if isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.integer		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.intaa}
											elif isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.floating		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																		import PIL.Image

																		if isinstance(_lowerCamelCase		,					PIL.Image.Image		):
																									SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
											return torch.tensor(_lowerCamelCase		,					**{**default_dtype, **self.torch_tensor_kwargs}		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Dict		):
											import torch

											# support for torch, tf, jax etc.
											if hasattr(_lowerCamelCase		,					'''__array__'''		) and not isinstance(_lowerCamelCase		,					torch.Tensor		):
																		SCREAMING_SNAKE_CASE_   	=    data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(_lowerCamelCase		,					np.ndarray		):
																		if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																									return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											elif isinstance(_lowerCamelCase		,					(list, tuple)		):
																		return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											return self._tensorize(_lowerCamelCase		)


				def     _A							(							self:   str		,					_lowerCamelCase:   dict		):
											return map_nested(self._recursive_tensorize		,					_lowerCamelCase		,					map_list=_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_row(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_row(_lowerCamelCase		)
											return self.recursive_tensorize(_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_column(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_column(_lowerCamelCase		,					pa_table.column_names[0]		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self._consolidate(_lowerCamelCase		)
											return column





				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											for column_name in batch:
																		SCREAMING_SNAKE_CASE_   	=    self._consolidate(batch[column_name]		)
											return batch


