


'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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      (				A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							KandinskyInpaintPipeline
		_a       					=							['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		_a       					=							[
		    'prompt',
		    'negative_prompt',
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		_a       					=							[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'negative_prompt',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		_a       					=							False




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return 32




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return 32




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return self.time_input_dim




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return self.time_input_dim * 4




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return 100




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''				)
							return tokenizer




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.manual_seed(0				)
							UpperCamelCase__  :List[str]				   =					MCLIPConfig(
							    numDims=self.cross_attention_dim       ,  transformerDimensions=self.text_embedder_hidden_size       ,  hidden_size=self.text_embedder_hidden_size       ,  intermediate_size=37       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  vocab_size=1005       ,  )

							UpperCamelCase__  :Tuple				   =					MultilingualCLIP(UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					text_encoder.eval()

							return text_encoder




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.manual_seed(0				)

							UpperCamelCase__  :Optional[int]				   =					{
							    '''in_channels''': 9,
							    # Out channels is double in channels because predicts mean and variance
							    '''out_channels''': 8,
							    '''addition_embed_type''': '''text_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''': '''text_image_proj''',
							    '''cross_attention_dim''': self.cross_attention_dim,
							    '''attention_head_dim''': 4,
							    '''resnet_time_scale_shift''': '''scale_shift''',
							    '''class_embed_type''': None,
							}

							UpperCamelCase__  :Any				   =					UNetaDConditionModel(**UpperCamelCase_				)
							return model




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.manual_seed(0				)
							UpperCamelCase__  :Optional[Any]				   =					VQModel(**self.dummy_movq_kwargs				)
							return model




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					self.dummy_text_encoder
							UpperCamelCase__  :str				   =					self.dummy_tokenizer
							UpperCamelCase__  :Optional[int]				   =					self.dummy_unet
							UpperCamelCase__  :List[str]				   =					self.dummy_movq

							UpperCamelCase__  :Dict				   =					DDIMScheduler(
							    num_train_timesteps=1000       ,  beta_schedule='''linear'''       ,  beta_start=0.00085       ,  beta_end=0.012       ,  clip_sample=UpperCamelCase_       ,  set_alpha_to_one=UpperCamelCase_       ,  steps_offset=1       ,  prediction_type='''epsilon'''       ,  thresholding=UpperCamelCase_       ,  )

							UpperCamelCase__  :Any				   =					{
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''movq''': movq,
							}

							return components




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=0				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					floats_tensor((1, self.cross_attention_dim)       ,  rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					floats_tensor((1, self.cross_attention_dim)       ,  rng=random.Random(seed + 1				)				).to(UpperCamelCase_				)
							# create init_image
							UpperCamelCase__  :int				   =					floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
							UpperCamelCase__  :Optional[Any]				   =					Image.fromarray(np.uinta(UpperCamelCase_				)				).convert('''RGB'''				).resize((256, 256)				)
							# create mask
							UpperCamelCase__  :str				   =					np.ones((64, 64)       ,  dtype=np.floataa				)
							UpperCamelCase__  :Optional[int]				   =					0

							if str(UpperCamelCase_				).startswith('''mps'''				):
												UpperCamelCase__  :str				   =					torch.manual_seed(UpperCamelCase_				)
							else:
												UpperCamelCase__  :Union[str, Any]				   =					torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
							UpperCamelCase__  :Tuple				   =					{
							    '''prompt''': '''horse''',
							    '''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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					'''cpu'''

							UpperCamelCase__  :List[Any]				   =					self.get_dummy_components()

							UpperCamelCase__  :List[str]				   =					self.pipeline_class(**UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					pipe.to(UpperCamelCase_				)

							pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :int				   =					pipe(**self.get_dummy_inputs(UpperCamelCase_				)				)
							UpperCamelCase__  :int				   =					output.images

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

							UpperCamelCase__  :int				   =					image[0, -3:, -3:, -1]
							UpperCamelCase__  :List[Any]				   =					image_from_tuple[0, -3:, -3:, -1]

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

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

							UpperCamelCase__  :int				   =					np.array(
							    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]				)

							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				):

							'''simple docstring'''


							super().test_inference_batch_single_identical(expected_max_diff=3e-3				)



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

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''				)

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

							UpperCamelCase__  :Optional[int]				   =					'''a hat'''

							UpperCamelCase__  :Optional[Any]				   =					KandinskyPriorPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-1-prior'''       ,  torch_dtype=torch.floataa				)
							pipe_prior.to(UpperCamelCase_				)

							UpperCamelCase__  :List[Any]				   =					KandinskyInpaintPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-1-inpaint'''       ,  torch_dtype=torch.floataa				)
							UpperCamelCase__  :Union[str, Any]				   =					pipeline.to(UpperCamelCase_				)
							pipeline.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :int				   =					torch.Generator(device='''cpu'''				).manual_seed(0				)
							UpperCamelCase__   ,							UpperCamelCase__  :str				   =					pipe_prior(
							    UpperCamelCase_       ,  generator=UpperCamelCase_       ,  num_inference_steps=5       ,  negative_prompt=''''''       ,  ).to_tuple()

							UpperCamelCase__  :Optional[int]				   =					pipeline(
							    UpperCamelCase_       ,  image=UpperCamelCase_       ,  mask_image=UpperCamelCase_       ,  image_embeds=UpperCamelCase_       ,  negative_image_embeds=UpperCamelCase_       ,  generator=UpperCamelCase_       ,  num_inference_steps=100       ,  height=768       ,  width=768       ,  output_type='''np'''       ,  )

							UpperCamelCase__  :Optional[int]				   =					output.images[0]

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

							assert_mean_pixel_difference(UpperCamelCase_       ,  UpperCamelCase_				)



'''simple docstring'''
from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class   lowercase      :

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							if components is None:
												UpperCamelCase__  :List[Any]				   =					[]
							UpperCamelCase__  :Optional[Any]				   =					list(UpperCamelCase_				)




		def __len__(						self				):

							'''simple docstring'''


							return len(self.__components				)




		def __str__(						self				):

							'''simple docstring'''


							return "(" + ",".join(map(UpperCamelCase_       ,  self.__components				)				) + ")"




		def __add__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :Any				   =					len(self				)
							if size == len(UpperCamelCase_				):
												UpperCamelCase__  :Dict				   =					[self.__components[i] + other.component(UpperCamelCase_				) for i in range(UpperCamelCase_				)]
												return Vector(UpperCamelCase_				)
							else:
												raise Exception('''must have the same size'''				)




		def __sub__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					len(self				)
							if size == len(UpperCamelCase_				):
												UpperCamelCase__  :List[str]				   =					[self.__components[i] - other.component(UpperCamelCase_				) for i in range(UpperCamelCase_				)]
												return Vector(UpperCamelCase_				)
							else:  # error case
												raise Exception('''must have the same size'''				)




		@overload
		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							...




		@overload
		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							...




		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							if isinstance(UpperCamelCase_       ,  (float, int)				):
												UpperCamelCase__  :Optional[Any]				   =					[c * other for c in self.__components]
												return Vector(UpperCamelCase_				)
							elif isinstance(UpperCamelCase_       ,  UpperCamelCase_				) and len(self				) == len(UpperCamelCase_				):
												UpperCamelCase__  :Optional[int]				   =					len(self				)
												UpperCamelCase__  :List[Any]				   =					[self.__components[i] * other.component(UpperCamelCase_				) for i in range(UpperCamelCase_				)]
												return sum(UpperCamelCase_				)
							else:  # error case
												raise Exception('''invalid operand!'''				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return Vector(self.__components				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							if isinstance(UpperCamelCase_       ,  UpperCamelCase_				) and -len(self.__components				) <= i < len(self.__components				):
												return self.__components[i]
							else:
												raise Exception('''index out of range'''				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							assert -len(self.__components				) <= pos < len(self.__components				)
							UpperCamelCase__  :Optional[int]				   =					value




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if len(self.__components				) == 0:
												raise Exception('''Vector is empty'''				)
							UpperCamelCase__  :List[str]				   =					[c**2 for c in self.__components]
							return math.sqrt(sum(UpperCamelCase_				)				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = False				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					self * other
							UpperCamelCase__  :Optional[int]				   =					self.euclidean_length() * other.euclidean_length()
							if deg:
												return math.degrees(math.acos(num / den				)				)
							else:
												return math.acos(num / den				)



def      a (     __a  )    ->      Vector:



					'''simple docstring'''






					assert isinstance(__a					,   __a  )
					return Vector([0] * dimension  )


def      a (     __a					,   __a  )    ->      Vector:



					'''simple docstring'''






					assert isinstance(__a					,   __a  ) and (isinstance(__a					,   __a  ))
					UpperCamelCase__  :str				   =					[0] * dimension
					UpperCamelCase__  :Tuple				   =					1
					return Vector(__a  )


def      a (     __a					,   __a					,   __a  )    ->      Vector:



					'''simple docstring'''






					assert (
					    isinstance(__a					,   __a  )
					    and isinstance(__a					,   __a  )
					    and (isinstance(__a					,   (int, float)  ))
					)
					return x * scalar + y


def      a (     __a					,   __a					,   __a  )    ->      Vector:



					'''simple docstring'''






					random.seed(__a  )
					UpperCamelCase__  :Optional[Any]				   =					[random.randint(__a					,   __a  ) for _ in range(__a  )]
					return Vector(__a  )
class   lowercase      :

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					matrix
							UpperCamelCase__  :Union[str, Any]				   =					w
							UpperCamelCase__  :Optional[int]				   =					h




		def __str__(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					''''''
							for i in range(self.__height				):
												ans += "|"
												for j in range(self.__width				):
																	if j < self.__width - 1:
																						ans += str(self.__matrix[i][j]				) + ","
																	else:
																						ans += str(self.__matrix[i][j]				) + "|\n"
							return ans




		def __add__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							if self.__width == other.width() and self.__height == other.height():
												UpperCamelCase__  :int				   =					[]
												for i in range(self.__height				):
																	UpperCamelCase__  :int				   =					[
																	    self.__matrix[i][j] + other.component(UpperCamelCase_       ,  UpperCamelCase_				)
																	    for j in range(self.__width				)
																	]
																	matrix.append(UpperCamelCase_				)
												return Matrix(UpperCamelCase_       ,  self.__width       ,  self.__height				)
							else:
												raise Exception('''matrix must have the same dimension!'''				)




		def __sub__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							if self.__width == other.width() and self.__height == other.height():
												UpperCamelCase__  :Optional[Any]				   =					[]
												for i in range(self.__height				):
																	UpperCamelCase__  :Optional[int]				   =					[
																	    self.__matrix[i][j] - other.component(UpperCamelCase_       ,  UpperCamelCase_				)
																	    for j in range(self.__width				)
																	]
																	matrix.append(UpperCamelCase_				)
												return Matrix(UpperCamelCase_       ,  self.__width       ,  self.__height				)
							else:
												raise Exception('''matrices must have the same dimension!'''				)




		@overload
		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							...




		@overload
		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							...




		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							if isinstance(UpperCamelCase_       ,  UpperCamelCase_				):  # matrix-vector
												if len(UpperCamelCase_				) == self.__width:
																	UpperCamelCase__  :Any				   =					zero_vector(self.__height				)
																	for i in range(self.__height				):
																						UpperCamelCase__  :Any				   =					[
																						    self.__matrix[i][j] * other.component(UpperCamelCase_				)
																						    for j in range(self.__width				)
																						]
																						ans.change_component(UpperCamelCase_       ,  sum(UpperCamelCase_				)				)
																	return ans
												else:
																	raise Exception(
																	    '''vector must have the same size as the '''
																	    '''number of columns of the matrix!'''				)
							elif isinstance(UpperCamelCase_       ,  (int, float)				):  # matrix-scalar
												UpperCamelCase__  :Union[str, Any]				   =					[
												    [self.__matrix[i][j] * other for j in range(self.__width				)]
												    for i in range(self.__height				)
												]
												return Matrix(UpperCamelCase_       ,  self.__width       ,  self.__height				)
							return None




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return self.__height




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return self.__width




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							if 0 <= x < self.__height and 0 <= y < self.__width:
												return self.__matrix[x][y]
							else:
												raise Exception('''change_component: indices out of bounds'''				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							if 0 <= x < self.__height and 0 <= y < self.__width:
												UpperCamelCase__  :Tuple				   =					value
							else:
												raise Exception('''change_component: indices out of bounds'''				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							if self.__height != self.__width:
												raise Exception('''Matrix is not square'''				)
							UpperCamelCase__  :str				   =					self.__matrix[:x] + self.__matrix[x + 1 :]
							for i in range(len(UpperCamelCase_				)				):
												UpperCamelCase__  :Union[str, Any]				   =					minor[i][:y] + minor[i][y + 1 :]
							return Matrix(UpperCamelCase_       ,  self.__width - 1       ,  self.__height - 1				).determinant()




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							if self.__height != self.__width:
												raise Exception('''Matrix is not square'''				)
							if 0 <= x < self.__height and 0 <= y < self.__width:
												return (-1) ** (x + y) * self.minor(UpperCamelCase_       ,  UpperCamelCase_				)
							else:
												raise Exception('''Indices out of bounds'''				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if self.__height != self.__width:
												raise Exception('''Matrix is not square'''				)
							if self.__height < 1:
												raise Exception('''Matrix has no element'''				)
							elif self.__height == 1:
												return self.__matrix[0][0]
							elif self.__height == 2:
												return (
												    self.__matrix[0][0] * self.__matrix[1][1]
												    - self.__matrix[0][1] * self.__matrix[1][0]
												)
							else:
												UpperCamelCase__  :Optional[Any]				   =					[
												    self.__matrix[0][y] * self.cofactor(0       ,  UpperCamelCase_				) for y in range(self.__width				)
												]
												return sum(UpperCamelCase_				)



def      a (     __a  )    ->      Matrix:



					'''simple docstring'''






					UpperCamelCase__  :list[list[float]]				   =					[[0] * n for _ in range(__a  )]
					return Matrix(__a					,   __a					,   __a  )







def      a (     __a					,   __a					,   __a					,   __a  )    ->      Matrix:



					'''simple docstring'''






					random.seed(__a  )
					UpperCamelCase__  :list[list[float]]				   =					[
					    [random.randint(__a					,   __a  ) for _ in range(__a  )] for _ in range(__a  )
					]
					return Matrix(__a					,   __a					,   __a  )