


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

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					1
							UpperCamelCase__  :Dict				   =					3
							UpperCamelCase__  :Any				   =					(32, 32)

							UpperCamelCase__  :str				   =					floats_tensor((batch_size, num_channels) + sizes       ,  rng=random.Random(0				)				).to(UpperCamelCase_				)
							return image




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.manual_seed(0				)
							UpperCamelCase__  :Union[str, Any]				   =					UNetaDConditionModel(
							    block_out_channels=(32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=4       ,  out_channels=4       ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,  cross_attention_dim=32       ,  )
							return model




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.manual_seed(0				)
							UpperCamelCase__  :int				   =					AutoencoderKL(
							    block_out_channels=[32, 64]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,  latent_channels=4       ,  )
							return model




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.manual_seed(0				)
							UpperCamelCase__  :Any				   =					RobertaSeriesConfig(
							    hidden_size=32       ,  project_dim=32       ,  intermediate_size=37       ,  layer_norm_eps=1e-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=5006       ,  )
							return RobertaSeriesModelWithTransformation(UpperCamelCase_				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							def extract(*UpperCamelCase_       ,  **UpperCamelCase_				):
												class   lowercase      :

														"""simple docstring"""

														def __init__(						self				):

																			'''simple docstring'''


																			UpperCamelCase__  :List[Any]				   =					torch.ones([0]				)




														def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

																			'''simple docstring'''


																			self.pixel_values.to(UpperCamelCase_				)
																			return self



												return Out()

							return extract




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :Any				   =					self.dummy_cond_unet
							UpperCamelCase__  :Any				   =					PNDMScheduler(skip_prk_steps=UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					self.dummy_vae
							UpperCamelCase__  :Tuple				   =					self.dummy_text_encoder
							UpperCamelCase__  :Dict				   =					XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''				)
							UpperCamelCase__  :Optional[int]				   =					77

							UpperCamelCase__  :Optional[Any]				   =					self.dummy_image.to(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					init_image / 2 + 0.5

							# make sure here that pndm scheduler skips prk
							UpperCamelCase__  :str				   =					AltDiffusionImgaImgPipeline(
							    unet=UpperCamelCase_       ,  scheduler=UpperCamelCase_       ,  vae=UpperCamelCase_       ,  text_encoder=UpperCamelCase_       ,  tokenizer=UpperCamelCase_       ,  safety_checker=UpperCamelCase_       ,  feature_extractor=self.dummy_extractor       ,  )
							UpperCamelCase__  :Dict				   =					VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,  do_normalize=UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					alt_pipe.to(UpperCamelCase_				)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Tuple				   =					'''A painting of a squirrel eating a burger'''
							UpperCamelCase__  :List[Any]				   =					torch.Generator(device=UpperCamelCase_				).manual_seed(0				)
							UpperCamelCase__  :Tuple				   =					alt_pipe(
							    [prompt]       ,  generator=UpperCamelCase_       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type='''np'''       ,  image=UpperCamelCase_       ,  )

							UpperCamelCase__  :List[Any]				   =					output.images

							UpperCamelCase__  :int				   =					torch.Generator(device=UpperCamelCase_				).manual_seed(0				)
							UpperCamelCase__  :List[str]				   =					alt_pipe(
							    [prompt]       ,  generator=UpperCamelCase_       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type='''np'''       ,  image=UpperCamelCase_       ,  return_dict=UpperCamelCase_       ,  )[0]

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

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :Union[str, Any]				   =					np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 5e-3
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 5e-3




		@unittest.skipIf(torch_device != '''cuda'''       ,  '''This test requires a GPU'''				)
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					self.dummy_cond_unet
							UpperCamelCase__  :Union[str, Any]				   =					PNDMScheduler(skip_prk_steps=UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					self.dummy_vae
							UpperCamelCase__  :List[str]				   =					self.dummy_text_encoder
							UpperCamelCase__  :Optional[int]				   =					XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''				)
							UpperCamelCase__  :Optional[int]				   =					77

							UpperCamelCase__  :str				   =					self.dummy_image.to(UpperCamelCase_				)

							# put models in fp16
							UpperCamelCase__  :Any				   =					unet.half()
							UpperCamelCase__  :Tuple				   =					vae.half()
							UpperCamelCase__  :Tuple				   =					bert.half()

							# make sure here that pndm scheduler skips prk
							UpperCamelCase__  :Union[str, Any]				   =					AltDiffusionImgaImgPipeline(
							    unet=UpperCamelCase_       ,  scheduler=UpperCamelCase_       ,  vae=UpperCamelCase_       ,  text_encoder=UpperCamelCase_       ,  tokenizer=UpperCamelCase_       ,  safety_checker=UpperCamelCase_       ,  feature_extractor=self.dummy_extractor       ,  )
							UpperCamelCase__  :List[Any]				   =					VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,  do_normalize=UpperCamelCase_				)
							UpperCamelCase__  :int				   =					alt_pipe.to(UpperCamelCase_				)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :str				   =					'''A painting of a squirrel eating a burger'''
							UpperCamelCase__  :List[Any]				   =					torch.manual_seed(0				)
							UpperCamelCase__  :List[str]				   =					alt_pipe(
							    [prompt]       ,  generator=UpperCamelCase_       ,  num_inference_steps=2       ,  output_type='''np'''       ,  image=UpperCamelCase_       ,  ).images

							assert image.shape == (1, 32, 32, 3)




		@unittest.skipIf(torch_device != '''cuda'''       ,  '''This test requires a GPU'''				)
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/img2img/sketch-mountains-input.jpg'''				)
							# resize to resolution that is divisible by 8 but not 16 or 32
							UpperCamelCase__  :Optional[int]				   =					init_image.resize((760, 504)				)

							UpperCamelCase__  :Any				   =					'''BAAI/AltDiffusion'''
							UpperCamelCase__  :Union[str, Any]				   =					AltDiffusionImgaImgPipeline.from_pretrained(
							    UpperCamelCase_       ,  safety_checker=UpperCamelCase_       ,  )
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Any				   =					'''A fantasy landscape, trending on artstation'''

							UpperCamelCase__  :Optional[int]				   =					torch.manual_seed(0				)
							UpperCamelCase__  :str				   =					pipe(
							    prompt=UpperCamelCase_       ,  image=UpperCamelCase_       ,  strength=0.75       ,  guidance_scale=7.5       ,  generator=UpperCamelCase_       ,  output_type='''np'''       ,  )
							UpperCamelCase__  :Tuple				   =					output.images[0]

							UpperCamelCase__  :Optional[int]				   =					image[255:258, 383:386, -1]

							assert image.shape == (504, 760, 3)
							UpperCamelCase__  :str				   =					np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2



@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[Any]				   =					load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/img2img/sketch-mountains-input.jpg'''				)
							UpperCamelCase__  :Any				   =					init_image.resize((768, 512)				)
							UpperCamelCase__  :Any				   =					load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''				)

							UpperCamelCase__  :str				   =					'''BAAI/AltDiffusion'''
							UpperCamelCase__  :str				   =					AltDiffusionImgaImgPipeline.from_pretrained(
							    UpperCamelCase_       ,  safety_checker=UpperCamelCase_       ,  )
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Optional[int]				   =					'''A fantasy landscape, trending on artstation'''

							UpperCamelCase__  :List[str]				   =					torch.manual_seed(0				)
							UpperCamelCase__  :Any				   =					pipe(
							    prompt=UpperCamelCase_       ,  image=UpperCamelCase_       ,  strength=0.75       ,  guidance_scale=7.5       ,  generator=UpperCamelCase_       ,  output_type='''np'''       ,  )
							UpperCamelCase__  :Any				   =					output.images[0]

							assert image.shape == (512, 768, 3)
							# img2img is flaky across GPUs even in fp32, so using MAE here
							assert np.abs(expected_image - image				).max() < 1e-2



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

from decimal import Decimal

from numpy import array


def      a (     __a  )    ->      list[list[float]]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					Decimal

					# Check if the provided matrix has 2 rows and 2 columns
					# since this implementation only works for 2x2 matrices
					if len(__a  ) == 2 and len(matrix[0]  ) == 2 and len(matrix[1]  ) == 2:
										# Calculate the determinant of the matrix
										UpperCamelCase__  :str				   =					float(
										    d(matrix[0][0]  ) * d(matrix[1][1]  ) - d(matrix[1][0]  ) * d(matrix[0][1]  )  )
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''  )

										# Creates a copy of the matrix with swapped positions of the elements
										UpperCamelCase__  :List[Any]				   =					[[0.0, 0.0], [0.0, 0.0]]
										UpperCamelCase__   ,							UpperCamelCase__  :Union[str, Any]				   =					matrix[1][1], matrix[0][0]
										UpperCamelCase__   ,							UpperCamelCase__  :Dict				   =					-matrix[1][0], -matrix[0][1]

										# Calculate the inverse of the matrix
										return [
										    [(float(d(__a  )  ) / determinant) or 0.0 for n in row] for row in swapped_matrix
										]
					elif (
					    len(__a  ) == 3
					    and len(matrix[0]  ) == 3
					    and len(matrix[1]  ) == 3
					    and len(matrix[2]  ) == 3
					):
										# Calculate the determinant of the matrix using Sarrus rule
										UpperCamelCase__  :int				   =					float(
										    (
										        (d(matrix[0][0]  ) * d(matrix[1][1]  ) * d(matrix[2][2]  ))
										        + (d(matrix[0][1]  ) * d(matrix[1][2]  ) * d(matrix[2][0]  ))
										        + (d(matrix[0][2]  ) * d(matrix[1][0]  ) * d(matrix[2][1]  ))
										    )
										    - (
										        (d(matrix[0][2]  ) * d(matrix[1][1]  ) * d(matrix[2][0]  ))
										        + (d(matrix[0][1]  ) * d(matrix[1][0]  ) * d(matrix[2][2]  ))
										        + (d(matrix[0][0]  ) * d(matrix[1][2]  ) * d(matrix[2][1]  ))
										    )  )
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''  )

										# Creating cofactor matrix
										UpperCamelCase__  :List[Any]				   =					[
										    [d(0.0  ), d(0.0  ), d(0.0  )],
										    [d(0.0  ), d(0.0  ), d(0.0  )],
										    [d(0.0  ), d(0.0  ), d(0.0  )],
										]
										UpperCamelCase__  :Tuple				   =					(d(matrix[1][1]  ) * d(matrix[2][2]  )) - (
										    d(matrix[1][2]  ) * d(matrix[2][1]  )
										)
										UpperCamelCase__  :List[str]				   =					-(
										    (d(matrix[1][0]  ) * d(matrix[2][2]  )) - (d(matrix[1][2]  ) * d(matrix[2][0]  ))
										)
										UpperCamelCase__  :Union[str, Any]				   =					(d(matrix[1][0]  ) * d(matrix[2][1]  )) - (
										    d(matrix[1][1]  ) * d(matrix[2][0]  )
										)
										UpperCamelCase__  :List[Any]				   =					-(
										    (d(matrix[0][1]  ) * d(matrix[2][2]  )) - (d(matrix[0][2]  ) * d(matrix[2][1]  ))
										)
										UpperCamelCase__  :Optional[int]				   =					(d(matrix[0][0]  ) * d(matrix[2][2]  )) - (
										    d(matrix[0][2]  ) * d(matrix[2][0]  )
										)
										UpperCamelCase__  :List[Any]				   =					-(
										    (d(matrix[0][0]  ) * d(matrix[2][1]  )) - (d(matrix[0][1]  ) * d(matrix[2][0]  ))
										)
										UpperCamelCase__  :List[Any]				   =					(d(matrix[0][1]  ) * d(matrix[1][2]  )) - (
										    d(matrix[0][2]  ) * d(matrix[1][1]  )
										)
										UpperCamelCase__  :List[str]				   =					-(
										    (d(matrix[0][0]  ) * d(matrix[1][2]  )) - (d(matrix[0][2]  ) * d(matrix[1][0]  ))
										)
										UpperCamelCase__  :Dict				   =					(d(matrix[0][0]  ) * d(matrix[1][1]  )) - (
										    d(matrix[0][1]  ) * d(matrix[1][0]  )
										)

										# Transpose the cofactor matrix (Adjoint matrix)
										UpperCamelCase__  :Optional[Any]				   =					array(__a  )
										for i in range(3  ):
															for j in range(3  ):
																				UpperCamelCase__  :Optional[Any]				   =					cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
										UpperCamelCase__  :Any				   =					array(__a  )
										for i in range(3  ):
															for j in range(3  ):
																				inverse_matrix[i][j] /= d(__a  )

        # Calculate the inverse of the matrix
										return [[float(d(__a  )  ) or 0.0 for n in row] for row in inverse_matrix]
					raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''  )