from __future__ import annotations

from decimal import Decimal

from numpy import array
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							str       =	Decimal

   # Check if the provided matrix has 2 rows and 2 columns
   # since this implementation only works for 2x2 matrices
   if len(lowercase					) == 2 and len(matrix[0]					) == 2 and len(matrix[1]					) == 2:
      # Calculate the determinant of the matrix
      lowerCAmelCase_:							Union[str, Any]       =	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
      lowerCAmelCase_:							Optional[int]       =	[[0.0, 0.0], [0.0, 0.0]]
      lowerCAmelCase_  ,	lowerCAmelCase_:							List[Any]       =	matrix[1][1], matrix[0][0]
      lowerCAmelCase_  ,	lowerCAmelCase_:							List[Any]       =	-matrix[1][0], -matrix[0][1]

      # Calculate the inverse of the matrix
      return [
          [(float(d(lowercase					)					) / determinant) or 0.0 for n in row] for row in swapped_matrix
      ]
   elif (
       len(lowercase					) == 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
      lowerCAmelCase_:							str       =	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
      lowerCAmelCase_:							List[str]       =	[
          [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					)],
      ]
      lowerCAmelCase_:							List[Any]       =	(d(matrix[1][1]					) * d(matrix[2][2]					)) - (
          d(matrix[1][2]					) * d(matrix[2][1]					)
      )
      lowerCAmelCase_:							str       =	-(
          (d(matrix[1][0]					) * d(matrix[2][2]					)) - (d(matrix[1][2]					) * d(matrix[2][0]					))
      )
      lowerCAmelCase_:							str       =	(d(matrix[1][0]					) * d(matrix[2][1]					)) - (
          d(matrix[1][1]					) * d(matrix[2][0]					)
      )
      lowerCAmelCase_:							List[Any]       =	-(
          (d(matrix[0][1]					) * d(matrix[2][2]					)) - (d(matrix[0][2]					) * d(matrix[2][1]					))
      )
      lowerCAmelCase_:							Tuple       =	(d(matrix[0][0]					) * d(matrix[2][2]					)) - (
          d(matrix[0][2]					) * d(matrix[2][0]					)
      )
      lowerCAmelCase_:							Union[str, Any]       =	-(
          (d(matrix[0][0]					) * d(matrix[2][1]					)) - (d(matrix[0][1]					) * d(matrix[2][0]					))
      )
      lowerCAmelCase_:							List[str]       =	(d(matrix[0][1]					) * d(matrix[1][2]					)) - (
          d(matrix[0][2]					) * d(matrix[1][1]					)
      )
      lowerCAmelCase_:							Optional[int]       =	-(
          (d(matrix[0][0]					) * d(matrix[1][2]					)) - (d(matrix[0][2]					) * d(matrix[1][0]					))
      )
      lowerCAmelCase_:							List[str]       =	(d(matrix[0][0]					) * d(matrix[1][1]					)) - (
          d(matrix[0][1]					) * d(matrix[1][0]					)
      )

      # Transpose the cofactor matrix (Adjoint matrix)
      lowerCAmelCase_:							Tuple       =	array(lowercase					)
      for i in range(3					):
         for j in range(3					):
            lowerCAmelCase_:							Optional[int]       =	cofactor_matrix[j][i]

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

        # Calculate the inverse of the matrix
      return [[float(d(lowercase					)					) or 0.0 for n in row] for row in inverse_matrix]
   raise ValueError("Please provide a matrix of size 2x2 or 3x3."					)
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a  :      List[str]            =    False

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    pass





@nightly
@require_torch_gpu
class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def      _a    (    self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							List[str]       =	torch.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCamelCase__ )
          lowerCAmelCase_:							int       =	VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__ )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	generator.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	VersatileDiffusionTextToImagePipeline.from_pretrained(
           "shi-labs/versatile-diffusion"       ,						torch_dtype=torch.floataa )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							str       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							int       =	torch.manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=50       ,						output_type="numpy" ).images

       lowerCAmelCase_:							List[Any]       =	image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCAmelCase_:							Optional[int]       =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

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