


import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Optional[int]:
             torch.manual_seed(0  )
             a_      :	Optional[int]							=						UNetaDModel(
                 block_out_channels=(3_2, 6_4)  ,							layers_per_block=2  ,							sample_size=3_2  ,							in_channels=3  ,							out_channels=3  ,							down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,							up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,							)
             return model
      @property
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    str:
             torch.manual_seed(0  )
             a_      :	Any							=						VQModel(
                 block_out_channels=[3_2, 6_4]  ,							in_channels=3  ,							out_channels=3  ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,							latent_channels=3  ,							)
             return model
      @property
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    List[Any]:
             torch.manual_seed(0  )
             a_      :	int							=						CLIPTextConfig(
                 bos_token_id=0  ,							eos_token_id=2  ,							hidden_size=3_2  ,							intermediate_size=3_7  ,							layer_norm_eps=1E-05  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							pad_token_id=1  ,							vocab_size=1_0_0_0  ,							)
             return CLIPTextModel(SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Optional[Any]:
             a_      :	int							=						self.dummy_uncond_unet
             a_      :	List[str]							=						DDIMScheduler()
             a_      :	List[Any]							=						self.dummy_vq_model

             a_      :	List[Any]							=						LDMPipeline(unet=SCREAMING_SNAKE_CASE__  ,							vqvae=SCREAMING_SNAKE_CASE__  ,							scheduler=SCREAMING_SNAKE_CASE__  )
             ldm.to(SCREAMING_SNAKE_CASE__  )
             ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

             a_      :	List[Any]							=						torch.manual_seed(0  )
             a_      :	Any							=						ldm(generator=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=2  ,							output_type='numpy'  ).images

             a_      :	Dict							=						torch.manual_seed(0  )
             a_      :	str							=						ldm(generator=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=2  ,							output_type='numpy'  ,							return_dict=SCREAMING_SNAKE_CASE__  )[0]

             a_      :	Optional[int]							=						image[0, -3:, -3:, -1]
             a_      :	Union[str, Any]							=						image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)
             a_      :	str							=						np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]  )
             a_      :	str							=						1E-2 if torch_device != 'mps' else 3E-2

             assert np.abs(image_slice.flatten() - expected_slice  ).max() < tolerance
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < tolerance
@slow
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Any:
             a_      :	Dict							=						LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'  )
             ldm.to(SCREAMING_SNAKE_CASE__  )
             ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						torch.manual_seed(0  )
             a_      :	Any							=						ldm(generator=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=5  ,							output_type='numpy'  ).images

             a_      :	int							=						image[0, -3:, -3:, -1]

             assert image.shape == (1, 2_5_6, 2_5_6, 3)
             a_      :	Optional[Any]							=						np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]  )
             a_      :	str							=						1E-2 if torch_device != 'mps' else 3E-2

             assert np.abs(image_slice.flatten() - expected_slice  ).max() < tolerance






import unittest

import numpy as np







def  SCREAMING_SNAKE_CASE_		(    __A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray | None = None						,					)			->	np.ndarray:

       """simple docstring"""

       a_      :	List[str]							=						np.shape(__A       )
       a_      :	Any							=						np.shape(__A       )
       a_      :	List[Any]							=						np.shape(__A       )

       if shape_a[0] != shape_b[0]:
              a_      :	List[Any]							=						(
                  'Expected the same number of rows for A and B. '
                  F"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(__A       )

       if shape_b[1] != shape_c[1]:
              a_      :	int							=						(
                  'Expected the same number of columns for B and C. '
                  F"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(__A       )

       a_      :	Any							=						pseudo_inv
       if a_inv is None:
              try:
                     a_      :	List[str]							=						np.linalg.inv(__A       )
              except np.linalg.LinAlgError:
                     raise ValueError(
                         'Input matrix A is not invertible. Cannot compute Schur complement.'       )

       return mat_c - mat_b.T @ a_inv @ mat_b
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    None:
             a_      :	str							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Any							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1], [6, 3]]  )

             a_      :	int							=						schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[Any]							=						np.block([[a, b], [b.T, c]]  )

             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )

             self.assertAlmostEqual(SCREAMING_SNAKE_CASE__  ,							det_a * det_s  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    None:
             a_      :	Optional[int]							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	str							=						np.array([[2, 1], [6, 3]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    None:
             a_      :	Tuple							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1, 3], [6, 3, 5]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   unittest.main()


