

"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()


class 		_A							(      unittest.TestCase       ):





      @property
      def        _lowerCamelCase	(	self		) ->       Any:
         torch.manual_seed(0		)
         lowerCamelCase__						       =      UNetaDModel(
             block_out_channels=(32, 64)     ,    layers_per_block=2     ,    sample_size=32     ,    in_channels=3     ,    out_channels=3     ,    down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,    up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,    )
         return model





      def        _lowerCamelCase	(	self		) ->       Any:
         lowerCamelCase__						       =      "cpu"
         lowerCamelCase__						       =      self.dummy_uncond_unet
         lowerCamelCase__						       =      DDPMScheduler()

         lowerCamelCase__						       =      DDPMPipeline(unet=SCREAMING_SNAKE_CASE__     ,    scheduler=SCREAMING_SNAKE_CASE__		)
         ddpm.to(SCREAMING_SNAKE_CASE__		)
         ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      torch.Generator(device=SCREAMING_SNAKE_CASE__		).manual_seed(0		)
         lowerCamelCase__						       =      ddpm(generator=SCREAMING_SNAKE_CASE__     ,    num_inference_steps=2     ,    output_type="numpy"		).images

         lowerCamelCase__						       =      torch.Generator(device=SCREAMING_SNAKE_CASE__		).manual_seed(0		)
         lowerCamelCase__						       =      ddpm(generator=SCREAMING_SNAKE_CASE__     ,    num_inference_steps=2     ,    output_type="numpy"     ,    return_dict=SCREAMING_SNAKE_CASE__		)[0]

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

         assert image.shape == (1, 32, 32, 3)
         lowerCamelCase__						       =      np.array(
             [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.0_00, 1.199e-03, 2.648e-04, 5.101e-04]		)

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





      def        _lowerCamelCase	(	self		) ->       Union[str, Any]:
         lowerCamelCase__						       =      self.dummy_uncond_unet
         lowerCamelCase__						       =      DDPMScheduler(prediction_type="sample"		)

         lowerCamelCase__						       =      DDPMPipeline(unet=SCREAMING_SNAKE_CASE__     ,    scheduler=SCREAMING_SNAKE_CASE__		)
         ddpm.to(SCREAMING_SNAKE_CASE__		)
         ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      torch.manual_seed(0		)
         lowerCamelCase__						       =      ddpm(generator=SCREAMING_SNAKE_CASE__     ,    num_inference_steps=2     ,    output_type="numpy"		).images

         lowerCamelCase__						       =      torch.manual_seed(0		)
         lowerCamelCase__						       =      ddpm(generator=SCREAMING_SNAKE_CASE__     ,    num_inference_steps=2     ,    output_type="numpy"		)[0]

         lowerCamelCase__						       =      image[0, -3:, -3:, -1]
         lowerCamelCase__						       =      image_eps[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         lowerCamelCase__						       =      1e-2 if torch_device != "mps" else 3e-2
         assert np.abs(image_slice.flatten() - image_eps_slice.flatten()		).max() < tolerance






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





      def        _lowerCamelCase	(	self		) ->       str:
         lowerCamelCase__						       =      "google/ddpm-cifar10-32"

         lowerCamelCase__						       =      UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      DDPMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      DDPMPipeline(unet=SCREAMING_SNAKE_CASE__     ,    scheduler=SCREAMING_SNAKE_CASE__		)
         ddpm.to(SCREAMING_SNAKE_CASE__		)
         ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      torch.manual_seed(0		)
         lowerCamelCase__						       =      ddpm(generator=SCREAMING_SNAKE_CASE__     ,    output_type="numpy"		).images

         lowerCamelCase__						       =      image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         lowerCamelCase__						       =      np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]		)
         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2







"""simple docstring"""




import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )

   lowerCamelCase__						       =      np.zeros((rows, 1)						,     dtype=A__ )
   for row in reversed(range(A__ ) ):
      lowerCamelCase__						       =      0
      for col in range(row + 1						,     A__ ):
         total += coefficients[row, col] * x[col]

      lowerCamelCase__						       =      (vector[row] - total) / coefficients[row, row]

   return x



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )
   if rows != columns:
      return np.array(()						,     dtype=A__ )

   # augmented matrix
   lowerCamelCase__						       =      np.concatenate((coefficients, vector)						,     axis=1 )
   lowerCamelCase__						       =      augmented_mat.astype("float64" )

   # scale the matrix leaving it triangular
   for row in range(rows - 1 ):
      lowerCamelCase__						       =      augmented_mat[row, row]
      for col in range(row + 1						,     A__ ):
         lowerCamelCase__						       =      augmented_mat[col, row] / pivot
         augmented_mat[col, :] -= factor * augmented_mat[row, :]

   lowerCamelCase__						       =      retroactive_resolution(
       augmented_mat[:, 0:columns]						,     augmented_mat[:, columns : columns + 1] )

   return x


if __name__ == "__main__":
     import doctest

     doctest.testmod()




