






import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  DiTPipeline
      __snake_case       =  CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
      __snake_case       =  PipelineTesterMixin.required_optional_params - {
          '''latents''',
          '''num_images_per_prompt''',
          '''callback''',
          '''callback_steps''',
      }
      __snake_case       =  CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
      __snake_case       =  False







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Tuple:




       """simple docstring"""


       torch.manual_seed(0  )
       a		  =      TransformeraDModel(
           sample_size=16 ,					num_layers=2 ,					patch_size=4 ,					attention_head_dim=8 ,					num_attention_heads=2 ,					in_channels=4 ,					out_channels=8 ,					attention_bias=__UpperCAmelCase ,					activation_fn='''gelu-approximate''' ,					num_embeds_ada_norm=1_000 ,					norm_type='''ada_norm_zero''' ,					norm_elementwise_affine=__UpperCAmelCase ,					)
       a		  =      AutoencoderKL()
       a		  =      DDIMScheduler()
       a		  =      {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
       return components







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						Optional[Any]=0  )    ->Any:




       """simple docstring"""


       if str(__UpperCAmelCase  ).startswith('''mps'''  ):
        a		  =      torch.manual_seed(__UpperCAmelCase  )
       else:
        a		  =      torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
       a		  =      {
           '''class_labels''': [1],
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->int:




       """simple docstring"""


       a		  =      '''cpu'''

       a		  =      self.get_dummy_components()
       a		  =      self.pipeline_class(**__UpperCAmelCase  )
       pipe.to(__UpperCAmelCase  )
       pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

       a		  =      self.get_dummy_inputs(__UpperCAmelCase  )
       a		  =      pipe(**__UpperCAmelCase  ).images
       a		  =      image[0, -3:, -3:, -1]

       self.assertEqual(image.shape ,					(1, 16, 16, 3)  )
       a		  =      np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]  )
       a		  =      np.abs(image_slice.flatten() - expected_slice  ).max()
       self.assertLessEqual(__UpperCAmelCase ,					1e-3  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Union[str, Any]:




       """simple docstring"""


       self._test_inference_batch_single_identical(relax_max_difference=__UpperCAmelCase ,					expected_max_diff=1e-3  )







      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available() ,					reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,					)
      def       __lowerCAmelCase					(      self      :						Any  )    ->int:




       """simple docstring"""


       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )






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






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->str:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->int:




       """simple docstring"""


       a		  =      torch.manual_seed(0  )

       a		  =      DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''  )
       pipe.to('''cuda'''  )

       a		  =      ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
       a		  =      pipe.get_label_ids(__UpperCAmelCase  )

       a		  =      pipe(__UpperCAmelCase ,					generator=__UpperCAmelCase ,					num_inference_steps=40 ,					output_type='''np'''  ).images

       for word, image in zip(__UpperCAmelCase ,					__UpperCAmelCase  ):
        a		  =      load_numpy(
            F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""  )
        assert np.abs((expected_image - image).max()  ) < 1e-2







      def       __lowerCAmelCase					(      self      :						str  )    ->Tuple:




       """simple docstring"""


       a		  =      DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''  )
       a		  =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
       pipe.to('''cuda'''  )

       a		  =      ['''vase''', '''umbrella''']
       a		  =      pipe.get_label_ids(__UpperCAmelCase  )

       a		  =      torch.manual_seed(0  )
       a		  =      pipe(__UpperCAmelCase ,					generator=__UpperCAmelCase ,					num_inference_steps=25 ,					output_type='''np'''  ).images

       for word, image in zip(__UpperCAmelCase ,					__UpperCAmelCase  ):
        a		  =      load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            F"""/dit/{word}_512.npy"""  )

        assert np.abs((expected_image - image).max()  ) < 1e-1










from __future__ import annotations

UpperCAmelCase__				   =		list[list[int]]

# assigning initial values to the grid
UpperCAmelCase__				   =		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
UpperCAmelCase__				   =		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def 						_a    (      a :Matrix	,							a :int	,							a :int	,							a :int )							->			bool:
 for i in range(9 ):
  if grid[row][i] == n or grid[i][column] == n:
   return False

 for i in range(3 ):
  for j in range(3 ):
   if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
    return False

 return True
def 						_a    (      a :Matrix )							->			tuple[int, int] | None:
 for i in range(9 ):
  for j in range(9 ):
   if grid[i][j] == 0:
    return i, j
 return None
def 						_a    (      a :Matrix )							->			Matrix | None:
 if location := find_empty_location(a ):
  a  ,     a		  =      location
 else:
  # If the location is ``None``, then the grid is solved.
  return grid

 for digit in range(1	,							10 ):
  if is_safe(a	,							a	,							a	,							a ):
   a		  =      digit

   if sudoku(a ) is not None:
    return grid

   a		  =      0

 return None
def 						_a    (      a :Matrix )							->			None:
 for row in grid:
  for cell in row:
   print(a	,							end=''' ''' )
  print()


if __name__ == "__main__":
     # make a copy of grid so that you can compare with the unmodified grid
     for example_grid in (initial_grid, no_solution):
          print("\nExample grid:\n" + "=" * 20)
          print_solution(example_grid)
          print("\nExample grid solution:")
          UpperCAmelCase__				   =		sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("Cannot find a solution.")


