





import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 							snake_case_    (		lowerCamelCase__					,unittest.TestCase						):
  A_      							=   DDIMPipeline
  A_      							=   UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  A_      							=   PipelineTesterMixin.required_optional_params - {
      'num_images_per_prompt',
      'latents',
      'callback',
      'callback_steps',
  }
  A_      							=   UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
  A_      							=   False






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






   '''simple docstring'''

   torch.manual_seed(0      )
   __lowerCAmelCase					:	int      =       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""")       ,  )
   __lowerCAmelCase					:	Optional[int]      =       DDIMScheduler()
   __lowerCAmelCase					:	Optional[int]      =       {"""unet""": unet, """scheduler""": scheduler}
   return components






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					Dict       ,  _snake_case			:					List[Any]=0      )->str:






   '''simple docstring'''

   if str(_snake_case      ).startswith("""mps"""      ):
    __lowerCAmelCase					:	Union[str, Any]      =       torch.manual_seed(_snake_case      )
   else:
    __lowerCAmelCase					:	List[Any]      =       torch.Generator(device=_snake_case      ).manual_seed(_snake_case      )
   __lowerCAmelCase					:	Any      =       {
       """batch_size""": 1,
       """generator""": generator,
       """num_inference_steps""": 2,
       """output_type""": """numpy""",
   }
   return inputs






  def 				UpperCAmelCase__						(							self			:					str      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       """cpu"""

   __lowerCAmelCase					:	Optional[int]      =       self.get_dummy_components()
   __lowerCAmelCase					:	List[str]      =       self.pipeline_class(**_snake_case      )
   pipe.to(_snake_case      )
   pipe.set_progress_bar_config(disable=_snake_case      )

   __lowerCAmelCase					:	int      =       self.get_dummy_inputs(_snake_case      )
   __lowerCAmelCase					:	str      =       pipe(**_snake_case      ).images
   __lowerCAmelCase					:	List[str]      =       image[0, -3:, -3:, -1]

   self.assertEqual(image.shape       ,  (1, 32, 32, 3)      )
   __lowerCAmelCase					:	int      =       np.array(
       [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]      )
   __lowerCAmelCase					:	Union[str, Any]      =       np.abs(image_slice.flatten() - expected_slice      ).max()
   self.assertLessEqual(_snake_case       ,  1E-3      )






  def 				UpperCAmelCase__						(							self			:					Any      )->Optional[int]:






   '''simple docstring'''

   super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Tuple:






   '''simple docstring'''

   super().test_save_load_local(expected_max_difference=3E-3      )






  def 				UpperCAmelCase__						(							self			:					str      )->List[str]:






   '''simple docstring'''

   super().test_save_load_optional_components(expected_max_difference=3E-3      )






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Optional[Any]:






   '''simple docstring'''

   super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






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






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






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       """google/ddpm-cifar10-32"""

   __lowerCAmelCase					:	Optional[Any]      =       UNetaDModel.from_pretrained(_snake_case      )
   __lowerCAmelCase					:	str      =       DDIMScheduler()

   __lowerCAmelCase					:	str      =       DDIMPipeline(unet=_snake_case       ,  scheduler=_snake_case      )
   ddim.to(_snake_case      )
   ddim.set_progress_bar_config(disable=_snake_case      )

   __lowerCAmelCase					:	str      =       torch.manual_seed(0      )
   __lowerCAmelCase					:	int      =       ddim(generator=_snake_case       ,  eta=0.0       ,  output_type="""numpy"""      ).images

   __lowerCAmelCase					:	Union[str, Any]      =       image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   __lowerCAmelCase					:	Optional[Any]      =       np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]      )

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






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       """google/ddpm-ema-bedroom-256"""

   __lowerCAmelCase					:	Union[str, Any]      =       UNetaDModel.from_pretrained(_snake_case      )
   __lowerCAmelCase					:	Dict      =       DDIMScheduler.from_pretrained(_snake_case      )

   __lowerCAmelCase					:	Dict      =       DDIMPipeline(unet=_snake_case       ,  scheduler=_snake_case      )
   ddpm.to(_snake_case      )
   ddpm.set_progress_bar_config(disable=_snake_case      )

   __lowerCAmelCase					:	Union[str, Any]      =       torch.manual_seed(0      )
   __lowerCAmelCase					:	Dict      =       ddpm(generator=_snake_case       ,  output_type="""numpy"""      ).images

   __lowerCAmelCase					:	Optional[Any]      =       image[0, -3:, -3:, -1]

   assert image.shape == (1, 256, 256, 3)
   __lowerCAmelCase					:	Optional[int]      =       np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]      )

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






from __future__ import annotations






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   bool:
 __lowerCAmelCase					:	Tuple      =       len(SCREAMING_SNAKE_CASE   )
 # We need to create solution object to save path.
 __lowerCAmelCase					:	str      =       [[0 for _ in range(SCREAMING_SNAKE_CASE   )] for _ in range(SCREAMING_SNAKE_CASE   )]
 __lowerCAmelCase					:	str      =       run_maze(SCREAMING_SNAKE_CASE   ,   0   ,   0   ,   SCREAMING_SNAKE_CASE   )
 if solved:
  print("""\n""".join(str(SCREAMING_SNAKE_CASE   ) for row in solutions   )   )
 else:
  print("""No solution exists!"""   )
 return solved






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   bool:
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )
 # Final check point.
 if i == j == (size - 1):
  __lowerCAmelCase					:	str      =       1
  return True

 __lowerCAmelCase					:	Optional[Any]      =       (not i < 0) and (not j < 0)  # Check lower bounds
 __lowerCAmelCase					:	Optional[Any]      =       (i < size) and (j < size)  # Check upper bounds

 if lower_flag and upper_flag:
  # check for already visited and block points.
  __lowerCAmelCase					:	int      =       (not solutions[i][j]) and (not maze[i][j])
  if block_flag:
   # check visited
   __lowerCAmelCase					:	Tuple      =       1

   # check for directions
   if (
       run_maze(SCREAMING_SNAKE_CASE   ,   i + 1   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
       or run_maze(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   j + 1   ,   SCREAMING_SNAKE_CASE   )
       or run_maze(SCREAMING_SNAKE_CASE   ,   i - 1   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
       or run_maze(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   j - 1   ,   SCREAMING_SNAKE_CASE   )
   ):
    return True

   __lowerCAmelCase					:	Tuple      =       0
   return False
 return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()