



'''simple docstring'''
import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__lowerCAmelCase												=	logging.get_logger(__name__)

enable_full_determinism()


class       _lowerCAmelCase   (   __lowerCamelCase					,							__lowerCamelCase					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						UNetaDModel
							lowerCAmelCase_ 						=						"""sample"""

							@property
							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case       =  4
														_snake_case       =  3
														_snake_case       =  (32, 32)

														_snake_case       =  floats_tensor((batch_size, num_channels) + sizes  ).to(__lowercase  )
														_snake_case       =  torch.tensor([10]  ).to(__lowercase  )

														return {"sample": noise, "timestep": time_step}

							@property
							def 	lowercase							(self  )					->       List[str]:
														return (3, 32, 32)

							@property
							def 	lowercase							(self  )					->       List[Any]:
														return (3, 32, 32)





							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case       =  {
														    '''block_out_channels''': (32, 64),
														    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
														    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
														    '''attention_head_dim''': 3,
														    '''out_channels''': 3,
														    '''in_channels''': 3,
														    '''layers_per_block''': 2,
														    '''sample_size''': 32,
														}
														_snake_case       =  self.dummy_input
														return init_dict, inputs_dict


class       _lowerCAmelCase   (   __lowerCamelCase					,							__lowerCamelCase					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						UNetaDModel
							lowerCAmelCase_ 						=						"""sample"""

							@property
							def 	lowercase							(self  )					->       Any:
														_snake_case       =  4
														_snake_case       =  4
														_snake_case       =  (32, 32)

														_snake_case       =  floats_tensor((batch_size, num_channels) + sizes  ).to(__lowercase  )
														_snake_case       =  torch.tensor([10]  ).to(__lowercase  )

														return {"sample": noise, "timestep": time_step}

							@property
							def 	lowercase							(self  )					->       Optional[int]:
														return (4, 32, 32)

							@property
							def 	lowercase							(self  )					->       Dict:
														return (4, 32, 32)

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  {
														    '''sample_size''': 32,
														    '''in_channels''': 4,
														    '''out_channels''': 4,
														    '''layers_per_block''': 2,
														    '''block_out_channels''': (32, 64),
														    '''attention_head_dim''': 32,
														    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
														    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
														}
														_snake_case       =  self.dummy_input
														return init_dict, inputs_dict

							def 	lowercase							(self  )					->       int:
														_snake_case       =  UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""  ,  output_loading_info=__lowercase  )

														self.assertIsNotNone(__lowercase  )
														self.assertEqual(len(loading_info["""missing_keys"""]  )  ,  0  )

														model.to(__lowercase  )
														_snake_case       =  model(**self.dummy_input  ).sample

														assert image is not None, "Make sure output is not None"

							@unittest.skipIf(torch_device != """cuda"""  ,  """This test is supposed to run on GPU"""  )
							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""  ,  output_loading_info=__lowercase  )
														model.to(__lowercase  )
														_snake_case       =  model(**self.dummy_input  ).sample

														assert image is not None, "Make sure output is not None"

							@unittest.skipIf(torch_device != """cuda"""  ,  """This test is supposed to run on GPU"""  )
							def 	lowercase							(self  )					->       Tuple:
														# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
														_snake_case       =  UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""  ,  output_loading_info=__lowercase  )
														model_accelerate.to(__lowercase  )
														model_accelerate.eval()

														_snake_case       =  torch.randn(
														    1  ,  model_accelerate.config.in_channels  ,  model_accelerate.config.sample_size  ,  model_accelerate.config.sample_size  ,  generator=torch.manual_seed(0  )  ,  )
														_snake_case       =  noise.to(__lowercase  )
														_snake_case       =  torch.tensor([10] * noise.shape[0]  ).to(__lowercase  )

														_snake_case       =  model_accelerate(__lowercase  ,  __lowercase  )['''sample''']

														# two models don't need to stay in the device at the same time
														del model_accelerate
														torch.cuda.empty_cache()
														gc.collect()

														_snake_case       =  UNetaDModel.from_pretrained(
														    """fusing/unet-ldm-dummy-update"""  ,  output_loading_info=__lowercase  ,  low_cpu_mem_usage=__lowercase  )
														model_normal_load.to(__lowercase  )
														model_normal_load.eval()
														_snake_case       =  model_normal_load(__lowercase  ,  __lowercase  )['''sample''']

														assert torch_all_close(__lowercase  ,  __lowercase  ,  rtol=1e-3  )





							def 	lowercase							(self  )					->       str:
														_snake_case       =  UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""  )
														model.eval()
														model.to(__lowercase  )

														_snake_case       =  torch.randn(
														    1  ,  model.config.in_channels  ,  model.config.sample_size  ,  model.config.sample_size  ,  generator=torch.manual_seed(0  )  ,  )
														_snake_case       =  noise.to(__lowercase  )
														_snake_case       =  torch.tensor([10] * noise.shape[0]  ).to(__lowercase  )

														with torch.no_grad():
																					_snake_case       =  model(__lowercase  ,  __lowercase  ).sample

														_snake_case       =  output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														_snake_case       =  torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]  )
														# fmt: on

														self.assertTrue(torch_all_close(__lowercase  ,  __lowercase  ,  rtol=1e-3  )  )


class       _lowerCAmelCase   (   __lowerCamelCase					,							__lowerCamelCase					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						UNetaDModel
							lowerCAmelCase_ 						=						"""sample"""

							@property
							def 	lowercase							(self  ,  UpperCAmelCase=(32, 32)  )					->       Any:
														_snake_case       =  4
														_snake_case       =  3

														_snake_case       =  floats_tensor((batch_size, num_channels) + sizes  ).to(__lowercase  )
														_snake_case       =  torch.tensor(batch_size * [10]  ).to(dtype=torch.intaa  ,  device=__lowercase  )

														return {"sample": noise, "timestep": time_step}

							@property
							def 	lowercase							(self  )					->       Union[str, Any]:
														return (3, 32, 32)

							@property
							def 	lowercase							(self  )					->       Any:
														return (3, 32, 32)

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  {
														    '''block_out_channels''': [32, 64, 64, 64],
														    '''in_channels''': 3,
														    '''layers_per_block''': 1,
														    '''out_channels''': 3,
														    '''time_embedding_type''': '''fourier''',
														    '''norm_eps''': 1e-6,
														    '''mid_block_scale_factor''': math.sqrt(2.0  ),
														    '''norm_num_groups''': None,
														    '''down_block_types''': [
														        '''SkipDownBlock2D''',
														        '''AttnSkipDownBlock2D''',
														        '''SkipDownBlock2D''',
														        '''SkipDownBlock2D''',
														    ],
														    '''up_block_types''': [
														        '''SkipUpBlock2D''',
														        '''SkipUpBlock2D''',
														        '''AttnSkipUpBlock2D''',
														        '''SkipUpBlock2D''',
														    ],
														}
														_snake_case       =  self.dummy_input
														return init_dict, inputs_dict

							@slow
							def 	lowercase							(self  )					->       int:
														_snake_case       =  UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""  ,  output_loading_info=__lowercase  )
														self.assertIsNotNone(__lowercase  )
														self.assertEqual(len(loading_info["""missing_keys"""]  )  ,  0  )

														model.to(__lowercase  )
														_snake_case       =  self.dummy_input
														_snake_case       =  floats_tensor((4, 3) + (256, 256)  ).to(__lowercase  )
														_snake_case       =  noise
														_snake_case       =  model(**__lowercase  )

														assert image is not None, "Make sure output is not None"

							@slow
							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""  )
														model.to(__lowercase  )

														_snake_case       =  4
														_snake_case       =  3
														_snake_case       =  (256, 256)

														_snake_case       =  torch.ones((batch_size, num_channels) + sizes  ).to(__lowercase  )
														_snake_case       =  torch.tensor(batch_size * [1e-4]  ).to(__lowercase  )

														with torch.no_grad():
																					_snake_case       =  model(__lowercase  ,  __lowercase  ).sample

														_snake_case       =  output[0, -3:, -3:, -1].flatten().cpu()
														# fmt: off
														_snake_case       =  torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]  )
														# fmt: on

														self.assertTrue(torch_all_close(__lowercase  ,  __lowercase  ,  rtol=1e-2  )  )

							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case       =  UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""  )
														model.to(__lowercase  )

														_snake_case       =  4
														_snake_case       =  3
														_snake_case       =  (32, 32)

														_snake_case       =  torch.ones((batch_size, num_channels) + sizes  ).to(__lowercase  )
														_snake_case       =  torch.tensor(batch_size * [1e-4]  ).to(__lowercase  )

														with torch.no_grad():
																					_snake_case       =  model(__lowercase  ,  __lowercase  ).sample

														_snake_case       =  output[0, -3:, -3:, -1].flatten().cpu()
														# fmt: off
														_snake_case       =  torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]  )
														# fmt: on

														self.assertTrue(torch_all_close(__lowercase  ,  __lowercase  ,  rtol=1e-2  )  )





							def 	lowercase							(self  )					->       Optional[Any]:
														# not required for this model
														pass

'''simple docstring'''
from __future__ import annotations






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# We need to create solution object to save path.
							_snake_case       =  [[0 for _ in range(_SCREAMING_SNAKE_CASE				)] for _ in range(_SCREAMING_SNAKE_CASE				)]
							_snake_case       =  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							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# Final check point.
							if i == j == (size - 1):
														_snake_case       =  1
														return True

							_snake_case       =  (not i < 0) and (not j < 0)  # Check lower bounds
							_snake_case       =  (i < size) and (j < size)  # Check upper bounds

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

																					_snake_case       =  0
																					return False
							return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()