

from itertools import permutations
def 		UpperCamelCase_(    ) ->				List[Any]:
 UpperCAmelCase__           =       list(map(''.join						,					permutations('0123456789'     )     )     )
 return result[99_99_99]


if __name__ == "__main__":
 print(solution())






import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin



class 					lowercase		( _UpperCamelCase				,       _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       UNetaDModel
 __SCREAMING_SNAKE_CASE      							=       """sample"""




 @property
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       4
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       16

  UpperCAmelCase__           =       floats_tensor((batch_size, num_features, seq_len)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([10] * batch_size				).to(__a				)

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




 @property
 def 						UpperCamelCase__							(self				)				->      Union[str, Any]:






  """simple docstring"""





  return (4, 14, 16)




 @property
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  return (4, 14, 16)




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






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  pass




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  super().test_determinism()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_outputs_equivalence()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  super().test_from_save_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  super().test_from_save_pretrained_variant()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  super().test_model_from_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  super().test_output()




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






  """simple docstring"""





  UpperCAmelCase__           =       {
      'block_out_channels': (32, 64, 128, 256),
      'in_channels': 14,
      'out_channels': 14,
      'time_embedding_type': 'positional',
      'use_timestep_embedding': True,
      'flip_sin_to_cos': False,
      'freq_shift': 1.0,
      'out_block_type': 'OutConv1DBlock',
      'mid_block_type': 'MidResTemporalBlock1D',
      'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
      'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
      'act_fn': 'mish',
  }
  UpperCAmelCase__           =       self.dummy_input
  return init_dict, inputs_dict




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='unet'				)
  self.assertIsNotNone(__a				)
  self.assertEqual(len(loading_info['missing_keys']				) , 0				)

  model.to(__a				)
  UpperCAmelCase__           =       model(**self.dummy_input				)

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




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32' , subfolder='unet'				)
  torch.manual_seed(0				)
  if torch.cuda.is_available():
   torch.cuda.manual_seed_all(0				)

  UpperCAmelCase__           =       model.config.in_channels
  UpperCAmelCase__           =       16
  UpperCAmelCase__           =       torch.randn((1, seq_len, num_features)				).permute(
      0 , 2 , 1				)  # match original, we can update values and remove
  UpperCAmelCase__           =       torch.full((num_features,) , 0				)

  with torch.no_grad():
   UpperCAmelCase__           =       model(__a , __a				).sample.permute(0 , 2 , 1				)

  UpperCAmelCase__           =       output[0, -3:, -3:].flatten()
  # fmt: off
  UpperCAmelCase__           =       torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]				)
  # fmt: on
  self.assertTrue(torch.allclose(__a , __a , rtol=1E-3				)				)




 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  pass




 @slow
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       'harmonai/maestro-150k'
  UpperCAmelCase__           =       UNetaDModel.from_pretrained(__a , subfolder='unet'				)
  model.to(__a				)

  UpperCAmelCase__           =       65536
  UpperCAmelCase__           =       torch.sin(torch.arange(__a				)[None, None, :].repeat(1 , 2 , 1				)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([1]				).to(__a				)

  with torch.no_grad():
   UpperCAmelCase__           =       model(__a , __a				).sample

  UpperCAmelCase__           =       output.abs().sum()
  UpperCAmelCase__           =       output.abs().max()

  assert (output_sum - 2_24.08_96).abs() < 0.5
  assert (output_max - 0.06_07).abs() < 4E-4







class 					lowercase		( _UpperCamelCase				,       _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       UNetaDModel
 __SCREAMING_SNAKE_CASE      							=       """sample"""




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       4
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       16

  UpperCAmelCase__           =       floats_tensor((batch_size, num_features, seq_len)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([10] * batch_size				).to(__a				)

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




 @property
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  return (4, 14, 16)




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return (4, 14, 1)




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_determinism()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  super().test_outputs_equivalence()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  super().test_from_save_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  super().test_from_save_pretrained_variant()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_model_from_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.prepare_init_args_and_inputs_for_common()
  UpperCAmelCase__           =       self.model_class(**__a				)
  model.to(__a				)
  model.eval()

  with torch.no_grad():
   UpperCAmelCase__           =       model(**__a				)

   if isinstance(__a , __a				):
    UpperCAmelCase__           =       output.sample

  self.assertIsNotNone(__a				)
  UpperCAmelCase__           =       torch.Size((inputs_dict['sample'].shape[0], 1)				)
  self.assertEqual(output.shape , __a , 'Input and output shapes do not match'				)




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       {
      'in_channels': 14,
      'out_channels': 14,
      'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
      'up_block_types': [],
      'out_block_type': 'ValueFunction',
      'mid_block_type': 'ValueFunctionMidBlock1D',
      'block_out_channels': [32, 64, 128, 256],
      'layers_per_block': 1,
      'downsample_each_block': True,
      'use_timestep_embedding': True,
      'freq_shift': 1.0,
      'flip_sin_to_cos': False,
      'time_embedding_type': 'positional',
      'act_fn': 'mish',
  }
  UpperCAmelCase__           =       self.dummy_input
  return init_dict, inputs_dict




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='value_function'				)
  self.assertIsNotNone(__a				)
  self.assertEqual(len(vf_loading_info['missing_keys']				) , 0				)

  value_function.to(__a				)
  UpperCAmelCase__           =       value_function(**self.dummy_input				)

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




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='value_function'				)
  torch.manual_seed(0				)
  if torch.cuda.is_available():
   torch.cuda.manual_seed_all(0				)

  UpperCAmelCase__           =       value_function.config.in_channels
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       torch.randn((1, seq_len, num_features)				).permute(
      0 , 2 , 1				)  # match original, we can update values and remove
  UpperCAmelCase__           =       torch.full((num_features,) , 0				)

  with torch.no_grad():
   UpperCAmelCase__           =       value_function(__a , __a				).sample

  # fmt: off
  UpperCAmelCase__           =       torch.tensor([1_65.25] * seq_len				)
  # fmt: on
  self.assertTrue(torch.allclose(__a , __a , rtol=1E-3				)				)




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  pass



