code: 
  
  
  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 						A__      (					snake_case__ ,      snake_case__ ,      unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	UNetaDModel
      __magic_name__    						=	'sample'
      @property
      def 	a_    ( self					):
           snake_case					=       4
           snake_case					=       1_4
           snake_case					=       1_6
  
           snake_case					=       floats_tensor((batch_size, num_features, seq_len)					).to(__snake_case					)
           snake_case					=       torch.tensor([1_0] * batch_size					).to(__snake_case					)
  
           return {"sample": noise, "timestep": time_step}
      @property
      def 	a_    ( self					):
           return (4, 1_4, 1_6)
      @property
      def 	a_    ( self					):
           return (4, 1_4, 1_6)
      def 	a_    ( self					):
           pass
      def 	a_    ( self					):
           pass
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           super().test_determinism()
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           super().test_outputs_equivalence()
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           super().test_from_save_pretrained()
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           super().test_from_save_pretrained_variant()
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           super().test_model_from_pretrained()
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           super().test_output()
      def 	a_    ( self					):
           snake_case					=       {
               '''block_out_channels''': (3_2, 6_4, 1_2_8, 2_5_6),
               '''in_channels''': 1_4,
               '''out_channels''': 1_4,
               '''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''',
           }
           snake_case					=       self.dummy_input
           return init_dict, inputs_dict
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           snake_case				,						snake_case					=       UNetaDModel.from_pretrained(
               '''bglick13/hopper-medium-v2-value-function-hor32'''    ,      output_loading_info=__snake_case    ,      subfolder='''unet'''					)
           self.assertIsNotNone(__snake_case					)
           self.assertEqual(len(loading_info['''missing_keys''']					)    ,      0					)
  
           model.to(__snake_case					)
           snake_case					=       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 	a_    ( self					):
           snake_case					=       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					)
  
           snake_case					=       model.config.in_channels
           snake_case					=       1_6
           snake_case					=       torch.randn((1, seq_len, num_features)					).permute(
               0    ,      2    ,      1					)  # match original, we can update values and remove
           snake_case					=       torch.full((num_features,)    ,      0					)
  
           with torch.no_grad():
                snake_case					=       model(__snake_case    ,      __snake_case					).sample.permute(0    ,      2    ,      1					)
  
           snake_case					=       output[0, -3:, -3:].flatten()
           # fmt: off
           snake_case					=       torch.tensor([-2.13_7172, 1.142_6016, 0.368_8687, -0.76_6922, 0.730_3146, 0.1103_8864, -0.476_0633, 0.1327_0172, 0.0259_1348]					)
           # fmt: on
           self.assertTrue(torch.allclose(__snake_case    ,      __snake_case    ,      rtol=1E-3					)					)
      def 	a_    ( self					):
           # Not implemented yet for this UNet
           pass
  
  
  
  
      @slow
      def 	a_    ( self					):
           snake_case					=       '''harmonai/maestro-150k'''
           snake_case					=       UNetaDModel.from_pretrained(__snake_case    ,      subfolder='''unet'''					)
           model.to(__snake_case					)
  
           snake_case					=       6_5_5_3_6
           snake_case					=       torch.sin(torch.arange(__snake_case					)[None, None, :].repeat(1    ,      2    ,      1					)					).to(__snake_case					)
           snake_case					=       torch.tensor([1]					).to(__snake_case					)
  
           with torch.no_grad():
                snake_case					=       model(__snake_case    ,      __snake_case					).sample
  
           snake_case					=       output.abs().sum()
           snake_case					=       output.abs().max()
  
           assert (output_sum - 224.0896).abs() < 0.5
           assert (output_max - 0.0607).abs() < 4E-4
  
  
  
  
  
  
  class 						A__      (					snake_case__ ,      snake_case__ ,      unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	UNetaDModel
      __magic_name__    						=	'sample'
      @property
      def 	a_    ( self					):
           snake_case					=       4
           snake_case					=       1_4
           snake_case					=       1_6
  
           snake_case					=       floats_tensor((batch_size, num_features, seq_len)					).to(__snake_case					)
           snake_case					=       torch.tensor([1_0] * batch_size					).to(__snake_case					)
  
           return {"sample": noise, "timestep": time_step}
      @property
      def 	a_    ( self					):
           return (4, 1_4, 1_6)
      @property
      def 	a_    ( self					):
           return (4, 1_4, 1)
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           super().test_determinism()
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           super().test_outputs_equivalence()
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           super().test_from_save_pretrained()
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           super().test_from_save_pretrained_variant()
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           super().test_model_from_pretrained()
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           # UNetRL is a value-function is different output shape
           snake_case				,						snake_case					=       self.prepare_init_args_and_inputs_for_common()
           snake_case					=       self.model_class(**__snake_case					)
           model.to(__snake_case					)
           model.eval()
  
           with torch.no_grad():
                snake_case					=       model(**__snake_case					)
  
                if isinstance(__snake_case    ,      __snake_case					):
                     snake_case					=       output.sample
  
           self.assertIsNotNone(__snake_case					)
           snake_case					=       torch.Size((inputs_dict['''sample'''].shape[0], 1)					)
           self.assertEqual(output.shape    ,      __snake_case    ,      '''Input and output shapes do not match'''					)
      def 	a_    ( self					):
           pass
      def 	a_    ( self					):
           pass
      def 	a_    ( self					):
           snake_case					=       {
               '''in_channels''': 1_4,
               '''out_channels''': 1_4,
               '''down_block_types''': ['''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''],
               '''up_block_types''': [],
               '''out_block_type''': '''ValueFunction''',
               '''mid_block_type''': '''ValueFunctionMidBlock1D''',
               '''block_out_channels''': [3_2, 6_4, 1_2_8, 2_5_6],
               '''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''',
           }
           snake_case					=       self.dummy_input
           return init_dict, inputs_dict
      @unittest.skipIf(torch_device == '''mps'''    ,      '''mish op not supported in MPS'''					)
      def 	a_    ( self					):
           snake_case				,						snake_case					=       UNetaDModel.from_pretrained(
               '''bglick13/hopper-medium-v2-value-function-hor32'''    ,      output_loading_info=__snake_case    ,      subfolder='''value_function'''					)
           self.assertIsNotNone(__snake_case					)
           self.assertEqual(len(vf_loading_info['''missing_keys''']					)    ,      0					)
  
           value_function.to(__snake_case					)
           snake_case					=       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 	a_    ( self					):
           snake_case				,						snake_case					=       UNetaDModel.from_pretrained(
               '''bglick13/hopper-medium-v2-value-function-hor32'''    ,      output_loading_info=__snake_case    ,      subfolder='''value_function'''					)
           torch.manual_seed(0					)
           if torch.cuda.is_available():
                torch.cuda.manual_seed_all(0					)
  
           snake_case					=       value_function.config.in_channels
           snake_case					=       1_4
           snake_case					=       torch.randn((1, seq_len, num_features)					).permute(
               0    ,      2    ,      1					)  # match original, we can update values and remove
           snake_case					=       torch.full((num_features,)    ,      0					)
  
           with torch.no_grad():
                snake_case					=       value_function(__snake_case    ,      __snake_case					).sample
  
           # fmt: off
           snake_case					=       torch.tensor([165.25] * seq_len					)
           # fmt: on
           self.assertTrue(torch.allclose(__snake_case    ,      __snake_case    ,      rtol=1E-3					)					)
  
  
  
  
      def 	a_    ( self					):
           # Not implemented yet for this UNet
           pass
  
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  import random
  from collections.abc import Iterable
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case					):
           snake_case					=       {literal: None for literal in literals}
      def __str__( self					):
           return "{" + " , ".join(self.literals					) + "}"
      def __len__( self					):
           return len(self.literals					)
      def 	a_    ( self    ,      __snake_case					):
           for literal in self.literals:
                snake_case					=       literal[:2]
                if symbol in model:
                     snake_case					=       model[symbol]
                else:
                     continue
                if value is not None:
                     # Complement assignment if literal is in complemented form
                     if literal.endswith('''\''''					):
                          snake_case					=       not value
                snake_case					=       value
  
  
  
  
      def 	a_    ( self    ,      __snake_case					):
           for literal in self.literals:
                snake_case					=       literal.rstrip('''\''''					) if literal.endswith('''\''''					) else literal + '''\''''
                if symbol in self.literals:
                     return True
  
           self.assign(__snake_case					)
           for value in self.literals.values():
                if value in (True, None):
                     return value
           return any(self.literals.values()					)
  
  
  
  
  
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case					):
           snake_case					=       list(__snake_case					)
  
  
  
  
      def __str__( self					):
           return "{" + " , ".join(str(__snake_case					) for clause in self.clauses					) + "}"
  
  
  
  
  
  
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       random.randint(1							,5       )
       snake_case					=       '''A'''
       snake_case					=       0
       while i < no_of_literals:
            snake_case					=       random.randint(1							,5       )
            snake_case					=       base_var + str(UpperCamelCase_       )
            snake_case					=       random.randint(0							,1       )
            if var_complement == 1:
                 var_name += "'"
            if var_name in literals:
                 i -= 1
            else:
                 literals.append(UpperCamelCase_       )
            i += 1
       return Clause(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       set()
       snake_case					=       random.randint(1							,10       )
       while len(UpperCamelCase_       ) < no_of_clauses:
            clauses.add(generate_clause()       )
       return Formula(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       formula.clauses
       snake_case					=       []
       for clause in formula.clauses:
            for literal in clause.literals:
                 snake_case					=       literal[:2]
                 if symbol not in symbols_set:
                      symbols_set.append(UpperCamelCase_       )
       return clauses, symbols_set
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       {}
       snake_case					=       []
  
       for clause in clauses:
            if clause.evaluate(UpperCamelCase_       ):
                 continue
            for literal in clause.literals:
                 literals.append(UpperCamelCase_       )
  
       for s in symbols:
            snake_case					=       s + '''\''''
            if (s in literals and sym not in literals) or (
                s not in literals and sym in literals
            ):
                 pure_symbols.append(UpperCamelCase_       )
       for p in pure_symbols:
            snake_case					=       None
       for s in pure_symbols:
            snake_case					=       s + '''\''''
            if s in literals:
                 snake_case					=       True
            elif sym in literals:
                 snake_case					=       False
       return pure_symbols, assignment
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       for clause in clauses:
            if len(UpperCamelCase_       ) == 1:
                 unit_symbols.append(list(clause.literals.keys()       )[0]       )
            else:
                 snake_case				,						snake_case					=       0, 0
                 for literal, value in clause.literals.items():
                      if value is False:
                           f_count += 1
                      elif value is None:
                           snake_case					=       literal
                           n_count += 1
                 if f_count == len(UpperCamelCase_       ) - 1 and n_count == 1:
                      unit_symbols.append(UpperCamelCase_       )
       snake_case					=       {}
       for i in unit_symbols:
            snake_case					=       i[:2]
            snake_case					=       len(UpperCamelCase_       ) == 2
       snake_case					=       [i[:2] for i in unit_symbols]
  
       return unit_symbols, assignment
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       True
       for clause in clauses:
            snake_case					=       clause.evaluate(UpperCamelCase_       )
            if clause_check is False:
                 return False, None
            elif clause_check is None:
                 snake_case					=       False
                 continue
  
       if check_clause_all_true:
            return True, model
  
       try:
            snake_case				,						snake_case					=       find_pure_symbols(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
       except RecursionError:
            print('''raises a RecursionError and is'''       )
            return None, {}
       snake_case					=       None
       if len(UpperCamelCase_       ) > 0:
            snake_case				,						snake_case					=       pure_symbols[0], assignment[pure_symbols[0]]
  
       if p:
            snake_case					=       model
            snake_case					=       value
            snake_case					=       list(UpperCamelCase_       )
            if p in tmp_symbols:
                 tmp_symbols.remove(UpperCamelCase_       )
            return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
       snake_case				,						snake_case					=       find_unit_clauses(UpperCamelCase_							,UpperCamelCase_       )
       snake_case					=       None
       if len(UpperCamelCase_       ) > 0:
            snake_case				,						snake_case					=       unit_symbols[0], assignment[unit_symbols[0]]
       if p:
            snake_case					=       model
            snake_case					=       value
            snake_case					=       list(UpperCamelCase_       )
            if p in tmp_symbols:
                 tmp_symbols.remove(UpperCamelCase_       )
            return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
       snake_case					=       symbols[0]
       snake_case					=       symbols[1:]
       snake_case				,						snake_case					=       model, model
       snake_case				,						snake_case					=       True, False
  
       return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ) or dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
   _SCREAMING_SNAKE_CASE    :				Dict       =					generate_formula()
   print(f'''The formula {formula} is''', end=" ")
  
   _SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :				Any       =					generate_parameters(formula)
   _SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					dpll_algorithm(clauses, symbols, {})
  
   if solution:
    print(f'''satisfiable with the assignment {model}.''')
   else:
    print("not satisfiable.")
  
  
  
  
  