code: import tempfile
  import unittest
  
  import torch
  
  from diffusers import UNetaDConditionModel
  from diffusers.training_utils import EMAModel
  from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device
  
  
  enable_full_determinism()
  
  class 						A__      (					unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	'hf-internal-testing/tiny-stable-diffusion-pipe'
      __magic_name__    						=	1
      __magic_name__    						=	77
      __magic_name__    						=	32
      __magic_name__    						=	4
      __magic_name__    						=	__magic_name__    						=	64
      __magic_name__    						=	torch.manual_seed(0			)
      def 	a_    ( self    ,      __snake_case=0.9999					):
           snake_case					=       UNetaDConditionModel.from_pretrained(self.model_id    ,      subfolder='''unet'''					)
           snake_case					=       unet.to(__snake_case					)
           snake_case					=       EMAModel(unet.parameters()    ,      decay=__snake_case    ,      model_cls=__snake_case    ,      model_config=unet.config					)
           return unet, ema_unet
      def 	a_    ( self					):
           snake_case					=       torch.randn(
               self.batch_size    ,      self.num_in_channels    ,      self.latent_height    ,      self.latent_width    ,      generator=self.generator					).to(__snake_case					)
           snake_case					=       torch.randint(0    ,      1_0_0_0    ,      size=(self.batch_size,)    ,      generator=self.generator					).to(__snake_case					)
           snake_case					=       torch.randn(
               self.batch_size    ,      self.prompt_length    ,      self.text_encoder_hidden_dim    ,      generator=self.generator					).to(__snake_case					)
           return noisy_latents, timesteps, encoder_hidden_states
      def 	a_    ( self    ,      __snake_case					):
           snake_case					=       {}
           for k, param in unet.state_dict().items():
                snake_case					=       torch.randn_like(__snake_case					) + (param * torch.randn_like(__snake_case					))
                updated_state_dict.update({k: updated_param}					)
           unet.load_state_dict(__snake_case					)
           return unet
      def 	a_    ( self					):
           snake_case				,						snake_case					=       self.get_models()
           # Take the first (hypothetical) EMA step.
           ema_unet.step(unet.parameters()					)
           assert ema_unet.optimization_step == 1
  
           # Take two more.
           for _ in range(2					):
                ema_unet.step(unet.parameters()					)
           assert ema_unet.optimization_step == 3
      def 	a_    ( self					):
           snake_case				,						snake_case					=       self.get_models()
           # Since the `unet` is not being updated (i.e., backprop'd)
           # there won't be any difference between the `params` of `unet`
           # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
           ema_unet.step(unet.parameters()					)
           snake_case					=       list(unet.parameters()					)
           for s_param, param in zip(ema_unet.shadow_params    ,      __snake_case					):
                assert torch.allclose(__snake_case    ,      __snake_case					)
  
           # The above holds true even if we call `ema.step()` multiple times since
           # `unet` params are still not being updated.
           for _ in range(4					):
                ema_unet.step(unet.parameters()					)
           for s_param, param in zip(ema_unet.shadow_params    ,      __snake_case					):
                assert torch.allclose(__snake_case    ,      __snake_case					)
      def 	a_    ( self					):
           snake_case				,						snake_case					=       self.get_models()
           # Here we simulate the parameter updates for `unet`. Since there might
           # be some parameters which are initialized to zero we take extra care to
           # initialize their values to something non-zero before the multiplication.
           snake_case					=       self.simulate_backprop(__snake_case					)
  
           # Take the EMA step.
           ema_unet.step(unet_pseudo_updated_step_one.parameters()					)
  
           # Now the EMA'd parameters won't be equal to the original model parameters.
           snake_case					=       list(unet_pseudo_updated_step_one.parameters()					)
           for s_param, param in zip(ema_unet.shadow_params    ,      __snake_case					):
                assert ~torch.allclose(__snake_case    ,      __snake_case					)
  
           # Ensure this is the case when we take multiple EMA steps.
           for _ in range(4					):
                ema_unet.step(unet.parameters()					)
           for s_param, param in zip(ema_unet.shadow_params    ,      __snake_case					):
                assert ~torch.allclose(__snake_case    ,      __snake_case					)
      def 	a_    ( self					):
           # If we call EMA step after a backpropagation consecutively for two times,
           # the shadow params from those two steps should be different.
           snake_case				,						snake_case					=       self.get_models()
  
           # First backprop + EMA
           snake_case					=       self.simulate_backprop(__snake_case					)
           ema_unet.step(unet_step_one.parameters()					)
           snake_case					=       ema_unet.shadow_params
  
           # Second backprop + EMA
           snake_case					=       self.simulate_backprop(__snake_case					)
           ema_unet.step(unet_step_two.parameters()					)
           snake_case					=       ema_unet.shadow_params
  
           for step_one, step_two in zip(__snake_case    ,      __snake_case					):
                assert ~torch.allclose(__snake_case    ,      __snake_case					)
      def 	a_    ( self					):
           # If there's no decay even if there are backprops, EMA steps
           # won't take any effect i.e., the shadow params would remain the
           # same.
           snake_case				,						snake_case					=       self.get_models(decay=0.0					)
           snake_case					=       self.simulate_backprop(__snake_case					)
           ema_unet.step(unet_step_one.parameters()					)
           snake_case					=       ema_unet.shadow_params
  
           snake_case					=       self.simulate_backprop(__snake_case					)
           ema_unet.step(unet_step_two.parameters()					)
           snake_case					=       ema_unet.shadow_params
  
           for step_one, step_two in zip(__snake_case    ,      __snake_case					):
                assert torch.allclose(__snake_case    ,      __snake_case					)
  
  
  
  
      @skip_mps
      def 	a_    ( self					):
           snake_case				,						snake_case					=       self.get_models()
           snake_case				,						snake_case				,						snake_case					=       self.get_dummy_inputs()
  
           with tempfile.TemporaryDirectory() as tmpdir:
                ema_unet.save_pretrained(__snake_case					)
                snake_case					=       UNetaDConditionModel.from_pretrained(__snake_case    ,      model_cls=__snake_case					)
                snake_case					=       loaded_unet.to(unet.device					)
  
           # Since no EMA step has been performed the outputs should match.
           snake_case					=       unet(__snake_case    ,      __snake_case    ,      __snake_case					).sample
           snake_case					=       loaded_unet(__snake_case    ,      __snake_case    ,      __snake_case					).sample
  
           assert torch.allclose(__snake_case    ,      __snake_case    ,      atol=1E-4					)
  
  
  
  
  
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.")
  
  
  
  
  