


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








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.")




