






"""simple docstring"""



from __future__ import annotations

import random
from collections.abc import Iterable

class 			a	:







    def __init__(     self				,						_lowerCamelCase			):
      lowercase				       =			{literal: None for literal in literals}







    def __str__(     self			):
      return "{" + " , ".join(self.literals			) + "}"







    def __len__(     self			):
      return len(self.literals			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      for literal in self.literals:
        lowercase				       =			literal[:2]
        if symbol in model:
          lowercase				       =			model[symbol]
        else:
          continue
        if value is not None:
          # Complement assignment if literal is in complemented form
          if literal.endswith('\''			):
            lowercase				       =			not value
        lowercase				       =			value







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      for literal in self.literals:
        lowercase				       =			literal.rstrip('\''			) if literal.endswith('\''			) else literal + '\''
        if symbol in self.literals:
          return True

      self.assign(_lowerCamelCase			)
      for value in self.literals.values():
        if value in (True, None):
          return value
      return any(self.literals.values()			)


class 			a	:







    def __init__(     self				,						_lowerCamelCase			):
      lowercase				       =			list(_lowerCamelCase			)







    def __str__(     self			):
      return "{" + " , ".join(str(_lowerCamelCase			) for clause in self.clauses			) + "}"


def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			[]
  lowercase				       =			random.randint(1						,   5       )
  lowercase				       =			'A'
  lowercase				       =			0
  while i < no_of_literals:
    lowercase				       =			random.randint(1						,   5       )
    lowercase				       =			base_var + str(__snake_case       )
    lowercase				       =			random.randint(0						,   1       )
    if var_complement == 1:
      var_name += "'"
    if var_name in literals:
      i -= 1
    else:
      literals.append(__snake_case       )
    i += 1
  return Clause(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			set()
  lowercase				       =			random.randint(1						,   10       )
  while len(__snake_case       ) < no_of_clauses:
    clauses.add(generate_clause()       )
  return Formula(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Formula       ):





  '''simple docstring'''





  lowercase				       =			formula.clauses
  lowercase				       =			[]
  for clause in formula.clauses:
    for literal in clause.literals:
      lowercase				       =			literal[:2]
      if symbol not in symbols_set:
        symbols_set.append(__snake_case       )
  return clauses, symbols_set
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    list[Clause]						,   __snake_case     :    list[str]						,   __snake_case     :    dict[str, bool | None]       ):





  '''simple docstring'''





  lowercase				       =			[]
  lowercase				       =			{}
  lowercase				       =			[]

  for clause in clauses:
    if clause.evaluate(__snake_case       ):
      continue
    for literal in clause.literals:
      literals.append(__snake_case       )

  for s in symbols:
    lowercase				       =			s + '\''
    if (s in literals and sym not in literals) or (
        s not in literals and sym in literals
    ):
      pure_symbols.append(__snake_case       )
  for p in pure_symbols:
    lowercase				       =			None
  for s in pure_symbols:
    lowercase				       =			s + '\''
    if s in literals:
      lowercase				       =			True
    elif sym in literals:
      lowercase				       =			False
  return pure_symbols, assignment
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    list[Clause]						,   __snake_case     :    dict[str, bool | None]       ):





  '''simple docstring'''





  lowercase				       =			[]
  for clause in clauses:
    if len(__snake_case       ) == 1:
      unit_symbols.append(list(clause.literals.keys()       )[0]       )
    else:
      lowercase       ,	lowercase				       =			0, 0
      for literal, value in clause.literals.items():
        if value is False:
          f_count += 1
        elif value is None:
          lowercase				       =			literal
          n_count += 1
      if f_count == len(__snake_case       ) - 1 and n_count == 1:
        unit_symbols.append(__snake_case       )
  lowercase				       =			{}
  for i in unit_symbols:
    lowercase				       =			i[:2]
    lowercase				       =			len(__snake_case       ) == 2
  lowercase				       =			[i[:2] for i in unit_symbols]

  return unit_symbols, assignment
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    list[Clause]						,   __snake_case     :    list[str]						,   __snake_case     :    dict[str, bool | None]       ):





  '''simple docstring'''





  lowercase				       =			True
  for clause in clauses:
    lowercase				       =			clause.evaluate(__snake_case       )
    if clause_check is False:
      return False, None
    elif clause_check is None:
      lowercase				       =			False
      continue

  if check_clause_all_true:
    return True, model

  try:
    lowercase       ,	lowercase				       =			find_pure_symbols(__snake_case						,   __snake_case						,   __snake_case       )
  except RecursionError:
    print('raises a RecursionError and is'       )
    return None, {}
  lowercase				       =			None
  if len(__snake_case       ) > 0:
    lowercase       ,	lowercase				       =			pure_symbols[0], assignment[pure_symbols[0]]

  if p:
    lowercase				       =			model
    lowercase				       =			value
    lowercase				       =			list(__snake_case       )
    if p in tmp_symbols:
      tmp_symbols.remove(__snake_case       )
    return dpll_algorithm(__snake_case						,   __snake_case						,   __snake_case       )

  lowercase       ,	lowercase				       =			find_unit_clauses(__snake_case						,   __snake_case       )
  lowercase				       =			None
  if len(__snake_case       ) > 0:
    lowercase       ,	lowercase				       =			unit_symbols[0], assignment[unit_symbols[0]]
  if p:
    lowercase				       =			model
    lowercase				       =			value
    lowercase				       =			list(__snake_case       )
    if p in tmp_symbols:
      tmp_symbols.remove(__snake_case       )
    return dpll_algorithm(__snake_case						,   __snake_case						,   __snake_case       )
  lowercase				       =			symbols[0]
  lowercase				       =			symbols[1:]
  lowercase       ,	lowercase				       =			model, model
  lowercase       ,	lowercase				       =			True, False

  return dpll_algorithm(__snake_case						,   __snake_case						,   __snake_case       ) or dpll_algorithm(__snake_case						,   __snake_case						,   __snake_case       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _UpperCamelCase						:    Tuple		=						generate_formula()
    print(F'''The formula {formula} is''', end=' ')

    _UpperCamelCase,						_UpperCamelCase						:    Dict		=						generate_parameters(formula)
    _UpperCamelCase,						_UpperCamelCase						:    List[Any]		=						dpll_algorithm(clauses, symbols, {})

    if solution:
        print(F'''satisfiable with the assignment {model}.''')
    else:
        print('not satisfiable.')







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import EfficientNetImageProcessor





class    __lowerCAmelCase					(	unittest.TestCase			):




				def __init__(				self		,							_snake_case		,							_snake_case=13		,							_snake_case=3		,							_snake_case=18		,							_snake_case=30		,							_snake_case=400		,							_snake_case=True		,							_snake_case=None		,							_snake_case=True		,							_snake_case=[0.5, 0.5, 0.5]		,							_snake_case=[0.5, 0.5, 0.5]		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       size if size is not None else {"""height""": 18, """width""": 18}
									_lowerCAmelCase         =       parent
									_lowerCAmelCase         =       batch_size
									_lowerCAmelCase         =       num_channels
									_lowerCAmelCase         =       image_size
									_lowerCAmelCase         =       min_resolution
									_lowerCAmelCase         =       max_resolution
									_lowerCAmelCase         =       do_resize
									_lowerCAmelCase         =       size
									_lowerCAmelCase         =       do_normalize
									_lowerCAmelCase         =       image_mean
									_lowerCAmelCase         =       image_std




				def 				snake_case  (				self						):



									"""simple docstring"""





									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}





@require_torch
@require_vision
class    __lowerCAmelCase					(	lowerCamelCase__			,		unittest.TestCase			):
				__lowerCamelCase					=							EfficientNetImageProcessor if is_vision_available() else None




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       EfficientNetImageProcessorTester(self						)




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_snake_case		,							"""image_mean"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""image_std"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_normalize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_resize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""size"""						)						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size		,							{"""height""": 18, """width""": 18}						)

									_lowerCAmelCase         =       self.image_processing_class.from_dict(self.image_processor_dict		,							size=42						)
									self.assertEqual(image_processor.size		,							{"""height""": 42, """width""": 42}						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_lowerCAmelCase         =       prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_snake_case						)
									for image in image_inputs:
														self.assertIsInstance(_snake_case		,							Image.Image						)

									# Test not batched input
									_lowerCAmelCase         =       image_processing(image_inputs[0]		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       image_processing(_snake_case		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_lowerCAmelCase         =       prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_snake_case		,							numpify=_snake_case						)
									for image in image_inputs:
														self.assertIsInstance(_snake_case		,							np.ndarray						)

									# Test not batched input
									_lowerCAmelCase         =       image_processing(image_inputs[0]		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       image_processing(_snake_case		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_lowerCAmelCase         =       prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_snake_case		,							torchify=_snake_case						)
									for image in image_inputs:
														self.assertIsInstance(_snake_case		,							torch.Tensor						)

									# Test not batched input
									_lowerCAmelCase         =       image_processing(image_inputs[0]		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       image_processing(_snake_case		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)



