


from __future__ import annotations

import random
from collections.abc import Iterable







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase)      ->    None:
            _A				:	dict[str, bool | None]       =			{literal: None for literal in literals}


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


     def __len__(						self)      ->    int:
            return len(self.literals)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    None:
            for literal in self.literals:
                   _A				:	Dict       =			literal[:2]
                   if symbol in model:
                          _A				:	str       =			model[symbol]
                   else:
                          continue
                   if value is not None:
                          # Complement assignment if literal is in complemented form
                          if literal.endswith("'"):
                                 _A				:	Optional[int]       =			not value
                   _A				:	Dict       =			value


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    bool | None:
            for literal in self.literals:
                   _A				:	Any       =			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 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase)      ->    None:
            _A				:	str       =			list(__lowerCamelCase)


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




def 							_UpperCAmelCase    ():
       _A				:	int       =			[]
       _A				:	List[Any]       =			random.randint(1    ,							5						)
       _A				:	Tuple       =			"A"
       _A				:	Tuple       =			0
       while i < no_of_literals:
              _A				:	Tuple       =			random.randint(1    ,							5						)
              _A				:	Tuple       =			base_var + str(UpperCamelCase__						)
              _A				:	Optional[Any]       =			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    ():
       _A				:	set[Clause]       =			set()
       _A				:	Optional[int]       =			random.randint(1    ,							10						)
       while len(UpperCamelCase__						) < no_of_clauses:
              clauses.add(generate_clause()						)
       return Formula(UpperCamelCase__						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  Formula						):
       _A				:	Any       =			formula.clauses
       _A				:	int       =			[]
       for clause in formula.clauses:
              for literal in clause.literals:
                     _A				:	Dict       =			literal[:2]
                     if symbol not in symbols_set:
                            symbols_set.append(UpperCamelCase__						)
       return clauses, symbols_set
def 							_UpperCAmelCase    (UpperCamelCase__					:  list[Clause]    ,							UpperCamelCase__					:  list[str]    ,							UpperCamelCase__					:  dict[str, bool | None]						):
       _A				:	int       =			[]
       _A				:	dict[str, bool | None]       =			{}
       _A				:	str       =			[]

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

       for s in symbols:
              _A				:	Dict       =			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:
              _A				:	str       =			None
       for s in pure_symbols:
              _A				:	str       =			s + "'"
              if s in literals:
                     _A				:	Any       =			True
              elif sym in literals:
                     _A				:	Dict       =			False
       return pure_symbols, assignment
def 							_UpperCAmelCase    (UpperCamelCase__					:  list[Clause]    ,							UpperCamelCase__					:  dict[str, bool | None]						):
       _A				:	List[str]       =			[]
       for clause in clauses:
              if len(UpperCamelCase__						) == 1:
                     unit_symbols.append(list(clause.literals.keys()						)[0]						)
              else:
                     _A   ,      _A				:	List[Any]       =			0, 0
                     for literal, value in clause.literals.items():
                            if value is False:
                                   f_count += 1
                            elif value is None:
                                   _A				:	List[str]       =			literal
                                   n_count += 1
                     if f_count == len(UpperCamelCase__						) - 1 and n_count == 1:
                            unit_symbols.append(UpperCamelCase__						)
       _A				:	dict[str, bool | None]       =			{}
       for i in unit_symbols:
              _A				:	Dict       =			i[:2]
              _A				:	Any       =			len(UpperCamelCase__						) == 2
       _A				:	str       =			[i[:2] for i in unit_symbols]

       return unit_symbols, assignment
def 							_UpperCAmelCase    (UpperCamelCase__					:  list[Clause]    ,							UpperCamelCase__					:  list[str]    ,							UpperCamelCase__					:  dict[str, bool | None]						):
       _A				:	str       =			True
       for clause in clauses:
              _A				:	Optional[int]       =			clause.evaluate(UpperCamelCase__						)
              if clause_check is False:
                     return False, None
              elif clause_check is None:
                     _A				:	str       =			False
                     continue

       if check_clause_all_true:
              return True, model

       try:
              _A   ,      _A				:	Union[str, Any]       =			find_pure_symbols(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)
       except RecursionError:
              print("raises a RecursionError and is"						)
              return None, {}
       _A				:	Optional[Any]       =			None
       if len(UpperCamelCase__						) > 0:
              _A   ,      _A				:	Optional[Any]       =			pure_symbols[0], assignment[pure_symbols[0]]

       if p:
              _A				:	Optional[Any]       =			model
              _A				:	int       =			value
              _A				:	str       =			list(UpperCamelCase__						)
              if p in tmp_symbols:
                     tmp_symbols.remove(UpperCamelCase__						)
              return dpll_algorithm(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)

       _A   ,      _A				:	Dict       =			find_unit_clauses(UpperCamelCase__    ,							UpperCamelCase__						)
       _A				:	str       =			None
       if len(UpperCamelCase__						) > 0:
              _A   ,      _A				:	Any       =			unit_symbols[0], assignment[unit_symbols[0]]
       if p:
              _A				:	str       =			model
              _A				:	Any       =			value
              _A				:	List[str]       =			list(UpperCamelCase__						)
              if p in tmp_symbols:
                     tmp_symbols.remove(UpperCamelCase__						)
              return dpll_algorithm(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)
       _A				:	Any       =			symbols[0]
       _A				:	List[Any]       =			symbols[1:]
       _A   ,      _A				:	Union[str, Any]       =			model, model
       _A   ,      _A				:	Any       =			True, False

       return dpll_algorithm(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						) or dpll_algorithm(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase__						=			generate_formula()
    print(f"The formula {formula} is", end=' ')

    lowerCAmelCase__		,lowerCAmelCase__						=			generate_parameters(formula)
    lowerCAmelCase__		,lowerCAmelCase__						=			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


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import SwinaSRImageProcessor
    from transformers.image_transforms import get_image_size







class 					lowerCAmelCase__	(    unittest.TestCase):




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=7			,						__lowerCamelCase=3			,						__lowerCamelCase=1_8			,						__lowerCamelCase=3_0			,						__lowerCamelCase=4_0_0			,						__lowerCamelCase=True			,						__lowerCamelCase=1 / 2_5_5			,						__lowerCamelCase=True			,						__lowerCamelCase=8			,						)      ->    Optional[Any]:
            _A				:	List[str]       =			parent
            _A				:	str       =			batch_size
            _A				:	Any       =			num_channels
            _A				:	Any       =			image_size
            _A				:	Optional[int]       =			min_resolution
            _A				:	Union[str, Any]       =			max_resolution
            _A				:	Optional[Any]       =			do_rescale
            _A				:	str       =			rescale_factor
            _A				:	str       =			do_pad
            _A				:	Dict       =			pad_size


     def 			_lowerCamelCase			(						self)      ->    str:
            return {
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_pad": self.do_pad,
                "pad_size": self.pad_size,
            }


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase=False			,						__lowerCamelCase=False			,						__lowerCamelCase=False)      ->    str:
            assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

            if equal_resolution:
                   _A				:	List[str]       =			[]
                   for i in range(self.batch_size):
                          image_inputs.append(
                              np.random.randint(
                                  2_5_5			,						size=(self.num_channels, self.max_resolution, self.max_resolution)			,						dtype=np.uinta))
            else:
                   _A				:	List[str]       =			[]
                   for i in range(self.batch_size):
                          _A   ,      _A				:	int       =			np.random.choice(np.arange(self.min_resolution			,						self.max_resolution)			,						2)
                          image_inputs.append(np.random.randint(2_5_5			,						size=(self.num_channels, width, height)			,						dtype=np.uinta))

            if not numpify and not torchify:
                   # PIL expects the channel dimension as last dimension
                   _A				:	Optional[Any]       =			[Image.fromarray(np.moveaxis(__lowerCamelCase			,						0			,						-1)) for x in image_inputs]

            if torchify:
                   _A				:	Union[str, Any]       =			[torch.from_numpy(__lowerCamelCase) for x in image_inputs]

            return image_inputs







@require_torch
@require_vision
class 					lowerCAmelCase__	(    a					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			SwinaSRImageProcessor if is_vision_available() else None


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	int       =			SwinaSRImageProcessingTester(self)


     @property
     def 			_lowerCamelCase			(						self)      ->    Any:
            return self.image_processor_tester.prepare_image_processor_dict()


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            _A				:	Tuple       =			self.image_processing_class(**self.image_processor_dict)
            self.assertTrue(hasattr(__lowerCamelCase			,						"do_rescale"))
            self.assertTrue(hasattr(__lowerCamelCase			,						"rescale_factor"))
            self.assertTrue(hasattr(__lowerCamelCase			,						"do_pad"))
            self.assertTrue(hasattr(__lowerCamelCase			,						"pad_size"))


     def 			_lowerCamelCase			(						self)      ->    Dict:
            pass


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Tuple:
            _A   ,      _A				:	Dict       =			get_image_size(__lowerCamelCase)
            _A				:	Tuple       =			self.image_processor_tester.pad_size

            _A				:	Dict       =			(old_height // size + 1) * size - old_height
            _A				:	Any       =			(old_width // size + 1) * size - old_width
            return old_height + pad_height, old_width + pad_width


     def 			_lowerCamelCase			(						self)      ->    Dict:
            # Initialize image_processor
            _A				:	Optional[int]       =			self.image_processing_class(**self.image_processor_dict)
            # create random PIL images
            _A				:	Union[str, Any]       =			self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase			,						Image.Image)

            # Test not batched input
            _A				:	str       =			image_processor(image_inputs[0]			,						return_tensors="pt").pixel_values
            _A   ,      _A				:	int       =			self.calculate_expected_size(np.array(image_inputs[0]))
            self.assertEqual(
                encoded_images.shape			,						(
                    1,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )			,						)


     def 			_lowerCamelCase			(						self)      ->    int:
            # Initialize image_processor
            _A				:	Any       =			self.image_processing_class(**self.image_processor_dict)
            # create random numpy tensors
            _A				:	int       =			self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,						numpify=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase			,						np.ndarray)

            # Test not batched input
            _A				:	Union[str, Any]       =			image_processor(image_inputs[0]			,						return_tensors="pt").pixel_values
            _A   ,      _A				:	Union[str, Any]       =			self.calculate_expected_size(image_inputs[0])
            self.assertEqual(
                encoded_images.shape			,						(
                    1,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )			,						)


     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            # Initialize image_processor
            _A				:	str       =			self.image_processing_class(**self.image_processor_dict)
            # create random PyTorch tensors
            _A				:	Tuple       =			self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,						torchify=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase			,						torch.Tensor)

            # Test not batched input
            _A				:	List[str]       =			image_processor(image_inputs[0]			,						return_tensors="pt").pixel_values
            _A   ,      _A				:	List[str]       =			self.calculate_expected_size(image_inputs[0])
            self.assertEqual(
                encoded_images.shape			,						(
                    1,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )			,						)
