



"""simple docstring"""







from __future__ import annotations

import random
from collections.abc import Iterable





class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_   ):
			_lowercase				: dict[str, bool | None]     =   {literal: None for literal in literals}






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






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






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			for literal in self.literals:
					_lowercase				: int     =   literal[:2]
					if symbol in model:
							_lowercase				: List[Any]     =   model[symbol]
					else:
							continue
					if value is not None:
							# Complement assignment if literal is in complemented form
							if literal.endswith("""'"""   ):
									_lowercase				: int     =   not value
					_lowercase				: Any     =   value






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			for literal in self.literals:
					_lowercase				: int     =   literal.rstrip("""'"""   ) if literal.endswith("""'"""   ) else literal + """'"""
					if symbol in self.literals:
							return True

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







class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_   ):
			_lowercase				: Optional[int]     =   list(UpperCAmelCase_   )






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







def  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: Optional[Any]     =   []
		_lowercase				: Optional[Any]     =   random.randint(1   ,				5      )
		_lowercase				: str     =   """A"""
		_lowercase				: Optional[int]     =   0
		while i < no_of_literals:
				_lowercase				: Any     =   random.randint(1   ,				5      )
				_lowercase				: List[str]     =   base_var + str(__UpperCAmelCase      )
				_lowercase				: 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  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: set[Clause]     =   set()
		_lowercase				: str     =   random.randint(1   ,				10      )
		while len(__UpperCAmelCase      ) < no_of_clauses:
				clauses.add(generate_clause()      )
		return Formula(__UpperCAmelCase      )


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: Any     =   formula.clauses
		_lowercase				: List[str]     =   []
		for clause in formula.clauses:
				for literal in clause.literals:
						_lowercase				: Tuple     =   literal[:2]
						if symbol not in symbols_set:
								symbols_set.append(__UpperCAmelCase      )
		return clauses, symbols_set


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: Optional[Any]     =   []
		_lowercase				: dict[str, bool | None]     =   {}
		_lowercase				: Optional[Any]     =   []

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

		for s in symbols:
				_lowercase				: Any     =   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:
				_lowercase				: List[str]     =   None
		for s in pure_symbols:
				_lowercase				: str     =   s + """'"""
				if s in literals:
						_lowercase				: int     =   True
				elif sym in literals:
						_lowercase				: Dict     =   False
		return pure_symbols, assignment


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: Optional[Any]     =   []
		for clause in clauses:
				if len(__UpperCAmelCase      ) == 1:
						unit_symbols.append(list(clause.literals.keys()      )[0]      )
				else:
						_lowercase     ,  _lowercase				: Optional[Any]     =   0, 0
						for literal, value in clause.literals.items():
								if value is False:
										f_count += 1
								elif value is None:
										_lowercase				: List[Any]     =   literal
										n_count += 1
						if f_count == len(__UpperCAmelCase      ) - 1 and n_count == 1:
								unit_symbols.append(__UpperCAmelCase      )
		_lowercase				: dict[str, bool | None]     =   {}
		for i in unit_symbols:
				_lowercase				: Optional[Any]     =   i[:2]
				_lowercase				: Tuple     =   len(__UpperCAmelCase      ) == 2
		_lowercase				: Optional[Any]     =   [i[:2] for i in unit_symbols]

		return unit_symbols, assignment


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: Dict     =   True
		for clause in clauses:
				_lowercase				: List[Any]     =   clause.evaluate(__UpperCAmelCase      )
				if clause_check is False:
						return False, None
				elif clause_check is None:
						_lowercase				: List[Any]     =   False
						continue

		if check_clause_all_true:
				return True, model

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

		if p:
				_lowercase				: str     =   model
				_lowercase				: Union[str, Any]     =   value
				_lowercase				: List[str]     =   list(__UpperCAmelCase      )
				if p in tmp_symbols:
						tmp_symbols.remove(__UpperCAmelCase      )
				return dpll_algorithm(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )

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

		return dpll_algorithm(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ) or dpll_algorithm(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		UpperCAmelCase:  Union[str, Any]			     =				generate_formula()
		print(F'The formula {formula} is', end=""" """)

		UpperCAmelCase     , UpperCAmelCase:  Optional[Any]			     =				generate_parameters(formula)
		UpperCAmelCase     , UpperCAmelCase:  Union[str, 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 ConvNextImageProcessor





class A__     (			unittest.TestCase			):



 """simple docstring"""







 def __init__(		self			:						Optional[Any]  ,    A_			:						Union[str, Any]  ,    A_			:						List[Any]=7  ,    A_			:						Optional[Any]=3  ,    A_			:						Optional[int]=1_8  ,    A_			:						Dict=3_0  ,    A_			:						Dict=4_0_0  ,    A_			:						List[Any]=True  ,    A_			:						int=None  ,    A_			:						List[Any]=0.875  ,    A_			:						List[str]=True  ,    A_			:						List[Any]=[0.5, 0.5, 0.5]  ,    A_			:						Optional[Any]=[0.5, 0.5, 0.5]  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       List[str]	   =     size if size is not None else {"shortest_edge": 2_0}
      _lowerCAmelCase :       Union[str, Any]	   =     parent
      _lowerCAmelCase :       int	   =     batch_size
      _lowerCAmelCase :       Any	   =     num_channels
      _lowerCAmelCase :       str	   =     image_size
      _lowerCAmelCase :       List[Any]	   =     min_resolution
      _lowerCAmelCase :       Tuple	   =     max_resolution
      _lowerCAmelCase :       Any	   =     do_resize
      _lowerCAmelCase :       List[str]	   =     size
      _lowerCAmelCase :       List[str]	   =     crop_pct
      _lowerCAmelCase :       Tuple	   =     do_normalize
      _lowerCAmelCase :       int	   =     image_mean
      _lowerCAmelCase :       Dict	   =     image_std


 def  __magic_name__							(		self			:						Dict							):






      '''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,
          "crop_pct": self.crop_pct,
      }





@require_torch
@require_vision
class A__     (			A	,		unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       int  			=    ConvNextImageProcessor if is_vision_available() else None

 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     ConvNextImageProcessingTester(self							)

 @property
 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()

 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     self.image_processing_class(**self.image_processor_dict							)
      self.assertTrue(hasattr(A_  ,    "do_resize"							)							)
      self.assertTrue(hasattr(A_  ,    "size"							)							)
      self.assertTrue(hasattr(A_  ,    "crop_pct"							)							)
      self.assertTrue(hasattr(A_  ,    "do_normalize"							)							)
      self.assertTrue(hasattr(A_  ,    "image_mean"							)							)
      self.assertTrue(hasattr(A_  ,    "image_std"							)							)

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     self.image_processing_class.from_dict(self.image_processor_dict							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 2_0}							)

      _lowerCAmelCase :       Optional[int]	   =     self.image_processing_class.from_dict(self.image_processor_dict  ,    size=4_2							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 4_2}							)

 def  __magic_name__							(		self			:						List[str]							):






      '''simple docstring'''




      pass

 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PIL images
      _lowerCAmelCase :       Tuple	   =     prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    Image.Image							)

      # Test not batched input
      _lowerCAmelCase :       List[Any]	   =     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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Any	   =     image_processing(A_  ,    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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




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

      # Test not batched input
      _lowerCAmelCase :       str	   =     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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Optional[Any]	   =     image_processing(A_  ,    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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )


 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PyTorch tensors
      _lowerCAmelCase :       List[str]	   =     prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=A_  ,    torchify=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    torch.Tensor							)

      # Test not batched input
      _lowerCAmelCase :       Any	   =     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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Union[str, Any]	   =     image_processing(A_  ,    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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )






