



from __future__ import annotations

import random
from collections.abc import Iterable



class     __snake_case						:




							def __init__(	self					:	Tuple    ,    A_					:	list[str]):
									lowerCAmelCase_      :					dict[str, bool | None]     =			{literal: None for literal in literals}




							def __str__(	self					:	Optional[int]):
									return "{" + " , ".join(self.literals) + "}"




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




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	dict[str, bool | None]):
									for literal in self.literals:
											lowerCAmelCase_      :					str     =			literal[:2]
											if symbol in model:
													lowerCAmelCase_      :					Union[str, Any]     =			model[symbol]
											else:
													continue
											if value is not None:
													# Complement assignment if literal is in complemented form
													if literal.endswith('''\''''):
															lowerCAmelCase_      :					Optional[int]     =			not value
											lowerCAmelCase_      :					List[Any]     =			value







							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	dict[str, bool | None]):
									for literal in self.literals:
											lowerCAmelCase_      :					Optional[int]     =			literal.rstrip('''\'''') if literal.endswith('''\'''') else literal + '''\''''
											if symbol in self.literals:
													return True

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





class     __snake_case						:




							def __init__(	self					:	Dict    ,    A_					:	Iterable[Clause]):
									lowerCAmelCase_      :					List[str]     =			list(A_)







							def __str__(	self					:	List[str]):
									return "{" + " , ".join(str(A_) for clause in self.clauses) + "}"





def       UpperCamelCase(  ):
		lowerCAmelCase_      :					str     =			[]
		lowerCAmelCase_      :					List[Any]     =			random.randint(1					,5			)
		lowerCAmelCase_      :					int     =			'''A'''
		lowerCAmelCase_      :					Union[str, Any]     =			0
		while i < no_of_literals:
				lowerCAmelCase_      :					Dict     =			random.randint(1					,5			)
				lowerCAmelCase_      :					Any     =			base_var + str(__UpperCamelCase			)
				lowerCAmelCase_      :					List[str]     =			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(  ):
		lowerCAmelCase_      :					set[Clause]     =			set()
		lowerCAmelCase_      :					List[str]     =			random.randint(1					,10			)
		while len(__UpperCamelCase			) < no_of_clauses:
				clauses.add(generate_clause()			)
		return Formula(__UpperCamelCase			)


def       UpperCamelCase(  __UpperCamelCase							:		Formula			):
		lowerCAmelCase_      :					Dict     =			formula.clauses
		lowerCAmelCase_      :					Union[str, Any]     =			[]
		for clause in formula.clauses:
				for literal in clause.literals:
						lowerCAmelCase_      :					Optional[int]     =			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]			):
		lowerCAmelCase_      :					List[Any]     =			[]
		lowerCAmelCase_      :					dict[str, bool | None]     =			{}
		lowerCAmelCase_      :					List[str]     =			[]

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

		for s in symbols:
				lowerCAmelCase_      :					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:
				lowerCAmelCase_      :					List[Any]     =			None
		for s in pure_symbols:
				lowerCAmelCase_      :					Optional[Any]     =			s + '''\''''
				if s in literals:
						lowerCAmelCase_      :					Union[str, Any]     =			True
				elif sym in literals:
						lowerCAmelCase_      :					Optional[Any]     =			False
		return pure_symbols, assignment


def       UpperCamelCase(  __UpperCamelCase							:		list[Clause]					,__UpperCamelCase							:		dict[str, bool | None]			):
		lowerCAmelCase_      :					List[Any]     =			[]
		for clause in clauses:
				if len(__UpperCamelCase			) == 1:
						unit_symbols.append(list(clause.literals.keys()			)[0]			)
				else:
						lowerCAmelCase_      :					Optional[Any]     =			0, 0
						for literal, value in clause.literals.items():
								if value is False:
										f_count += 1
								elif value is None:
										lowerCAmelCase_      :					Optional[int]     =			literal
										n_count += 1
						if f_count == len(__UpperCamelCase			) - 1 and n_count == 1:
								unit_symbols.append(__UpperCamelCase			)
		lowerCAmelCase_      :					dict[str, bool | None]     =			{}
		for i in unit_symbols:
				lowerCAmelCase_      :					str     =			i[:2]
				lowerCAmelCase_      :					Tuple     =			len(__UpperCamelCase			) == 2
		lowerCAmelCase_      :					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]			):
		lowerCAmelCase_      :					Tuple     =			True
		for clause in clauses:
				lowerCAmelCase_      :					Optional[int]     =			clause.evaluate(__UpperCamelCase			)
				if clause_check is False:
						return False, None
				elif clause_check is None:
						lowerCAmelCase_      :					str     =			False
						continue

		if check_clause_all_true:
				return True, model

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

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

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

		return dpll_algorithm(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase			) or dpll_algorithm(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						A__  :  Any       = generate_formula()
						print(F'''The formula {formula} is''', end=''' ''')

						A__  :  List[str]       = generate_parameters(formula)
						A__  :  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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VideoMAEImageProcessor



class     __snake_case						(					unittest.TestCase						):




							def __init__(	self					:	str    ,    A_					:	str    ,    A_					:	int=7    ,    A_					:	List[Any]=3    ,    A_					:	Dict=1_0    ,    A_					:	List[str]=1_8    ,    A_					:	List[Any]=3_0    ,    A_					:	str=4_0_0    ,    A_					:	str=True    ,    A_					:	Tuple=None    ,    A_					:	Optional[Any]=True    ,    A_					:	Tuple=[0.5, 0.5, 0.5]    ,    A_					:	Any=[0.5, 0.5, 0.5]    ,    A_					:	List[str]=None    ,    ):
									lowerCAmelCase_      :					Optional[Any]     =			size if size is not None else {'''shortest_edge''': 1_8}
									lowerCAmelCase_      :					Optional[int]     =			crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

									lowerCAmelCase_      :					Any     =			parent
									lowerCAmelCase_      :					Optional[Any]     =			batch_size
									lowerCAmelCase_      :					List[Any]     =			num_channels
									lowerCAmelCase_      :					Optional[int]     =			num_frames
									lowerCAmelCase_      :					Optional[int]     =			image_size
									lowerCAmelCase_      :					List[Any]     =			min_resolution
									lowerCAmelCase_      :					str     =			max_resolution
									lowerCAmelCase_      :					List[str]     =			do_resize
									lowerCAmelCase_      :					int     =			size
									lowerCAmelCase_      :					Union[str, Any]     =			do_normalize
									lowerCAmelCase_      :					Union[str, Any]     =			image_mean
									lowerCAmelCase_      :					List[str]     =			image_std
									lowerCAmelCase_      :					Any     =			crop_size







							def        UpperCAmelCase__      (	self					:	Any):
									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_size": self.crop_size,
									}





@require_torch
@require_vision
class     __snake_case						(					UpperCamelCase_						,unittest.TestCase						):
							_a      				=			VideoMAEImageProcessor if is_vision_available() else None




							def        UpperCAmelCase__      (	self					:	Tuple):
									lowerCAmelCase_      :					int     =			VideoMAEImageProcessingTester(self)




							@property
							def        UpperCAmelCase__      (	self					:	Tuple):
									return self.image_processor_tester.prepare_image_processor_dict()




							def        UpperCAmelCase__      (	self					:	Optional[int]):
									lowerCAmelCase_      :					int     =			self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(A_    ,    '''image_mean'''))
									self.assertTrue(hasattr(A_    ,    '''image_std'''))
									self.assertTrue(hasattr(A_    ,    '''do_normalize'''))
									self.assertTrue(hasattr(A_    ,    '''do_resize'''))
									self.assertTrue(hasattr(A_    ,    '''do_center_crop'''))
									self.assertTrue(hasattr(A_    ,    '''size'''))




							def        UpperCAmelCase__      (	self					:	Dict):
									lowerCAmelCase_      :					int     =			self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size    ,    {'''shortest_edge''': 1_8})
									self.assertEqual(image_processor.crop_size    ,    {'''height''': 1_8, '''width''': 1_8})

									lowerCAmelCase_      :					str     =			self.image_processing_class.from_dict(self.image_processor_dict    ,    size=4_2    ,    crop_size=8_4)
									self.assertEqual(image_processor.size    ,    {'''shortest_edge''': 4_2})
									self.assertEqual(image_processor.crop_size    ,    {'''height''': 8_4, '''width''': 8_4})




							def        UpperCAmelCase__      (	self					:	Dict):
									pass




							def        UpperCAmelCase__      (	self					:	str):
									# Initialize image_processing
									lowerCAmelCase_      :					int     =			self.image_processing_class(**self.image_processor_dict)
									# create random PIL videos
									lowerCAmelCase_      :					Optional[int]     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    Image.Image)

									# Test not batched input
									lowerCAmelCase_      :					int     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					Tuple     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )




							def        UpperCAmelCase__      (	self					:	int):
									# Initialize image_processing
									lowerCAmelCase_      :					Dict     =			self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									lowerCAmelCase_      :					str     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_    ,    numpify=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    np.ndarray)

									# Test not batched input
									lowerCAmelCase_      :					Tuple     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					Union[str, Any]     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )







							def        UpperCAmelCase__      (	self					:	List[str]):
									# Initialize image_processing
									lowerCAmelCase_      :					Tuple     =			self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									lowerCAmelCase_      :					Tuple     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_    ,    torchify=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    torch.Tensor)

									# Test not batched input
									lowerCAmelCase_      :					List[str]     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					List[Any]     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )





