



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    ["""image_processor""", """tokenizer"""]
				lowercase__     =    """LayoutLMv2ImageProcessor"""
				lowercase__     =    ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")

				def __init__(			self  ,			snake_case__=None  ,			snake_case__=None  ,			**snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= None
									if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."  ,			snake_case__  ,			)
														UpperCamelCase_			= kwargs.pop("feature_extractor"     )

									UpperCamelCase_			= image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."     )
									if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."     )

									super().__init__(snake_case__  ,			snake_case__     )

				def __call__(			self  ,			snake_case__  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = True  ,			snake_case__ = False  ,			snake_case__ = False  ,			snake_case__ = None  ,			snake_case__ = 0  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = False  ,			snake_case__ = False  ,			snake_case__ = False  ,			snake_case__ = False  ,			snake_case__ = True  ,			snake_case__ = None  ,			**snake_case__  ,			):

									'''simple docstring'''


									if self.image_processor.apply_ocr and (boxes is not None):
														raise ValueError(
														    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."     )

									if self.image_processor.apply_ocr and (word_labels is not None):
														raise ValueError(
														    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."     )

									if return_overflowing_tokens is True and return_offsets_mapping is False:
														raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."     )

									# first, apply the image processor
									UpperCamelCase_			= self.image_processor(images=snake_case__  ,			return_tensors=snake_case__     )

									# second, apply the tokenizer
									if text is not None and self.image_processor.apply_ocr and text_pair is None:
														if isinstance(snake_case__  ,			snake_case__     ):
																			UpperCamelCase_			= [text]  # add batch dimension (as the image processor always adds a batch dimension)
														UpperCamelCase_			= features["words"]

									UpperCamelCase_			= self.tokenizer(
									    text=text if text is not None else features["words"]  ,			text_pair=text_pair if text_pair is not None else None  ,			boxes=boxes if boxes is not None else features["boxes"]  ,			word_labels=snake_case__  ,			add_special_tokens=snake_case__  ,			padding=snake_case__  ,			truncation=snake_case__  ,			max_length=snake_case__  ,			stride=snake_case__  ,			pad_to_multiple_of=snake_case__  ,			return_token_type_ids=snake_case__  ,			return_attention_mask=snake_case__  ,			return_overflowing_tokens=snake_case__  ,			return_special_tokens_mask=snake_case__  ,			return_offsets_mapping=snake_case__  ,			return_length=snake_case__  ,			verbose=snake_case__  ,			return_tensors=snake_case__  ,			**snake_case__  ,			)

									# add pixel values
									UpperCamelCase_			= features.pop("pixel_values"     )
									if return_overflowing_tokens is True:
														UpperCamelCase_			= self.get_overflowing_images(snake_case__  ,			encoded_inputs["overflow_to_sample_mapping"]     )
									UpperCamelCase_			= images

									return encoded_inputs

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= []
									for sample_idx in overflow_to_sample_mapping:
														images_with_overflow.append(images[sample_idx]     )

									if len(snake_case__     ) != len(snake_case__     ):
														raise ValueError(
														    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
														    F""" {len(snake_case__     )} and {len(snake_case__     )}"""     )

									return images_with_overflow

				def    _lowerCamelCase       (			self  ,			*snake_case__  ,			**snake_case__     ):

									'''simple docstring'''


									return self.tokenizer.batch_decode(*snake_case__  ,			**snake_case__     )

				def    _lowerCamelCase       (			self  ,			*snake_case__  ,			**snake_case__     ):

									'''simple docstring'''


									return self.tokenizer.decode(*snake_case__  ,			**snake_case__     )

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,			snake_case__  ,			)
									return self.image_processor_class

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,			snake_case__  ,			)
									return self.image_processor





from __future__ import annotations

import random
from collections.abc import Iterable


class    _lowercase :
				'''simple docstring'''







				def __init__(			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= {literal: None for literal in literals}

				def __str__(			self     ):

									'''simple docstring'''


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

				def __len__(			self     ):

									'''simple docstring'''


									return len(self.literals     )

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


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

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									for literal in self.literals:
														UpperCamelCase_			= 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    _lowercase :
				'''simple docstring'''







				def __init__(			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= list(snake_case__     )

				def __str__(			self     ):

									'''simple docstring'''


									return "{" + " , ".join(str(snake_case__     ) for clause in self.clauses     ) + "}"




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



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



def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= formula.clauses
					UpperCamelCase_			= []
					for clause in formula.clauses:
										for literal in clause.literals:
															UpperCamelCase_			= literal[:2]
															if symbol not in symbols_set:
																				symbols_set.append(_lowerCAmelCase)
					return clauses, symbols_set



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= []
					UpperCamelCase_			= {}
					UpperCamelCase_			= []

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

					for s in symbols:
										UpperCamelCase_			= s + "'"
										if (s in literals and sym not in literals) or (
										    s not in literals and sym in literals
										):
															pure_symbols.append(_lowerCAmelCase)
					for p in pure_symbols:
										UpperCamelCase_			= None
					for s in pure_symbols:
										UpperCamelCase_			= s + "'"
										if s in literals:
															UpperCamelCase_			= True
										elif sym in literals:
															UpperCamelCase_			= False
					return pure_symbols, assignment



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

					return unit_symbols, assignment



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

					if check_clause_all_true:
										return True, model

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

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

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

					return dpll_algorithm(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase) or dpll_algorithm(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

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

	UpperCAmelCase			,			UpperCAmelCase :				Any							=generate_parameters(formula)
	UpperCAmelCase			,			UpperCAmelCase :				str							=dpll_algorithm(clauses, symbols, {})

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