'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.mpnet.modeling_tf_mpnet import (
						    TFMPNetForMaskedLM,
						    TFMPNetForMultipleChoice,
						    TFMPNetForQuestionAnswering,
						    TFMPNetForSequenceClassification,
						    TFMPNetForTokenClassification,
						    TFMPNetModel,
						)




class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			def __init__(       self							, snake_case_							, snake_case_=13							, snake_case_=7							, snake_case_=True							, snake_case_=True							, snake_case_=False							, snake_case_=True							, snake_case_=99							, snake_case_=64							, snake_case_=2							, snake_case_=4							, snake_case_=64							, snake_case_="gelu"							, snake_case_=0.1							, snake_case_=0.1							, snake_case_=512							, snake_case_=16							, snake_case_=2							, snake_case_=0.02							, snake_case_=3							, snake_case_=4							, snake_case_=None							, ):
									snake_case   :					Optional[int]							=						parent
									snake_case   :					List[Any]							=						batch_size
									snake_case   :					Dict							=						seq_length
									snake_case   :					Union[str, Any]							=						is_training
									snake_case   :					Optional[int]							=						use_input_mask
									snake_case   :					int							=						use_token_type_ids
									snake_case   :					str							=						use_labels
									snake_case   :					Optional[int]							=						vocab_size
									snake_case   :					Union[str, Any]							=						hidden_size
									snake_case   :					Dict							=						num_hidden_layers
									snake_case   :					str							=						num_attention_heads
									snake_case   :					Union[str, Any]							=						intermediate_size
									snake_case   :					str							=						hidden_act
									snake_case   :					Optional[Any]							=						hidden_dropout_prob
									snake_case   :					Dict							=						attention_probs_dropout_prob
									snake_case   :					Any							=						max_position_embeddings
									snake_case   :					Optional[int]							=						type_vocab_size
									snake_case   :					Union[str, Any]							=						type_sequence_label_size
									snake_case   :					Any							=						initializer_range
									snake_case   :					Any							=						num_labels
									snake_case   :					Union[str, Any]							=						num_choices
									snake_case   :					Optional[int]							=						scope



			def      lowerCamelCase   (       self				):
									snake_case   :					Tuple							=						ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size				)

									snake_case   :					List[str]							=						None
									if self.use_input_mask:
															snake_case   :					Optional[Any]							=						random_attention_mask([self.batch_size, self.seq_length]				)

									snake_case   :					Any							=						None
									snake_case   :					Optional[int]							=						None
									snake_case   :					List[Any]							=						None
									if self.use_labels:
															snake_case   :					Optional[int]							=						ids_tensor([self.batch_size]							, self.type_sequence_label_size				)
															snake_case   :					List[str]							=						ids_tensor([self.batch_size, self.seq_length]							, self.num_labels				)
															snake_case   :					Optional[int]							=						ids_tensor([self.batch_size]							, self.num_choices				)

									snake_case   :					Tuple							=						MPNetConfig(
									    vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, max_position_embeddings=self.max_position_embeddings							, initializer_range=self.initializer_range							, )
									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_				):
									snake_case   :					Dict							=						TFMPNetModel(config=snake_case_				)
									snake_case   :					List[Any]							=						{"input_ids": input_ids, "attention_mask": input_mask}
									snake_case   :					Union[str, Any]							=						model(snake_case_				)
									snake_case   :					str							=						[input_ids, input_mask]
									snake_case   :					Optional[Any]							=						model(snake_case_				)
									self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_				):
									snake_case   :					Optional[Any]							=						TFMPNetForMaskedLM(config=snake_case_				)
									snake_case   :					Tuple							=						{"input_ids": input_ids, "attention_mask": input_mask}
									snake_case   :					Optional[int]							=						model(snake_case_				)
									self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_				):
									snake_case   :					str							=						TFMPNetForQuestionAnswering(config=snake_case_				)
									snake_case   :					List[Any]							=						{
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									}
									snake_case   :					Dict							=						model(snake_case_				)
									self.parent.assertEqual(result.start_logits.shape							, (self.batch_size, self.seq_length)				)
									self.parent.assertEqual(result.end_logits.shape							, (self.batch_size, self.seq_length)				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_				):
									snake_case   :					Optional[int]							=						self.num_labels
									snake_case   :					Optional[int]							=						TFMPNetForSequenceClassification(snake_case_				)
									snake_case   :					List[str]							=						{"input_ids": input_ids, "attention_mask": input_mask}
									snake_case   :					Dict							=						model(snake_case_				)
									self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_labels)				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_				):
									snake_case   :					Any							=						self.num_choices
									snake_case   :					str							=						TFMPNetForMultipleChoice(snake_case_				)
									snake_case   :					Dict							=						tf.tile(tf.expand_dims(snake_case_							, 1				)							, (1, self.num_choices, 1)				)
									snake_case   :					Any							=						tf.tile(tf.expand_dims(snake_case_							, 1				)							, (1, self.num_choices, 1)				)
									snake_case   :					str							=						{
									    "input_ids": multiple_choice_inputs_ids,
									    "attention_mask": multiple_choice_input_mask,
									}
									snake_case   :					Tuple							=						model(snake_case_				)
									self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_choices)				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_				):
									snake_case   :					Optional[int]							=						self.num_labels
									snake_case   :					Tuple							=						TFMPNetForTokenClassification(snake_case_				)
									snake_case   :					Any							=						{"input_ids": input_ids, "attention_mask": input_mask}
									snake_case   :					Tuple							=						model(snake_case_				)
									self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.num_labels)				)






			def      lowerCamelCase   (       self				):
									snake_case   :					str							=						self.prepare_config_and_inputs()
									((snake_case)							,				(snake_case)							,				(snake_case)							,				(snake_case)							,				(snake_case)							,				(snake_case))   :					List[str]							=						config_and_inputs
									snake_case   :					int							=						{"input_ids": input_ids, "attention_mask": input_mask}
									return config, inputs_dict







@require_tf
class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase       ,							_UpperCAmelCase       ,							unittest.TestCase			):



			"""simple docstring"""






			__magic_name__   :							Optional[Any]             =					(
			    (
			        TFMPNetForMaskedLM,
			        TFMPNetForMultipleChoice,
			        TFMPNetForQuestionAnswering,
			        TFMPNetForSequenceClassification,
			        TFMPNetForTokenClassification,
			        TFMPNetModel,
			    )
			    if is_tf_available()
			    else ()
			)
			__magic_name__   :							Optional[int]             =					(
			    {
			        """feature-extraction""": TFMPNetModel,
			        """fill-mask""": TFMPNetForMaskedLM,
			        """question-answering""": TFMPNetForQuestionAnswering,
			        """text-classification""": TFMPNetForSequenceClassification,
			        """token-classification""": TFMPNetForTokenClassification,
			        """zero-shot""": TFMPNetForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			__magic_name__   :							Any             =					False
			__magic_name__   :							List[str]             =					False



			def      lowerCamelCase   (       self				):
									snake_case   :					Optional[Any]							=						TFMPNetModelTester(self				)
									snake_case   :					str							=						ConfigTester(self							, config_class=snake_case_							, hidden_size=37				)



			def      lowerCamelCase   (       self				):
									self.config_tester.run_common_tests()



			def      lowerCamelCase   (       self				):
									snake_case   :					Any							=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_model(*snake_case_				)



			def      lowerCamelCase   (       self				):
									snake_case   :					int							=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_masked_lm(*snake_case_				)



			def      lowerCamelCase   (       self				):
									snake_case   :					Dict							=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case_				)



			def      lowerCamelCase   (       self				):
									snake_case   :					Tuple							=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case_				)



			def      lowerCamelCase   (       self				):
									snake_case   :					Union[str, Any]							=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case_				)



			def      lowerCamelCase   (       self				):
									snake_case   :					Union[str, Any]							=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case_				)






			@slow
			def      lowerCamelCase   (       self				):
									for model_name in ["microsoft/mpnet-base"]:
															snake_case   :					List[Any]							=						TFMPNetModel.from_pretrained(snake_case_				)
															self.assertIsNotNone(snake_case_				)







@require_tf
class 		SCREAMING_SNAKE_CASE_				(unittest.TestCase			):



			"""simple docstring"""






			@slow
			def      lowerCamelCase   (       self				):
									snake_case   :					Tuple							=						TFMPNetModel.from_pretrained("microsoft/mpnet-base"				)
									snake_case   :					Optional[Any]							=						tf.constant([[0, 1, 2, 3, 4, 5]]				)
									snake_case   :					int							=						model(snake_case_				)[0]

									snake_case   :					str							=						[1, 6, 768]
									self.assertEqual(output.shape							, snake_case_				)

									snake_case   :					str							=						tf.constant(
									    [
									        [
									            [-0.1_067_172, 0.08_216_473, 0.0_024_543],
									            [-0.03_465_879, 0.8_354_118, -0.03_252_288],
									            [-0.06_569_476, -0.12_424_111, -0.0_494_436],
									        ]
									    ]				)
									tf.debugging.assert_near(output[:, :3, :3]							, snake_case_							, atol=1e-4				)






'''simple docstring'''

from __future__ import annotations

import random
from collections.abc import Iterable




class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			def __init__(       self							, snake_case_				):
									snake_case   :					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							, snake_case_				):
									for literal in self.literals:
															snake_case   :					Union[str, Any]							=						literal[:2]
															if symbol in model:
																					snake_case   :					str							=						model[symbol]
															else:
																					continue
															if value is not None:
																					# Complement assignment if literal is in complemented form
																					if literal.endswith("'"				):
																											snake_case   :					Dict							=						not value
															snake_case   :					str							=						value






			def      lowerCamelCase   (       self							, snake_case_				):
									for literal in self.literals:
															snake_case   :					int							=						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 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			def __init__(       self							, snake_case_				):
									snake_case   :					Tuple							=						list(snake_case_				)






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







def 					UpperCAmelCase__      (							)						->		Clause:



						"""simple docstring"""

						snake_case   :					Optional[Any]							=						[]
						snake_case   :					Optional[Any]							=						random.randint(1,			5)
						snake_case   :					Tuple							=						"A"
						snake_case   :					Optional[Any]							=						0
						while i < no_of_literals:
												snake_case   :					Union[str, Any]							=						random.randint(1,			5)
												snake_case   :					List[Any]							=						base_var + str(_lowerCAmelCase)
												snake_case   :					Union[str, Any]							=						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 					UpperCAmelCase__      (							)						->		Formula:



						"""simple docstring"""

						snake_case   :					set[Clause]							=						set()
						snake_case   :					Union[str, Any]							=						random.randint(1,			10)
						while len(_lowerCAmelCase) < no_of_clauses:
												clauses.add(generate_clause())
						return Formula(_lowerCAmelCase)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Formula)						->		tuple[list[Clause], list[str]]:



						"""simple docstring"""

						snake_case   :					Dict							=						formula.clauses
						snake_case   :					List[Any]							=						[]
						for clause in formula.clauses:
												for literal in clause.literals:
																		snake_case   :					Any							=						literal[:2]
																		if symbol not in symbols_set:
																								symbols_set.append(_lowerCAmelCase)
						return clauses, symbols_set


def 					UpperCAmelCase__      (							_lowerCAmelCase						: list[Clause],			_lowerCAmelCase						: list[str],			_lowerCAmelCase						: dict[str, bool | None])						->		tuple[list[str], dict[str, bool | None]]:



						"""simple docstring"""

						snake_case   :					Optional[int]							=						[]
						snake_case   :					dict[str, bool | None]							=						{}
						snake_case   :					Dict							=						[]

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

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: list[Clause],			_lowerCAmelCase						: dict[str, bool | None])						->		tuple[list[str], dict[str, bool | None]]:



						"""simple docstring"""

						snake_case   :					Any							=						[]
						for clause in clauses:
												if len(_lowerCAmelCase) == 1:
																		unit_symbols.append(list(clause.literals.keys())[0])
												else:
																		snake_case							,				snake_case   :					Union[str, Any]							=						0, 0
																		for literal, value in clause.literals.items():
																								if value is False:
																														f_count += 1
																								elif value is None:
																														snake_case   :					str							=						literal
																														n_count += 1
																		if f_count == len(_lowerCAmelCase) - 1 and n_count == 1:
																								unit_symbols.append(_lowerCAmelCase)
						snake_case   :					dict[str, bool | None]							=						{}
						for i in unit_symbols:
												snake_case   :					str							=						i[:2]
												snake_case   :					Any							=						len(_lowerCAmelCase) == 2
						snake_case   :					int							=						[i[:2] for i in unit_symbols]

						return unit_symbols, assignment


def 					UpperCAmelCase__      (							_lowerCAmelCase						: list[Clause],			_lowerCAmelCase						: list[str],			_lowerCAmelCase						: dict[str, bool | None])						->		tuple[bool | None, dict[str, bool | None] | None]:



						"""simple docstring"""

						snake_case   :					Optional[int]							=						True
						for clause in clauses:
												snake_case   :					List[Any]							=						clause.evaluate(_lowerCAmelCase)
												if clause_check is False:
																		return False, None
												elif clause_check is None:
																		snake_case   :					Union[str, Any]							=						False
																		continue

						if check_clause_all_true:
												return True, model

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

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

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

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()

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

						A_      ,   A_        =   generate_parameters(formula)
						A_      ,   A_        =   dpll_algorithm(clauses, symbols, {})

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





