




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase__    :					List[Any]					=						{
    "auto_factory": ["get_values"],
    "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"],
    "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"],
    "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"],
    "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"],
    "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					Union[str, Any]					=						[
		    "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
		    "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING",
		    "MODEL_FOR_AUDIO_XVECTOR_MAPPING",
		    "MODEL_FOR_BACKBONE_MAPPING",
		    "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
		    "MODEL_FOR_CAUSAL_LM_MAPPING",
		    "MODEL_FOR_CTC_MAPPING",
		    "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
		    "MODEL_FOR_DEPTH_ESTIMATION_MAPPING",
		    "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
		    "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING",
		    "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING",
		    "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
		    "MODEL_FOR_MASKED_LM_MAPPING",
		    "MODEL_FOR_MASK_GENERATION_MAPPING",
		    "MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
		    "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
		    "MODEL_FOR_OBJECT_DETECTION_MAPPING",
		    "MODEL_FOR_PRETRAINING_MAPPING",
		    "MODEL_FOR_QUESTION_ANSWERING_MAPPING",
		    "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
		    "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
		    "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
		    "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
		    "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
		    "MODEL_FOR_TEXT_ENCODING_MAPPING",
		    "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
		    "MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING",
		    "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING",
		    "MODEL_FOR_VISION_2_SEQ_MAPPING",
		    "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING",
		    "MODEL_MAPPING",
		    "MODEL_WITH_LM_HEAD_MAPPING",
		    "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
		    "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING",
		    "AutoModel",
		    "AutoBackbone",
		    "AutoModelForAudioClassification",
		    "AutoModelForAudioFrameClassification",
		    "AutoModelForAudioXVector",
		    "AutoModelForCausalLM",
		    "AutoModelForCTC",
		    "AutoModelForDepthEstimation",
		    "AutoModelForImageClassification",
		    "AutoModelForImageSegmentation",
		    "AutoModelForInstanceSegmentation",
		    "AutoModelForMaskGeneration",
		    "AutoModelForTextEncoding",
		    "AutoModelForMaskedImageModeling",
		    "AutoModelForMaskedLM",
		    "AutoModelForMultipleChoice",
		    "AutoModelForNextSentencePrediction",
		    "AutoModelForObjectDetection",
		    "AutoModelForPreTraining",
		    "AutoModelForQuestionAnswering",
		    "AutoModelForSemanticSegmentation",
		    "AutoModelForSeq2SeqLM",
		    "AutoModelForSequenceClassification",
		    "AutoModelForSpeechSeq2Seq",
		    "AutoModelForTableQuestionAnswering",
		    "AutoModelForTokenClassification",
		    "AutoModelForUniversalSegmentation",
		    "AutoModelForVideoClassification",
		    "AutoModelForVision2Seq",
		    "AutoModelForVisualQuestionAnswering",
		    "AutoModelForDocumentQuestionAnswering",
		    "AutoModelWithLMHead",
		    "AutoModelForZeroShotImageClassification",
		    "AutoModelForZeroShotObjectDetection",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					Union[str, Any]					=						[
		    "TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
		    "TF_MODEL_FOR_CAUSAL_LM_MAPPING",
		    "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
		    "TF_MODEL_FOR_MASK_GENERATION_MAPPING",
		    "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
		    "TF_MODEL_FOR_MASKED_LM_MAPPING",
		    "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
		    "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
		    "TF_MODEL_FOR_PRETRAINING_MAPPING",
		    "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
		    "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
		    "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
		    "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
		    "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
		    "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
		    "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
		    "TF_MODEL_FOR_TEXT_ENCODING_MAPPING",
		    "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
		    "TF_MODEL_FOR_VISION_2_SEQ_MAPPING",
		    "TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
		    "TF_MODEL_MAPPING",
		    "TF_MODEL_WITH_LM_HEAD_MAPPING",
		    "TFAutoModel",
		    "TFAutoModelForAudioClassification",
		    "TFAutoModelForCausalLM",
		    "TFAutoModelForImageClassification",
		    "TFAutoModelForMaskedImageModeling",
		    "TFAutoModelForMaskedLM",
		    "TFAutoModelForMaskGeneration",
		    "TFAutoModelForMultipleChoice",
		    "TFAutoModelForNextSentencePrediction",
		    "TFAutoModelForPreTraining",
		    "TFAutoModelForDocumentQuestionAnswering",
		    "TFAutoModelForQuestionAnswering",
		    "TFAutoModelForSemanticSegmentation",
		    "TFAutoModelForSeq2SeqLM",
		    "TFAutoModelForSequenceClassification",
		    "TFAutoModelForSpeechSeq2Seq",
		    "TFAutoModelForTableQuestionAnswering",
		    "TFAutoModelForTextEncoding",
		    "TFAutoModelForTokenClassification",
		    "TFAutoModelForVision2Seq",
		    "TFAutoModelForZeroShotImageClassification",
		    "TFAutoModelWithLMHead",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					Any					=						[
		    "FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
		    "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING",
		    "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
		    "FLAX_MODEL_FOR_MASKED_LM_MAPPING",
		    "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
		    "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
		    "FLAX_MODEL_FOR_PRETRAINING_MAPPING",
		    "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
		    "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
		    "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
		    "FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
		    "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
		    "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING",
		    "FLAX_MODEL_MAPPING",
		    "FlaxAutoModel",
		    "FlaxAutoModelForCausalLM",
		    "FlaxAutoModelForImageClassification",
		    "FlaxAutoModelForMaskedLM",
		    "FlaxAutoModelForMultipleChoice",
		    "FlaxAutoModelForNextSentencePrediction",
		    "FlaxAutoModelForPreTraining",
		    "FlaxAutoModelForQuestionAnswering",
		    "FlaxAutoModelForSeq2SeqLM",
		    "FlaxAutoModelForSequenceClassification",
		    "FlaxAutoModelForSpeechSeq2Seq",
		    "FlaxAutoModelForTokenClassification",
		    "FlaxAutoModelForVision2Seq",
		]


if TYPE_CHECKING:
		from .auto_factory import get_values
		from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
		from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
		from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
		from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
		from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_auto import (
				    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
				    MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
				    MODEL_FOR_AUDIO_XVECTOR_MAPPING,
				    MODEL_FOR_BACKBONE_MAPPING,
				    MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
				    MODEL_FOR_CAUSAL_LM_MAPPING,
				    MODEL_FOR_CTC_MAPPING,
				    MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
				    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
				    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
				    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
				    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
				    MODEL_FOR_MASK_GENERATION_MAPPING,
				    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
				    MODEL_FOR_MASKED_LM_MAPPING,
				    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
				    MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
				    MODEL_FOR_OBJECT_DETECTION_MAPPING,
				    MODEL_FOR_PRETRAINING_MAPPING,
				    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
				    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
				    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
				    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
				    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
				    MODEL_FOR_TEXT_ENCODING_MAPPING,
				    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
				    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
				    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
				    MODEL_FOR_VISION_2_SEQ_MAPPING,
				    MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
				    MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
				    MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
				    MODEL_MAPPING,
				    MODEL_WITH_LM_HEAD_MAPPING,
				    AutoBackbone,
				    AutoModel,
				    AutoModelForAudioClassification,
				    AutoModelForAudioFrameClassification,
				    AutoModelForAudioXVector,
				    AutoModelForCausalLM,
				    AutoModelForCTC,
				    AutoModelForDepthEstimation,
				    AutoModelForDocumentQuestionAnswering,
				    AutoModelForImageClassification,
				    AutoModelForImageSegmentation,
				    AutoModelForInstanceSegmentation,
				    AutoModelForMaskedImageModeling,
				    AutoModelForMaskedLM,
				    AutoModelForMaskGeneration,
				    AutoModelForMultipleChoice,
				    AutoModelForNextSentencePrediction,
				    AutoModelForObjectDetection,
				    AutoModelForPreTraining,
				    AutoModelForQuestionAnswering,
				    AutoModelForSemanticSegmentation,
				    AutoModelForSeqaSeqLM,
				    AutoModelForSequenceClassification,
				    AutoModelForSpeechSeqaSeq,
				    AutoModelForTableQuestionAnswering,
				    AutoModelForTextEncoding,
				    AutoModelForTokenClassification,
				    AutoModelForUniversalSegmentation,
				    AutoModelForVideoClassification,
				    AutoModelForVisionaSeq,
				    AutoModelForVisualQuestionAnswering,
				    AutoModelForZeroShotImageClassification,
				    AutoModelForZeroShotObjectDetection,
				    AutoModelWithLMHead,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_auto import (
				    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
				    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
				    TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
				    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
				    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
				    TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
				    TF_MODEL_FOR_MASKED_LM_MAPPING,
				    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
				    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
				    TF_MODEL_FOR_PRETRAINING_MAPPING,
				    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
				    TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
				    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
				    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
				    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
				    TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
				    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
				    TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
				    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
				    TF_MODEL_MAPPING,
				    TF_MODEL_WITH_LM_HEAD_MAPPING,
				    TFAutoModel,
				    TFAutoModelForAudioClassification,
				    TFAutoModelForCausalLM,
				    TFAutoModelForDocumentQuestionAnswering,
				    TFAutoModelForImageClassification,
				    TFAutoModelForMaskedImageModeling,
				    TFAutoModelForMaskedLM,
				    TFAutoModelForMaskGeneration,
				    TFAutoModelForMultipleChoice,
				    TFAutoModelForNextSentencePrediction,
				    TFAutoModelForPreTraining,
				    TFAutoModelForQuestionAnswering,
				    TFAutoModelForSemanticSegmentation,
				    TFAutoModelForSeqaSeqLM,
				    TFAutoModelForSequenceClassification,
				    TFAutoModelForSpeechSeqaSeq,
				    TFAutoModelForTableQuestionAnswering,
				    TFAutoModelForTextEncoding,
				    TFAutoModelForTokenClassification,
				    TFAutoModelForVisionaSeq,
				    TFAutoModelForZeroShotImageClassification,
				    TFAutoModelWithLMHead,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_auto import (
				    FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
				    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
				    FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
				    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
				    FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
				    FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
				    FLAX_MODEL_FOR_PRETRAINING_MAPPING,
				    FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
				    FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
				    FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
				    FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
				    FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
				    FLAX_MODEL_MAPPING,
				    FlaxAutoModel,
				    FlaxAutoModelForCausalLM,
				    FlaxAutoModelForImageClassification,
				    FlaxAutoModelForMaskedLM,
				    FlaxAutoModelForMultipleChoice,
				    FlaxAutoModelForNextSentencePrediction,
				    FlaxAutoModelForPreTraining,
				    FlaxAutoModelForQuestionAnswering,
				    FlaxAutoModelForSeqaSeqLM,
				    FlaxAutoModelForSequenceClassification,
				    FlaxAutoModelForSpeechSeqaSeq,
				    FlaxAutoModelForTokenClassification,
				    FlaxAutoModelForVisionaSeq,
				)

else:
		import sys

		lowercase__    :					Dict					=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












from __future__ import annotations

import random
from collections.abc import Iterable
class 		UpperCAmelCase       :







						'''simple docstring'''





						def __init__(						self							:					int	,     __lowercase							:					list[str]						):






							"""simple docstring"""






							snake_case_		   =					{literal: None for literal in literals}






						def __str__(						self							:					str						):






							"""simple docstring"""






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






						def __len__(						self							:					Optional[int]						):






							"""simple docstring"""






							return len(self.literals						)






						def 	snake_case__						(						self							:					Dict	,     __lowercase							:					dict[str, bool | None]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					List[Any]	,     __lowercase							:					dict[str, bool | None]						):






							"""simple docstring"""






							for literal in self.literals:
								snake_case_		   =					literal.rstrip("'"						) if literal.endswith("'"						) else literal + "'"
								if symbol in self.literals:
									return True

							self.assign(__lowercase						)
							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							:					Union[str, Any]	,     __lowercase							:					Iterable[Clause]						):






							"""simple docstring"""






							snake_case_		   =					list(__lowercase						)






						def __str__(						self							:					Dict						):






							"""simple docstring"""






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







def 						lowerCamelCase__   (		):







	'''simple docstring'''





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


def 						lowerCamelCase__   (		):







	'''simple docstring'''





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


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





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


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					[]
	snake_case_		   =					{}
	snake_case_		   =					[]

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

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


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





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

	return unit_symbols, assignment


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





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

	if check_clause_all_true:
		return True, model

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

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

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

	return dpll_algorithm(_A					,     _A					,     _A							) or dpll_algorithm(_A					,     _A					,     _A							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowercase__    :					Tuple					=						generate_formula()
		print(f'''The formula {formula} is''', end=" ")

		lowercase__		,	lowercase__    :					str					=						generate_parameters(formula)
		lowercase__		,	lowercase__    :					List[Any]					=						dpll_algorithm(clauses, symbols, {})

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