

import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
 from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor



class     lowercase_       :



      def __init__(     self :							Tuple     ,   A__ :							Tuple     ,   A__ :							Optional[Any]=13     ,   A__ :							str=32     ,   A__ :							Optional[int]=2     ,   A__ :							str=3     ,   A__ :							Optional[int]=16     ,   A__ :							int=[1, 2, 1]     ,   A__ :							Dict=[2, 2, 4]     ,   A__ :							Optional[Any]=2     ,   A__ :							List[str]=2.0     ,   A__ :							str=True     ,   A__ :							int=0.0     ,   A__ :							List[Any]=0.0     ,   A__ :							int=0.1     ,   A__ :							Tuple="gelu"     ,   A__ :							str=False     ,   A__ :							str=True     ,   A__ :							Any=0.02     ,   A__ :							int=1e-5     ,   A__ :							Union[str, Any]=True     ,   A__ :							List[str]=None     ,   A__ :							Optional[int]=True     ,   A__ :							List[str]=10     ,   A__ :							Tuple=8     ,   A__ :							List[Any]=["stage1", "stage2"]     ,   A__ :							Any=[1, 2]     ,   )       ->	Tuple:
        _snake_case		=				parent
        _snake_case		=				batch_size
        _snake_case		=				image_size
        _snake_case		=				patch_size
        _snake_case		=				num_channels
        _snake_case		=				embed_dim
        _snake_case		=				depths
        _snake_case		=				num_heads
        _snake_case		=				window_size
        _snake_case		=				mlp_ratio
        _snake_case		=				qkv_bias
        _snake_case		=				hidden_dropout_prob
        _snake_case		=				attention_probs_dropout_prob
        _snake_case		=				drop_path_rate
        _snake_case		=				hidden_act
        _snake_case		=				use_absolute_embeddings
        _snake_case		=				patch_norm
        _snake_case		=				layer_norm_eps
        _snake_case		=				initializer_range
        _snake_case		=				is_training
        _snake_case		=				scope
        _snake_case		=				use_labels
        _snake_case		=				type_sequence_label_size
        _snake_case		=				encoder_stride
        _snake_case		=				out_features
        _snake_case		=				out_indices



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	str:
        _snake_case		=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        _snake_case		=				None
        if self.use_labels:
          _snake_case		=				ids_tensor([self.batch_size]     ,   self.type_sequence_label_size						)

        _snake_case		=				self.get_config()

        return config, pixel_values, labels



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	int:
        return SwinConfig(
            image_size=self.image_size     ,   patch_size=self.patch_size     ,   num_channels=self.num_channels     ,   embed_dim=self.embed_dim     ,   depths=self.depths     ,   num_heads=self.num_heads     ,   window_size=self.window_size     ,   mlp_ratio=self.mlp_ratio     ,   qkv_bias=self.qkv_bias     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   drop_path_rate=self.drop_path_rate     ,   hidden_act=self.hidden_act     ,   use_absolute_embeddings=self.use_absolute_embeddings     ,   path_norm=self.patch_norm     ,   layer_norm_eps=self.layer_norm_eps     ,   initializer_range=self.initializer_range     ,   encoder_stride=self.encoder_stride     ,   out_features=self.out_features     ,   out_indices=self.out_indices     ,   )



      def 		UpperCamelCase_						(     self :							Dict     ,   A__ :							Dict     ,   A__ :							List[Any]     ,   A__ :							int						)       ->	int:
        _snake_case		=				SwinModel(config=snake_case__						)
        model.to(snake_case__						)
        model.eval()
        _snake_case		=				model(snake_case__						)

        _snake_case		=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
        _snake_case		=				int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

        self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, expected_seq_len, expected_dim)						)



      def 		UpperCamelCase_						(     self :							Dict     ,   A__ :							Optional[Any]     ,   A__ :							Dict     ,   A__ :							List[Any]						)       ->	str:
        _snake_case		=				SwinBackbone(config=snake_case__						)
        model.to(snake_case__						)
        model.eval()
        _snake_case		=				model(snake_case__						)

        # verify hidden states
        self.parent.assertEqual(len(result.feature_maps						)     ,   len(config.out_features						)						)
        self.parent.assertListEqual(list(result.feature_maps[0].shape						)     ,   [self.batch_size, model.channels[0], 16, 16]						)

        # verify channels
        self.parent.assertEqual(len(model.channels						)     ,   len(config.out_features						)						)

        # verify backbone works with out_features=None
        _snake_case		=				None
        _snake_case		=				SwinBackbone(config=snake_case__						)
        model.to(snake_case__						)
        model.eval()
        _snake_case		=				model(snake_case__						)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps						)     ,   1						)
        self.parent.assertListEqual(list(result.feature_maps[0].shape						)     ,   [self.batch_size, model.channels[-1], 4, 4]						)

        # verify channels
        self.parent.assertEqual(len(model.channels						)     ,   1						)



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							Dict     ,   A__ :							Dict     ,   A__ :							Union[str, Any]						)       ->	List[str]:
        _snake_case		=				SwinForMaskedImageModeling(config=snake_case__						)
        model.to(snake_case__						)
        model.eval()
        _snake_case		=				model(snake_case__						)
        self.parent.assertEqual(
            result.logits.shape     ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

        # test greyscale images
        _snake_case		=				1
        _snake_case		=				SwinForMaskedImageModeling(snake_case__						)
        model.to(snake_case__						)
        model.eval()

        _snake_case		=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
        _snake_case		=				model(snake_case__						)
        self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, 1, self.image_size, self.image_size)						)



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							Union[str, Any]     ,   A__ :							List[Any]     ,   A__ :							Dict						)       ->	Optional[int]:
        _snake_case		=				self.type_sequence_label_size
        _snake_case		=				SwinForImageClassification(snake_case__						)
        model.to(snake_case__						)
        model.eval()
        _snake_case		=				model(snake_case__     ,   labels=snake_case__						)
        self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)						)

        # test greyscale images
        _snake_case		=				1
        _snake_case		=				SwinForImageClassification(snake_case__						)
        model.to(snake_case__						)
        model.eval()

        _snake_case		=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
        _snake_case		=				model(snake_case__						)
        self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)						)







      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	str:
        _snake_case		=				self.prepare_config_and_inputs()
        (
            (
            _snake_case     
        ),	(
            _snake_case     
        ),	(
            _snake_case     
        ),	     
        )		=				config_and_inputs
        _snake_case		=				{'''pixel_values''': pixel_values}
        return config, inputs_dict



@require_torch
class     lowercase_       (    a_  ,				a_  ,				unittest.TestCase	):
      UpperCamelCase_     :						Optional[int]		 =							(
          (
              SwinModel,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase_     :						Optional[Any]		 =							(
          {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
          if is_torch_available()
          else {}
      )
      UpperCamelCase_     :						Union[str, Any]		 =							True

      UpperCamelCase_     :						Dict		 =							False
      UpperCamelCase_     :						str		 =							False
      UpperCamelCase_     :						List[Any]		 =							False



      def 		UpperCamelCase_						(     self :							Dict						)       ->	Union[str, Any]:
        _snake_case		=				SwinModelTester(self						)
        _snake_case		=				ConfigTester(self     ,   config_class=snake_case__     ,   embed_dim=37						)



      def 		UpperCamelCase_						(     self :							List[Any]						)       ->	List[str]:
        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()



      def 		UpperCamelCase_						(     self :							List[Any]						)       ->	Optional[int]:
        return



      def 		UpperCamelCase_						(     self :							int						)       ->	Tuple:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case__						)



      @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''						)
      def 		UpperCamelCase_						(     self :							Any						)       ->	List[Any]:
        pass



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	List[Any]:
        super().test_training_gradient_checkpointing()



      def 		UpperCamelCase_						(     self :							Dict						)       ->	Tuple:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*snake_case__						)



      def 		UpperCamelCase_						(     self :							int						)       ->	List[str]:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__						)



      def 		UpperCamelCase_						(     self :							List[Any]						)       ->	str:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case__						)



      @unittest.skip(reason='''Swin does not use inputs_embeds'''						)
      def 		UpperCamelCase_						(     self :							Any						)       ->	List[Any]:
        pass



      @unittest.skip(reason='''Swin Transformer does not use feedforward chunking'''						)
      def 		UpperCamelCase_						(     self :							Tuple						)       ->	Union[str, Any]:
        pass



      def 		UpperCamelCase_						(     self :							Dict						)       ->	Optional[Any]:
        _snake_case,	_snake_case		=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          _snake_case		=				model_class(snake_case__						)
          self.assertIsInstance(model.get_input_embeddings()     ,   (nn.Module)						)
          _snake_case		=				model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(snake_case__     ,   nn.Linear						)						)



      def 		UpperCamelCase_						(     self :							Optional[int]						)       ->	Optional[int]:
        _snake_case,	_snake_case		=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          _snake_case		=				model_class(snake_case__						)
          _snake_case		=				inspect.signature(model.forward						)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          _snake_case		=				[*signature.parameters.keys()]

          _snake_case		=				['''pixel_values''']
          self.assertListEqual(arg_names[:1]     ,   snake_case__						)



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Optional[Any]:
        _snake_case,	_snake_case		=				self.model_tester.prepare_config_and_inputs_for_common()
        _snake_case		=				True

        for model_class in self.all_model_classes:
          _snake_case		=				True
          _snake_case		=				False
          _snake_case		=				True
          _snake_case		=				model_class(snake_case__						)
          model.to(snake_case__						)
          model.eval()
          with torch.no_grad():
            _snake_case		=				model(**self._prepare_for_class(snake_case__     ,   snake_case__						)						)
          _snake_case		=				outputs.attentions
          _snake_case		=				len(self.model_tester.depths						)
          self.assertEqual(len(snake_case__						)     ,   snake_case__						)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _snake_case		=				True
          _snake_case		=				config.window_size**2
          _snake_case		=				model_class(snake_case__						)
          model.to(snake_case__						)
          model.eval()
          with torch.no_grad():
            _snake_case		=				model(**self._prepare_for_class(snake_case__     ,   snake_case__						)						)
          _snake_case		=				outputs.attentions
          self.assertEqual(len(snake_case__						)     ,   snake_case__						)

          self.assertListEqual(
              list(attentions[0].shape[-3:]						)     ,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,   )
          _snake_case		=				len(snake_case__						)

          # Check attention is always last and order is fine
          _snake_case		=				True
          _snake_case		=				True
          _snake_case		=				model_class(snake_case__						)
          model.to(snake_case__						)
          model.eval()
          with torch.no_grad():
            _snake_case		=				model(**self._prepare_for_class(snake_case__     ,   snake_case__						)						)

          # also another +1 for reshaped_hidden_states
          _snake_case		=				1 if model_class.__name__ == '''SwinBackbone''' else 2
          self.assertEqual(out_len + added_hidden_states     ,   len(snake_case__						)						)

          _snake_case		=				outputs.attentions

          self.assertEqual(len(snake_case__						)     ,   snake_case__						)

          self.assertListEqual(
              list(self_attentions[0].shape[-3:]						)     ,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,   )



      def 		UpperCamelCase_						(     self :							int     ,   A__ :							List[Any]     ,   A__ :							str     ,   A__ :							Dict     ,   A__ :							Union[str, Any]						)       ->	List[str]:
        _snake_case		=				model_class(snake_case__						)
        model.to(snake_case__						)
        model.eval()

        with torch.no_grad():
          _snake_case		=				model(**self._prepare_for_class(snake_case__     ,   snake_case__						)						)

        _snake_case		=				outputs.hidden_states

        _snake_case		=				getattr(
            self.model_tester     ,   '''expected_num_hidden_layers'''     ,   len(self.model_tester.depths						) + 1						)
        self.assertEqual(len(snake_case__						)     ,   snake_case__						)

        # Swin has a different seq_length
        _snake_case		=				(
            config.patch_size
            if isinstance(config.patch_size     ,   collections.abc.Iterable						)
            else (config.patch_size, config.patch_size)
        )

        _snake_case		=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]						)     ,   [num_patches, self.model_tester.embed_dim]     ,   )

        if not model_class.__name__ == "SwinBackbone":
          _snake_case		=				outputs.reshaped_hidden_states
          self.assertEqual(len(snake_case__						)     ,   snake_case__						)

          _snake_case,	_snake_case,	_snake_case,	_snake_case		=				reshaped_hidden_states[0].shape
          _snake_case		=				(
              reshaped_hidden_states[0].view(snake_case__     ,   snake_case__     ,   height * width						).permute(0     ,   2     ,   1						)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]						)     ,   [num_patches, self.model_tester.embed_dim]     ,   )



      def 		UpperCamelCase_						(     self :							Dict						)       ->	Dict:
        _snake_case,	_snake_case		=				self.model_tester.prepare_config_and_inputs_for_common()

        _snake_case		=				(
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size     ,   collections.abc.Iterable						)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes:
          _snake_case		=				True
          self.check_hidden_states_output(snake_case__     ,   snake_case__     ,   snake_case__     ,   snake_case__						)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          _snake_case		=				True

          self.check_hidden_states_output(snake_case__     ,   snake_case__     ,   snake_case__     ,   snake_case__						)



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Optional[int]:
        _snake_case,	_snake_case		=				self.model_tester.prepare_config_and_inputs_for_common()
        _snake_case		=				3

        _snake_case		=				(
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size     ,   collections.abc.Iterable						)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        _snake_case		=				(
            config.patch_size
            if isinstance(config.patch_size     ,   collections.abc.Iterable						)
            else (config.patch_size, config.patch_size)
        )

        _snake_case		=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        _snake_case		=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
          _snake_case		=				True
          self.check_hidden_states_output(snake_case__     ,   snake_case__     ,   snake_case__     ,   (padded_height, padded_width)						)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          _snake_case		=				True
          self.check_hidden_states_output(snake_case__     ,   snake_case__     ,   snake_case__     ,   (padded_height, padded_width)						)



      @slow
      def 		UpperCamelCase_						(     self :							str						)       ->	List[Any]:


        for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _snake_case		=				SwinModel.from_pretrained(snake_case__						)
          self.assertIsNotNone(snake_case__						)







      def 		UpperCamelCase_						(     self :							Optional[int]						)       ->	List[str]:
        _snake_case,	_snake_case		=				self.model_tester.prepare_config_and_inputs_for_common()

        _snake_case		=				_config_zero_init(snake_case__						)
        for model_class in self.all_model_classes:
          _snake_case		=				model_class(config=snake_case__						)
          for name, param in model.named_parameters():
            if "embeddings" not in name and param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9).round() / 1e9).item()     ,   [0.0, 1.0]     ,   msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""     ,   )



@require_vision
@require_torch
class     lowercase_       (    unittest.TestCase	):



      @cached_property
      def 		UpperCamelCase_						(     self :							List[str]						)       ->	List[str]:
        return (
            AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''						)
            if is_vision_available()
            else None
        )







      @slow
      def 		UpperCamelCase_						(     self :							List[str]						)       ->	Dict:
        _snake_case		=				SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''						).to(snake_case__						)
        _snake_case		=				self.default_image_processor

        _snake_case		=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
        _snake_case		=				image_processor(images=snake_case__     ,   return_tensors='''pt'''						).to(snake_case__						)

        # forward pass
        with torch.no_grad():
          _snake_case		=				model(**snake_case__						)

        # verify the logits
        _snake_case		=				torch.Size((1, 1000)						)
        self.assertEqual(outputs.logits.shape     ,   snake_case__						)
        _snake_case		=				torch.tensor([-0.0948, -0.6454, -0.0921]						).to(snake_case__						)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,   snake_case__     ,   atol=1e-4						)						)



@require_torch
class     lowercase_       (    unittest.TestCase  ,				a_	):
      UpperCamelCase_     :						List[str]		 =							(SwinBackbone,) if is_torch_available() else ()
      UpperCamelCase_     :						Optional[Any]		 =							SwinConfig



      def 		UpperCamelCase_						(     self :							str						)       ->	Any:
        _snake_case		=				SwinModelTester(self						)






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.""")
