




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

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


if is_tf_available():
		import tensorflow as tf

		from transformers.models.gptj.modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    shape_list,
		)
class 		UpperCAmelCase       :







						'''simple docstring'''





						def __init__(						self							:					Union[str, Any]	,     __lowercase							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					parent
							snake_case_		   =					13
							snake_case_		   =					7
							snake_case_		   =					True
							snake_case_		   =					True
							snake_case_		   =					True
							snake_case_		   =					True
							snake_case_		   =					True
							snake_case_		   =					99
							snake_case_		   =					32
							snake_case_		   =					4
							snake_case_		   =					2
							snake_case_		   =					4
							snake_case_		   =					37
							snake_case_		   =					"gelu"
							snake_case_		   =					0.1
							snake_case_		   =					0.1
							snake_case_		   =					5_12
							snake_case_		   =					16
							snake_case_		   =					2
							snake_case_		   =					0.02
							snake_case_		   =					3
							snake_case_		   =					4
							snake_case_		   =					None
							snake_case_		   =					self.vocab_size - 1
							snake_case_		   =					self.vocab_size - 1
							snake_case_		   =					self.vocab_size - 1






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					ids_tensor([self.batch_size, self.seq_length]	,     self.vocab_size						)

							snake_case_		   =					None
							if self.use_input_mask:
								snake_case_		   =					random_attention_mask([self.batch_size, self.seq_length]						)

							snake_case_		   =					None
							if self.use_token_type_ids:
								snake_case_		   =					ids_tensor([self.batch_size, self.seq_length]	,     self.type_vocab_size						)

							snake_case_		   =					None
							if self.use_mc_token_ids:
								snake_case_		   =					ids_tensor([self.batch_size, self.num_choices]	,     self.seq_length						)

							snake_case_		   =					None
							snake_case_		   =					None
							snake_case_		   =					None
							if self.use_labels:
								snake_case_		   =					ids_tensor([self.batch_size]	,     self.type_sequence_label_size						)
								snake_case_		   =					ids_tensor([self.batch_size, self.seq_length]	,     self.num_labels						)
								snake_case_		   =					ids_tensor([self.batch_size]	,     self.num_choices						)

							snake_case_		   =					GPTJConfig(
							    vocab_size=self.vocab_size	,     n_embd=self.hidden_size	,     n_layer=self.num_hidden_layers	,     n_head=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	,     n_positions=self.max_position_embeddings	,     type_vocab_size=self.type_vocab_size	,     initializer_range=self.initializer_range	,     bos_token_id=self.bos_token_id	,     eos_token_id=self.eos_token_id	,     pad_token_id=self.pad_token_id	,     rotary_dim=self.rotary_dim	,     return_dict=__lowercase	,     )

							snake_case_		   =					ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,     2						)

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							    token_type_ids,
							    mc_token_ids,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							)






						def 	snake_case__						(						self							:					int	,     __lowercase							:					Tuple	,     __lowercase							:					Optional[int]	,     __lowercase							:					Any	,     __lowercase							:					List[Any]	,     __lowercase							:					Any	,     *__lowercase							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModel(config=__lowercase						)
							snake_case_		   =					{
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							snake_case_		   =					model(__lowercase						)

							snake_case_		   =					[input_ids, None, input_mask]  # None is the input for 'past'
							snake_case_		   =					model(__lowercase						)

							snake_case_		   =					model(__lowercase						)

							self.parent.assertEqual(result.last_hidden_state.shape	,     (self.batch_size, self.seq_length, self.hidden_size)						)






						def 	snake_case__						(						self							:					List[Any]	,     __lowercase							:					Optional[int]	,     __lowercase							:					str	,     __lowercase							:					Any	,     __lowercase							:					Optional[int]	,     __lowercase							:					int	,     *__lowercase							:					str						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModel(config=__lowercase						)

							# first forward pass
							snake_case_		   =					model(__lowercase	,     token_type_ids=__lowercase	,     use_cache=__lowercase						)
							snake_case_		   =					model(__lowercase	,     token_type_ids=__lowercase						)
							snake_case_		   =					model(__lowercase	,     token_type_ids=__lowercase	,     use_cache=__lowercase						)

							self.parent.assertTrue(len(__lowercase						) == len(__lowercase						)						)
							self.parent.assertTrue(len(__lowercase						) == len(__lowercase						) + 1						)

							snake_case_   ,				snake_case_		   =					outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							snake_case_		   =					ids_tensor((self.batch_size, 1)	,     config.vocab_size						)
							snake_case_		   =					ids_tensor([self.batch_size, 1]	,     self.type_vocab_size						)

							# append to next input_ids and token_type_ids
							snake_case_		   =					tf.concat([input_ids, next_tokens]	,     axis=-1						)
							snake_case_		   =					tf.concat([token_type_ids, next_token_types]	,     axis=-1						)

							snake_case_		   =					model(__lowercase	,     token_type_ids=__lowercase						)["last_hidden_state"]
							snake_case_		   =					model(__lowercase	,     token_type_ids=__lowercase	,     past_key_values=__lowercase						)[
							    "last_hidden_state"
							]

							# select random slice
							snake_case_		   =					int(ids_tensor((1,)	,     shape_list(__lowercase						)[-1]						)						)
							snake_case_		   =					output_from_no_past[:, -1, random_slice_idx]
							snake_case_		   =					output_from_past[:, 0, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__lowercase	,     __lowercase	,     rtol=1E-6						)






						def 	snake_case__						(						self							:					Optional[int]	,     __lowercase							:					List[Any]	,     __lowercase							:					Optional[Any]	,     __lowercase							:					Dict	,     __lowercase							:					Optional[Any]	,     __lowercase							:					int	,     *__lowercase							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModel(config=__lowercase						)

							# create attention mask
							snake_case_		   =					self.seq_length // 2
							snake_case_		   =					tf.ones((self.batch_size, half_seq_length)	,     dtype=tf.intaa						)
							snake_case_		   =					tf.zeros((self.batch_size, self.seq_length - half_seq_length)	,     dtype=tf.intaa						)
							snake_case_		   =					tf.concat([attn_mask_begin, attn_mask_end]	,     axis=1						)

							# first forward pass
							snake_case_   ,				snake_case_		   =					model(__lowercase	,     attention_mask=__lowercase						).to_tuple()

							# create hypothetical next token and extent to next_input_ids
							snake_case_		   =					ids_tensor((self.batch_size, 1)	,     config.vocab_size						)

							# change a random masked slice from input_ids
							snake_case_		   =					ids_tensor((1,)	,     __lowercase						).numpy() + 1
							snake_case_		   =					ids_tensor((self.batch_size, self.seq_length)	,     config.vocab_size						)
							snake_case_		   =					tf.range(self.seq_length						) == (self.seq_length - random_seq_idx_to_change)
							snake_case_		   =					tf.transpose(
							    tf.broadcast_to(tf.expand_dims(__lowercase	,     -1						)	,     (self.seq_length, self.batch_size)						)						)
							snake_case_		   =					tf.where(__lowercase	,     __lowercase	,     __lowercase						)

							# append to next input_ids and attn_mask
							snake_case_		   =					tf.concat([input_ids, next_tokens]	,     axis=-1						)
							snake_case_		   =					tf.concat([attn_mask, tf.ones((shape_list(__lowercase						)[0], 1)	,     dtype=tf.intaa						)]	,     axis=1						)

							# get two different outputs
							snake_case_		   =					model(__lowercase	,     attention_mask=__lowercase						)["last_hidden_state"]
							snake_case_		   =					model(__lowercase	,     past_key_values=__lowercase	,     attention_mask=__lowercase						)[
							    "last_hidden_state"
							]

							# select random slice
							snake_case_		   =					int(ids_tensor((1,)	,     shape_list(__lowercase						)[-1]						)						)
							snake_case_		   =					output_from_no_past[:, -1, random_slice_idx]
							snake_case_		   =					output_from_past[:, 0, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__lowercase	,     __lowercase	,     rtol=1E-12						)






						def 	snake_case__						(						self							:					Optional[Any]	,     __lowercase							:					Any	,     __lowercase							:					int	,     __lowercase							:					Optional[Any]	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Tuple	,     *__lowercase							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModel(config=__lowercase						)

							snake_case_		   =					input_ids[:1, :]
							snake_case_		   =					input_mask[:1, :]
							snake_case_		   =					token_type_ids[:1, :]
							snake_case_		   =					1

							# first forward pass
							snake_case_		   =					model(__lowercase	,     attention_mask=__lowercase	,     token_type_ids=__lowercase	,     use_cache=__lowercase						)

							snake_case_   ,				snake_case_		   =					outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							snake_case_		   =					ids_tensor((self.batch_size, 3)	,     config.vocab_size						)
							snake_case_		   =					ids_tensor((self.batch_size, 3)	,     2						)
							snake_case_		   =					ids_tensor((self.batch_size, 3)	,     self.type_vocab_size						)

							# append to next input_ids and token_type_ids
							snake_case_		   =					tf.concat([input_ids, next_tokens]	,     axis=-1						)
							snake_case_		   =					tf.concat([input_mask, next_attn_mask]	,     axis=-1						)
							snake_case_		   =					tf.concat([token_type_ids, next_token_types]	,     axis=-1						)

							snake_case_		   =					model(
							    __lowercase	,     token_type_ids=__lowercase	,     attention_mask=__lowercase						)["last_hidden_state"]
							snake_case_		   =					model(
							    __lowercase	,     token_type_ids=__lowercase	,     attention_mask=__lowercase	,     past_key_values=__lowercase	,     )["last_hidden_state"]
							self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]						)

							# select random slice
							snake_case_		   =					int(ids_tensor((1,)	,     shape_list(__lowercase						)[-1]						)						)
							snake_case_		   =					output_from_no_past[:, -3:, random_slice_idx]
							snake_case_		   =					output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__lowercase	,     __lowercase	,     rtol=1E-3						)






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Any	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Optional[Any]	,     __lowercase							:					int	,     *__lowercase							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJForCausalLM(config=__lowercase						)
							snake_case_		   =					{
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							snake_case_		   =					model(__lowercase						)
							self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.seq_length, self.vocab_size)						)






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					self.prepare_config_and_inputs()

							(
							    (
							    snake_case_
							)   ,				(
							    snake_case_
							)   ,				(
							    snake_case_
							)   ,				(
							    snake_case_
							)   ,				(
							    snake_case_
							)   ,				(
							    snake_case_
							)   ,				(
							    snake_case_
							)   ,				(
							    snake_case_
							)   ,				(
							    snake_case_
							)   ,				
							)		   =					config_and_inputs

							snake_case_		   =					{
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							    "attention_mask": input_mask,
							}
							return config, inputs_dict







@require_tf
class 		UpperCAmelCase       (    UpperCAmelCase__	,       UpperCAmelCase__	,       UpperCAmelCase__	,       unittest.TestCase      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (
						    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
						    if is_tf_available()
						    else ()
						)

						lowerCAmelCase_							=       (TFGPTJForCausalLM,) if is_tf_available() else ()
						lowerCAmelCase_							=       (
						    {
						        '''feature-extraction''': TFGPTJModel,
						        '''question-answering''': TFGPTJForQuestionAnswering,
						        '''text-classification''': TFGPTJForSequenceClassification,
						        '''text-generation''': TFGPTJForCausalLM,
						        '''zero-shot''': TFGPTJForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						lowerCAmelCase_							=       False
						lowerCAmelCase_							=       False
						lowerCAmelCase_							=       False
						lowerCAmelCase_							=       False






						def 	snake_case__						(						self							:					Union[str, Any]	,     __lowercase							:					Optional[int]	,     __lowercase							:					Any	,     __lowercase							:					str	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Dict						):






							"""simple docstring"""






							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith("Fast"						)
							):
								# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
								# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
								# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
								return True

							return False






						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModelTester(self						)
							snake_case_		   =					ConfigTester(self	,     config_class=__lowercase	,     n_embd=37						)






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model(*__lowercase						)






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_past(*__lowercase						)






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_attention_mask_past(*__lowercase						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_past_large_inputs(*__lowercase						)






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_lm_head_model(*__lowercase						)






						@slow
						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) > 0	,     "skip testing on GPU for now to avoid GPU OOM."	,     )
						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B"	,     from_pt=__lowercase						)
							self.assertIsNotNone(__lowercase						)






						@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."						)
						def 	snake_case__						(						self							:					str						):






							"""simple docstring"""






							super().test_resize_token_embeddings()







@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 		UpperCAmelCase       (    unittest.TestCase      ):







						'''simple docstring'''





						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"	,     from_pt=__lowercase						)
							snake_case_		   =					tf.convert_to_tensor([[4_64, 32_90]]	,     dtype=tf.intaa						)  # The dog
							# fmt: off
							# The dog is a man's best friend. It is a loyal companion, and it is a friend
							snake_case_		   =					[4_64, 32_90, 3_18, 2_57, 5_82, 3_38, 12_66, 15_45, 13, 6_32, 3_18, 2_57, 91_12, 1_51_85, 11, 2_90, 3_40, 3_18, 2_57, 15_45]
							# fmt: on
							snake_case_		   =					model.generate(__lowercase	,     do_sample=__lowercase						)
							self.assertListEqual(output_ids[0].numpy().tolist()	,     __lowercase						)






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"	,     revision="float16"						)
							snake_case_		   =					TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"	,     revision="float16"	,     from_pt=__lowercase						)

							snake_case_		   =					tokenizer("Today is a nice day and"	,     return_tensors="tf"						)
							# forces the generation to happen on CPU, to avoid GPU-related quirks
							with tf.device(":/CPU:0"						):
								snake_case_		   =					model.generate(**__lowercase	,     do_sample=__lowercase	,     seed=[42, 0]						)
							snake_case_		   =					tokenizer.decode(output_ids[0]	,     skip_special_tokens=__lowercase						)

							snake_case_		   =					"Today is a nice day and I’m going to go for a walk. I’"
							self.assertEqual(__lowercase	,     __lowercase						)






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"	,     revision="float16"	,     from_pt=__lowercase						)
							snake_case_		   =					AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"	,     revision="float16"						)

							snake_case_		   =					"left"

							# Define PAD Token = EOS Token = 50256
							snake_case_		   =					tokenizer.eos_token
							snake_case_		   =					model.config.eos_token_id

							# use different length sentences to test batching
							snake_case_		   =					[
							    "Hello, my dog is a little",
							    "Today, I",
							]
							snake_case_		   =					[
							    "Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia",
							    "Today, I’m going to be talking about a topic that’",
							]
							return model, tokenizer, sentences, expected_output_sentences






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_   ,				snake_case_   ,				snake_case_   ,				snake_case_		   =					self._get_beam_search_test_objects()

							snake_case_		   =					tokenizer(__lowercase	,     return_tensors="tf"	,     padding=__lowercase						)
							snake_case_		   =					model.generate(**__lowercase	,     do_sample=__lowercase	,     num_beams=2						)
							snake_case_		   =					tokenizer.batch_decode(__lowercase	,     skip_special_tokens=__lowercase						)
							self.assertListEqual(__lowercase	,     __lowercase						)






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_   ,				snake_case_   ,				snake_case_   ,				snake_case_		   =					self._get_beam_search_test_objects()

							snake_case_		   =					tokenizer(__lowercase	,     return_tensors="tf"	,     padding=__lowercase						)
							snake_case_		   =					tokenizer(sentences[0]	,     return_tensors="tf"						)
							snake_case_		   =					model.generate(**__lowercase	,     do_sample=__lowercase	,     num_beams=2						)
							snake_case_		   =					(
							    shape_list(inputs_non_padded["input_ids"]						)[-1]
							    - tf.reduce_sum(tf.cast(inputs["attention_mask"][-1]	,     tf.intaa						)						).numpy()
							)
							snake_case_		   =					tokenizer(sentences[1]	,     return_tensors="tf"						)
							snake_case_		   =					model.generate(
							    **__lowercase	,     do_sample=__lowercase	,     num_beams=2	,     max_length=model.config.max_length - num_paddings						)
							snake_case_		   =					tokenizer.decode(output_non_padded[0]	,     skip_special_tokens=__lowercase						)
							snake_case_		   =					tokenizer.decode(output_padded[0]	,     skip_special_tokens=__lowercase						)
							self.assertListEqual(__lowercase	,     [non_padded_sentence, padded_sentence]						)






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_   ,				snake_case_   ,				snake_case_   ,				snake_case_		   =					self._get_beam_search_test_objects()

							snake_case_		   =					tokenizer(__lowercase	,     return_tensors="tf"	,     padding=__lowercase						)
							snake_case_		   =					tf.function(model.generate	,     jit_compile=__lowercase						)
							snake_case_		   =					xla_generate(**__lowercase	,     do_sample=__lowercase	,     num_beams=2						)
							snake_case_		   =					tokenizer.batch_decode(__lowercase	,     skip_special_tokens=__lowercase						)
							self.assertListEqual(__lowercase	,     __lowercase						)






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.")
