

"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import RoFormerConfig, 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 import (
							    TFRoFormerForCausalLM,
							    TFRoFormerForMaskedLM,
							    TFRoFormerForMultipleChoice,
							    TFRoFormerForQuestionAnswering,
							    TFRoFormerForSequenceClassification,
							    TFRoFormerForTokenClassification,
							    TFRoFormerModel,
							)
							from transformers.models.roformer.modeling_tf_roformer import (
							    TFRoFormerSelfAttention,
							    TFRoFormerSinusoidalPositionalEmbedding,
							)







class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					def __init__(       self			:				Dict   ,lowercase_			:				Tuple   ,lowercase_			:				List[str]=1_3   ,lowercase_			:				int=7   ,lowercase_			:				Optional[Any]=True   ,lowercase_			:				Any=True   ,lowercase_			:				int=True   ,lowercase_			:				Any=True   ,lowercase_			:				str=9_9   ,lowercase_			:				Union[str, Any]=3_2   ,lowercase_			:				str=2   ,lowercase_			:				Union[str, Any]=4   ,lowercase_			:				Optional[int]=3_7   ,lowercase_			:				Any="gelu"   ,lowercase_			:				str=0.1   ,lowercase_			:				Dict=0.1   ,lowercase_			:				Optional[int]=5_1_2   ,lowercase_			:				Optional[Any]=1_6   ,lowercase_			:				Optional[Any]=2   ,lowercase_			:				int=0.02   ,lowercase_			:				Union[str, Any]=3   ,lowercase_			:				Optional[Any]=4   ,lowercase_			:				Dict=None   ,):
						lowerCAmelCase__	:						Tuple  				=	parent
						lowerCAmelCase__	:						Dict  				=	1_3
						lowerCAmelCase__	:						List[str]  				=	7
						lowerCAmelCase__	:						Union[str, Any]  				=	True
						lowerCAmelCase__	:						Any  				=	True
						lowerCAmelCase__	:						Optional[Any]  				=	True
						lowerCAmelCase__	:						Optional[int]  				=	True
						lowerCAmelCase__	:						List[Any]  				=	9_9
						lowerCAmelCase__	:						Any  				=	3_2
						lowerCAmelCase__	:						Optional[int]  				=	2
						lowerCAmelCase__	:						Optional[Any]  				=	4
						lowerCAmelCase__	:						Any  				=	3_7
						lowerCAmelCase__	:						Tuple  				=	'''gelu'''
						lowerCAmelCase__	:						Any  				=	0.1
						lowerCAmelCase__	:						Union[str, Any]  				=	0.1
						lowerCAmelCase__	:						List[Any]  				=	5_1_2
						lowerCAmelCase__	:						str  				=	1_6
						lowerCAmelCase__	:						Union[str, Any]  				=	2
						lowerCAmelCase__	:						List[Any]  				=	0.02
						lowerCAmelCase__	:						Optional[Any]  				=	3
						lowerCAmelCase__	:						Union[str, Any]  				=	4
						lowerCAmelCase__	:						Optional[int]  				=	None







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						Tuple  				=	ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size		)

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

						lowerCAmelCase__	:						List[Any]  				=	None
						if self.use_token_type_ids:
							lowerCAmelCase__	:						List[str]  				=	ids_tensor([self.batch_size, self.seq_length]   ,self.type_vocab_size		)

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

						lowerCAmelCase__	:						Tuple  				=	RoFormerConfig(
						    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   ,type_vocab_size=self.type_vocab_size   ,initializer_range=self.initializer_range   ,return_dict=lowercase_   ,)

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				List[str]   ,lowercase_			:				Tuple   ,lowercase_			:				Tuple   ,lowercase_			:				Optional[int]   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				Dict		):
						lowerCAmelCase__	:						Optional[int]  				=	TFRoFormerModel(config=lowercase_		)
						lowerCAmelCase__	:						Optional[Any]  				=	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

						lowerCAmelCase__	:						str  				=	[input_ids, input_mask]
						lowerCAmelCase__	:						str  				=	model(lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	model(lowercase_		)

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







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				Any   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int   ,lowercase_			:				Optional[int]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				Optional[int]		):
						lowerCAmelCase__	:						Dict  				=	True
						lowerCAmelCase__	:						Tuple  				=	TFRoFormerForCausalLM(config=lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	{
						    '''input_ids''': input_ids,
						    '''attention_mask''': input_mask,
						    '''token_type_ids''': token_type_ids,
						}
						lowerCAmelCase__	:						Dict  				=	model(lowercase_		)['''logits''']
						self.parent.assertListEqual(
						    list(prediction_scores.numpy().shape		)   ,[self.batch_size, self.seq_length, self.vocab_size]		)







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				Any   ,lowercase_			:				int   ,lowercase_			:				int   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				Optional[int]   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	TFRoFormerForMaskedLM(config=lowercase_		)
						lowerCAmelCase__	:						Dict  				=	{
						    '''input_ids''': input_ids,
						    '''attention_mask''': input_mask,
						    '''token_type_ids''': token_type_ids,
						}
						lowerCAmelCase__	:						Union[str, Any]  				=	model(lowercase_		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.vocab_size)		)







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				List[Any]   ,lowercase_			:				List[Any]   ,lowercase_			:				Any   ,lowercase_			:				str   ,lowercase_			:				int   ,lowercase_			:				List[Any]   ,lowercase_			:				List[str]		):
						lowerCAmelCase__	:						List[str]  				=	self.num_labels
						lowerCAmelCase__	:						List[str]  				=	TFRoFormerForSequenceClassification(config=lowercase_		)
						lowerCAmelCase__	:						int  				=	{
						    '''input_ids''': input_ids,
						    '''attention_mask''': input_mask,
						    '''token_type_ids''': token_type_ids,
						}

						lowerCAmelCase__	:						str  				=	model(lowercase_		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)		)







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				List[str]   ,lowercase_			:				Dict   ,lowercase_			:				Any   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				List[Any]   ,lowercase_			:				Optional[int]		):
						lowerCAmelCase__	:						List[Any]  				=	self.num_choices
						lowerCAmelCase__	:						Tuple  				=	TFRoFormerForMultipleChoice(config=lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	tf.tile(tf.expand_dims(lowercase_   ,1		)   ,(1, self.num_choices, 1)		)
						lowerCAmelCase__	:						Tuple  				=	tf.tile(tf.expand_dims(lowercase_   ,1		)   ,(1, self.num_choices, 1)		)
						lowerCAmelCase__	:						Tuple  				=	tf.tile(tf.expand_dims(lowercase_   ,1		)   ,(1, self.num_choices, 1)		)
						lowerCAmelCase__	:						Optional[Any]  				=	{
						    '''input_ids''': multiple_choice_inputs_ids,
						    '''attention_mask''': multiple_choice_input_mask,
						    '''token_type_ids''': multiple_choice_token_type_ids,
						}
						lowerCAmelCase__	:						Union[str, Any]  				=	model(lowercase_		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_choices)		)







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				Optional[Any]   ,lowercase_			:				List[str]   ,lowercase_			:				List[str]   ,lowercase_			:				List[str]   ,lowercase_			:				Optional[int]   ,lowercase_			:				Any   ,lowercase_			:				Dict		):
						lowerCAmelCase__	:						Any  				=	self.num_labels
						lowerCAmelCase__	:						List[Any]  				=	TFRoFormerForTokenClassification(config=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	{
						    '''input_ids''': input_ids,
						    '''attention_mask''': input_mask,
						    '''token_type_ids''': token_type_ids,
						}
						lowerCAmelCase__	:						Optional[int]  				=	model(lowercase_		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.num_labels)		)







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				List[str]   ,lowercase_			:				Any   ,lowercase_			:				Any   ,lowercase_			:				Dict   ,lowercase_			:				str   ,lowercase_			:				Any   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						List[Any]  				=	TFRoFormerForQuestionAnswering(config=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	{
						    '''input_ids''': input_ids,
						    '''attention_mask''': input_mask,
						    '''token_type_ids''': token_type_ids,
						}

						lowerCAmelCase__	:						str  				=	model(lowercase_		)
						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			:				Dict		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.prepare_config_and_inputs()
						(
						    (
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,
						)	:						Optional[int]  				=	config_and_inputs
						lowerCAmelCase__	:						List[Any]  				=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
						return config, inputs_dict







@require_tf
class 				SCREAMING_SNAKE_CASE					(		a_    ,							a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       (
					    (
					        TFRoFormerModel,
					        TFRoFormerForCausalLM,
					        TFRoFormerForMaskedLM,
					        TFRoFormerForQuestionAnswering,
					        TFRoFormerForSequenceClassification,
					        TFRoFormerForTokenClassification,
					        TFRoFormerForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					lowercase__				 =       (
					    {
					        "feature-extraction": TFRoFormerModel,
					        "fill-mask": TFRoFormerForMaskedLM,
					        "question-answering": TFRoFormerForQuestionAnswering,
					        "text-classification": TFRoFormerForSequenceClassification,
					        "text-generation": TFRoFormerForCausalLM,
					        "token-classification": TFRoFormerForTokenClassification,
					        "zero-shot": TFRoFormerForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					lowercase__				 =       False
					lowercase__				 =       False







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				Dict   ,lowercase_			:				int   ,lowercase_			:				Optional[int]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				str		):
						if pipeline_test_casse_name == "TextGenerationPipelineTests":
							return True

						return False







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Any  				=	TFRoFormerModelTester(self		)
						lowerCAmelCase__	:						Any  				=	ConfigTester(self   ,config_class=lowercase_   ,hidden_size=3_7		)







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						self.config_tester.run_common_tests()







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase_		)







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						str  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*lowercase_		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[int]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_lm_head(*lowercase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						Dict  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*lowercase_		)







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						List[Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*lowercase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						List[Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Dict  				=	TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''		)
						self.assertIsNotNone(lowercase_		)







@require_tf
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					@slow
					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Dict  				=	TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''		)
						lowerCAmelCase__	:						Dict  				=	tf.constant([[0, 1, 2, 3, 4, 5]]		)
						lowerCAmelCase__	:						Optional[int]  				=	model(lowercase_		)[0]

						# TODO Replace vocab size
						lowerCAmelCase__	:						int  				=	5_0_0_0_0

						lowerCAmelCase__	:						int  				=	[1, 6, vocab_size]
						self.assertEqual(output.shape   ,lowercase_		)

						print(output[:, :3, :3]		)

						# TODO Replace values below with what was printed above.
						lowerCAmelCase__	:						Optional[int]  				=	tf.constant(
						    [
						        [
						            [-0.1205_3341, -1.026_4901, 0.2922_1946],
						            [-1.513_3783, 0.19_7433, 0.1519_0607],
						            [-5.013_5403, -3.90_0256, -0.8403_8764],
						        ]
						    ]		)
						tf.debugging.assert_near(output[:, :3, :3]   ,lowercase_   ,atol=1E-4		)







@require_tf
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       1e-4







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	tf.constant([[4, 1_0]]		)
						lowerCAmelCase__	:						Optional[int]  				=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=6   ,embedding_dim=6		)

						lowerCAmelCase__	:						List[Any]  				=	emba(input_ids.shape		)
						lowerCAmelCase__	:						int  				=	tf.constant(
						    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]		)

						tf.debugging.assert_near(lowercase_   ,lowercase_   ,atol=self.tolerance		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						Tuple  				=	tf.constant(
						    [
						        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
						        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
						        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
						    ]		)
						lowerCAmelCase__	:						Tuple  				=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2   ,embedding_dim=5_1_2		)
						emba([2, 1_6, 5_1_2]		)
						lowerCAmelCase__	:						int  				=	emba.weight[:3, :5]

						tf.debugging.assert_near(lowercase_   ,lowercase_   ,atol=self.tolerance		)







@require_tf
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       1e-4







					def        __lowerCAmelCase (       self			:				str		):
						# 2,12,16,64
						lowerCAmelCase__	:						Optional[Any]  				=	tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4   ,dtype=tf.floataa		)   ,shape=(2, 1_2, 1_6, 6_4)		) / 1_0_0

						lowerCAmelCase__	:						Optional[Any]  				=	-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4   ,dtype=tf.floataa		)   ,shape=(2, 1_2, 1_6, 6_4)		) / 1_0_0

						lowerCAmelCase__	:						Optional[int]  				=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2   ,embedding_dim=6_4		)
						lowerCAmelCase__	:						str  				=	embed_positions([2, 1_6, 7_6_8]		)[None, None, :, :]

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	TFRoFormerSelfAttention.apply_rotary_position_embeddings(
						    lowercase_   ,lowercase_   ,lowercase_		)

						lowerCAmelCase__	:						str  				=	tf.constant(
						    [
						        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
						        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
						        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
						        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
						        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
						        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
						    ]		)
						lowerCAmelCase__	:						Union[str, Any]  				=	tf.constant(
						    [
						        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
						        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
						        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
						        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
						        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
						        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
						    ]		)

						tf.debugging.assert_near(query_layer[0, 0, :6, :8]   ,lowercase_   ,atol=self.tolerance		)
						tf.debugging.assert_near(key_layer[0, 0, :6, :8]   ,lowercase_   ,atol=self.tolerance		)








from __future__ import annotations

from collections.abc import Callable

__lowerCAmelCase		       =							list[list[float | int]]




def    snake_case_     (  snake_case			,							snake_case     )  ->				Matrix:
       lowercase__:   int  			=   len(snake_case     )
       lowercase__:   Matrix  			=   [[0 for _ in range(size + 1     )] for _ in range(snake_case     )]
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   float

       for row in range(snake_case     ):
              for col in range(snake_case     ):
                     lowercase__:   List[Any]  			=   matrix[row][col]

              lowercase__:   Optional[int]  			=   vector[row][0]

       lowercase__:   str  			=   0
       lowercase__:   Any  			=   0
       while row < size and col < size:
              # pivoting
              lowercase__:   List[Any]  			=   max((abs(augmented[rowa][col]     ), rowa) for rowa in range(snake_case			,							snake_case     )     )[
                  1
              ]
              if augmented[pivot_row][col] == 0:
                     col += 1
                     continue
              else:
                     lowercase__			,						lowercase__:   Optional[int]  			=   augmented[pivot_row], augmented[row]

              for rowa in range(row + 1			,							snake_case     ):
                     lowercase__:   Any  			=   augmented[rowa][col] / augmented[row][col]
                     lowercase__:   int  			=   0
                     for cola in range(col + 1			,							size + 1     ):
                            augmented[rowa][cola] -= augmented[row][cola] * ratio

              row += 1
              col += 1

       # back substitution
       for col in range(1			,							snake_case     ):
              for row in range(snake_case     ):
                     lowercase__:   Union[str, Any]  			=   augmented[row][col] / augmented[col][col]
                     for cola in range(snake_case			,							size + 1     ):
                            augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
       return [
           [round(augmented[row][size] / augmented[row][row]			,							10     )] for row in range(snake_case     )
       ]




def    snake_case_     (  snake_case     )  ->				Callable[[int], int]:
       lowercase__:   int  			=   len(snake_case     )
       lowercase__:   Matrix  			=   [[0 for _ in range(snake_case     )] for _ in range(snake_case     )]
       lowercase__:   Matrix  			=   [[0] for _ in range(snake_case     )]
       lowercase__:   Matrix
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int

       for x_val, y_val in enumerate(snake_case     ):
              for col in range(snake_case     ):
                     lowercase__:   List[str]  			=   (x_val + 1) ** (size - col - 1)
              lowercase__:   str  			=   y_val

       lowercase__:   Optional[int]  			=   solve(snake_case			,							snake_case     )

       def interpolated_func(snake_case     ) -> int:
              return sum(
                  round(coeffs[x_val][0]     ) * (var ** (size - x_val - 1))
                  for x_val in range(snake_case     )     )

       return interpolated_func




def    snake_case_     (  snake_case     )  ->				int:
       return (
           1
           - variable
           + variable**2
           - variable**3
           + variable**4
           - variable**5
           + variable**6
           - variable**7
           + variable**8
           - variable**9
           + variable**10
       )




def    snake_case_     (  snake_case = question_function			,							snake_case = 10     )  ->				int:
       lowercase__:   list[int]  			=   [func(snake_case     ) for x_val in range(1			,							order + 1     )]

       lowercase__:   list[Callable[[int], int]]  			=   [
           interpolate(data_points[:max_coeff]     ) for max_coeff in range(1			,							order + 1     )
       ]

       lowercase__:   int  			=   0
       lowercase__:   Callable[[int], int]
       lowercase__:   int

       for poly in polynomials:
              lowercase__:   List[str]  			=   1
              while func(snake_case     ) == poly(snake_case     ):
                     x_val += 1

              ret += poly(snake_case     )

       return ret


if __name__ == "__main__":
 print(F'''{solution() = }''')


