
def   lowerCamelCase__							(							__lowerCAmelCase    :    list[list]							):




			"""simple docstring"""


			lowerCAmelCase_         =      current_set.copy()
			for row_index, row in enumerate(__lowerCAmelCase							):
						lowerCAmelCase_         =      row[0]
						for column_index, column in enumerate(__lowerCAmelCase							):
									if magnitude == 0:
												lowerCAmelCase_         =      column
												continue
									lowerCAmelCase_         =      column / magnitude
    # Subtract to cancel term
			lowerCAmelCase_         =      current_set[0]
			lowerCAmelCase_         =      [first_row]
			lowerCAmelCase_         =      current_set[1::]
			for row in current_set:
						lowerCAmelCase_         =      []
						# If first term is 0, it is already in form we want, so we preserve it
						if row[0] == 0:
									final_set.append(__lowerCAmelCase							)
									continue
						for column_index in range(len(__lowerCAmelCase							)							):
									temp_row.append(first_row[column_index] - row[column_index]							)
						final_set.append(__lowerCAmelCase							)
			# Create next recursion iteration set
			if len(final_set[0]							) != 3:
						lowerCAmelCase_         =      final_set[0]
						lowerCAmelCase_         =      []
						lowerCAmelCase_         =      []
						for row in final_set[1::]:
									current_first_column.append(row[0]							)
									next_iteration.append(row[1::]							)
						lowerCAmelCase_         =      simplify(__lowerCAmelCase							)
						for i in range(len(__lowerCAmelCase							)							):
									resultant[i].insert(0       ,				current_first_column[i]							)
						resultant.insert(0       ,				__lowerCAmelCase							)
						lowerCAmelCase_         =      resultant
			return final_set






def   lowerCamelCase__							(							__lowerCAmelCase    :    list[list]							):




			"""simple docstring"""


			if len(__lowerCAmelCase							) == 0:
						raise IndexError("solve_simultaneous() requires n lists of length n+1"							)
			lowerCAmelCase_         =      len(__lowerCAmelCase							) + 1
			if any(len(__lowerCAmelCase							) != _length for item in equations							):
						raise IndexError("solve_simultaneous() requires n lists of length n+1"							)
			for row in equations:
						if any(not isinstance(__lowerCAmelCase       ,				(int, float)							) for column in row							):
									raise ValueError("solve_simultaneous() requires lists of integers"							)
			if len(__lowerCAmelCase							) == 1:
						return [equations[0][-1] / equations[0][0]]
			lowerCAmelCase_         =      equations.copy()
			if any(0 in row for row in data_set							):
						lowerCAmelCase_         =      data_set.copy()
						lowerCAmelCase_         =      []
						for row_index, row in enumerate(__lowerCAmelCase							):
									if 0 not in row:
												lowerCAmelCase_         =      data_set.pop(__lowerCAmelCase							)
												break
						if not full_row:
									raise ValueError("solve_simultaneous() requires at least 1 full equation"							)
						data_set.insert(0       ,				__lowerCAmelCase							)
			lowerCAmelCase_         =      data_set.copy()
			lowerCAmelCase_         =      simplify(__lowerCAmelCase							)
			lowerCAmelCase_         =      simplified[::-1]
			lowerCAmelCase_         =      []
			for row in simplified:
						lowerCAmelCase_         =      row[-1]
						if not solutions:
									if row[-2] == 0:
												solutions.append(0							)
												continue
									solutions.append(current_solution / row[-2]							)
									continue
						lowerCAmelCase_         =      row.copy()[: len(__lowerCAmelCase							) - 1 :]
						while temp_row[0] == 0:
									temp_row.pop(0							)
						if len(__lowerCAmelCase							) == 0:
									solutions.append(0							)
									continue
						lowerCAmelCase_         =      temp_row[1::]
						lowerCAmelCase_         =      temp_row[::-1]
						for column_index, column in enumerate(__lowerCAmelCase							):
									current_solution -= column * solutions[column_index]
						solutions.append(__lowerCAmelCase							)
			lowerCAmelCase_         =      []
			for item in solutions:
						final.append(float(round(__lowerCAmelCase       ,				5							)							)							)
			return final[::-1]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			_A  =	[
			    [2, 1, 1, 1, 1, 4],
			    [1, 2, 1, 1, 1, 5],
			    [1, 1, 2, 1, 1, 6],
			    [1, 1, 1, 2, 1, 7],
			    [1, 1, 1, 1, 2, 8],
			]
			print(solve_simultaneous(eq))
			print(solve_simultaneous([[4, 2]]))





from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
			    TFDebertaVaForMaskedLM,
			    TFDebertaVaForQuestionAnswering,
			    TFDebertaVaForSequenceClassification,
			    TFDebertaVaForTokenClassification,
			    TFDebertaVaModel,
			)





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=7							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=99							,							_UpperCamelCase=32							,							_UpperCamelCase=2							,							_UpperCamelCase=4							,							_UpperCamelCase=37							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=16							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=False							,							_UpperCamelCase=True							,							_UpperCamelCase="None"							,							_UpperCamelCase=3							,							_UpperCamelCase=4							,							_UpperCamelCase=None							,							)							-> Tuple:
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      seq_length
							lowerCAmelCase_         =      is_training
							lowerCAmelCase_         =      use_input_mask
							lowerCAmelCase_         =      use_token_type_ids
							lowerCAmelCase_         =      use_labels
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      type_sequence_label_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      num_labels
							lowerCAmelCase_         =      num_choices
							lowerCAmelCase_         =      relative_attention
							lowerCAmelCase_         =      position_biased_input
							lowerCAmelCase_         =      pos_att_type
							lowerCAmelCase_         =      scope




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size	)

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

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

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

							lowerCAmelCase_         =      DebertaVaConfig(
							    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							,							relative_attention=self.relative_attention							,							position_biased_input=self.position_biased_input							,							initializer_range=self.initializer_range							,							return_dict=_UpperCamelCase							,							)

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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaModel(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

							lowerCAmelCase_         =      [input_ids, input_mask]
							lowerCAmelCase_         =      model(_UpperCamelCase	)

							lowerCAmelCase_         =      model(_UpperCamelCase	)

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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Any:
							lowerCAmelCase_         =      TFDebertaVaForMaskedLM(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      TFDebertaVaForSequenceClassification(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      TFDebertaVaForTokenClassification(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.num_labels)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaForQuestionAnswering(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							lowerCAmelCase_         =      model(_UpperCamelCase	)
							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 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    
							)         =      config_and_inputs
							lowerCAmelCase_         =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict





@require_tf
class       _lowerCAmelCase   (      __a   , __a   , unittest.TestCase			):
				_lowercase     						=(
				    (
				        TFDebertaVaModel,
				        TFDebertaVaForMaskedLM,
				        TFDebertaVaForQuestionAnswering,
				        TFDebertaVaForSequenceClassification,
				        TFDebertaVaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowercase     						=(
				    {
				        '''feature-extraction''': TFDebertaVaModel,
				        '''fill-mask''': TFDebertaVaForMaskedLM,
				        '''question-answering''': TFDebertaVaForQuestionAnswering,
				        '''text-classification''': TFDebertaVaForSequenceClassification,
				        '''token-classification''': TFDebertaVaForTokenClassification,
				        '''zero-shot''': TFDebertaVaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      TFDebertaVaModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase							,							hidden_size=37	)




				def 	__a		(   self	)							-> str:
							self.config_tester.run_common_tests()




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase	)




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"	)
							self.assertIsNotNone(_UpperCamelCase	)





@require_tf
class       _lowerCAmelCase   (      unittest.TestCase			):




				@unittest.skip(reason="Model not available yet"	)
				def 	__a		(   self	)							-> List[str]:
							pass




				@slow
				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"	)
							lowerCAmelCase_         =      tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	)
							lowerCAmelCase_         =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
							lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase	)[0]

							lowerCAmelCase_         =      tf.constant(
							    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]	)
							tf.debugging.assert_near(output[:, 1:4, 1:4]							,							_UpperCamelCase							,							atol=1e-4	)



